Table of contents

Organized by Beijing Jiaotong University, the 2020 International Symposium on Automation, Information and Computing (ISAIC 2020) was held successfully online from December 2nd-4th, 2020. ISAIC 2020 was primarily scheduled to be held in Beijing, China from 2nd to 4th December. However, due to the COVID-19, it had to be changed to virtual model. The technical program comprised one plenary session with 8 plenary speeches (40 minutes for each including 3-5 minutes of Q&A), 10 parallel oral sessions including 27 invited speeches (25 minutes for each including 3-5 minutes of Q&A) and 88 online live presentations (15 minutes for each including 3-5 minutes of Q&A), 44 pre-recorded video presentations (15-20 minutes) and 22 e-poster presentations.

The ISAIC conference series aims to provide an academic platform for researchers and scholars to present and discuss their latest findings about automation, information and computing. ISAIC 2020 gathered over 220 participants from 39 different countries and areas. The main subjects of the conference were artificial intelligence, electronic and electric systems, information communication technology, information security, mathematics and system engineering.

This volume records the proceedings of ISAIC 2020 and contains 186 manuscripts that in accordance with the Journal's Peer Review Policy were strictly selected based on originality, significance, relevance and contribution to the area after being peer-reviewed.

List of General Chairs, Co-Chairs, Technical Program Committee and Guest Editor are available in this pdf.

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Single-blind

• Conference submission management system: http://onlinesystem.subisaic.com/

• Number of submissions received: 438

• Number of submissions sent for review: 422

• Number of submissions accepted: 186

• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 42%

• Average number of reviews per paper: 2

• Total number of reviewers involved: Around 200

• Any additional info on review process: Ithenticate was used to check the plagiarism issue during the review process

• Contact person for queries:

Shiping Wen

University of Technology Sydney, Australia

shiping.wen@uts.edu.au

Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and uneven roads. Most news reports of road accidents are also caused by potholes that are almost 10-30 cm deep, coupled with heavy rainfall that reduces visibility among drivers, significant damage to the suspension system to the vehicle or unnecessary traffic congestion. In this paper, deep learning detection with YOLOv3 algorithm is proposed apart from researches ranging from accelerometer detection, image processing or machine learning based detection as it is easier to develop and provide more accurate results. After pothole has been detected in real-time webcam, the location will be logged and displayed using Google Maps API for visualization. a total of 330 sets of data were sampled for the implementation of the pothole detection training model. As the results, the model provided 65.05 mAP and 0.9 % precision rate and 0.41 recall rate. The limitation of YOLOv3 algorithm detection can be improve further using GPU with higher specification performances and can sample 1000 to 10,000 datasets. The proposed algorithm provides acceptably high precision and efficient pothole monitoring solution under different scenarios for the users and may benefit the public and the government to monitor pothole in real-time.

This paper proposes method that can complete motion planning in one step using convolutional Long Short-Term Memory (LSTM) network. Service mobile robot movement from the starting position to the target position includes three main tasks: mapping, positioning, motion planning. The convolutional LSTM network mainly is used to complete motion planning. The input of the network is a GRB picture with obstacles, target position, starting position. The outputs of the network are linear velocity and angular velocity of service mobile robot. The convolution layer of the network is to mark obstacles, target position and starting position. LSTM layer describes the time characteristics of movement and full connected layer is used to smoothly fit linear velocity and angular velocity of service mobile robot. The convolutional LSTM network can complete tasks of path finding and control, that is, mapping pictures with obstacles, target position, starting position to linear velocity and angular velocity of service mobile robot. Compared with traditional separate solutions for motion tasks, this method has obvious advantages such as good fault tolerance and complete motion tasks planning in real time. In experiment, a mobile robot "Turtlebot" based on ROS systems was used to verify the effectiveness and convenience of the method for motion planning.

Although comparison is one of the most useful data validity evaluation method, it has a few potential drawbacks. One of them is that results from the training or the testing a machine learning model shall be obtained. Another one is that uncertainty due to the machine learning model itself may cause a difficulty in evaluating the data validity. In this paper, a new data validity evaluation method was proposed so that these drawbacks can be made up by the proposed method. The proposed method enables a data validity evaluation to be performed in data collection phase or data pre-processing phase. Uniform Manifold Approximation and Projection (UMAP) offers the methodological background to the proposed method.

Intelligent information processing is topical in modern technology design and development. The fundamental idea was developed by Turing as he made the first conceptual models of information-processing computers. Though it has practically never been noticed, Turing's work was a model of how to mimic human intelligent information processes and generate technologies, which can carry out intelligent tasks. The design method can be called cognitive mimetics as it imitates human information processes to design technologies and their applications. One can use cognitive mimetics even in solving techno-ethical problems. This is why we think that cognitive mimetics are vital as a method to generate intelligent information processes.

Research into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metrics.

Decision support systems (DSS) in law enforcement have a long history. Starting from the late 50s, they have been developed through several architectural approaches. Still, having a proven capability of DSSes to improve legal practice, the real-world application is limited due to multiple issues, including lack of trust, interpretability, validity, scalability, etc. The paper develops a service-based decision support platform for machine learning applications for eGovernance and internal policy modelling and presents a case study of the application of the platform for the case of migration law enforcement. We have developed a decision support platform a number of micro services that connect with each other asynchronously via the REST protocol. The artificial intelligence core of the platform was built upon a knowledge base, which includes machine learning models and methods. In this work we have developed a method of structuring, analysis of legal data models based on machine learning. In the course of computational experiment, the efficiency of the developed method was proved and the interpretation of the obtained results was performed to provide recommendations for the enhancement of administrative regulation.

The complexity of the decision-making process for experts in various topics is in the problem of initial processing of unstructured information. This problem of Big data requires the latest solutions that provide a structural reflection and integrative use of information descriptions under consideration by experts. It takes the process of information and analytical evaluation and decision-making beyond one subject area and raises questions about the ontological consideration of the problem itself. The ontological principles of such consideration are based on multiple hyperproperties of information for the realization of the categories of integration, systematicity and continuity, as well as implementation of structures and their functionality reflection and transformation. For this, there is the ontology of the problem of rational choice based on means of a ranking of alternatives on a set of indicators. It provides the creation of a system designed to solve the ranking problem based on the ontological model of the subject area. Hyperproperties of ontological representation of large amounts of information ensure the implementation of meta-tasks of its analysis, its structuring, synthesis, and support the process of rational choice. Such an approach creates the conditions for optimal processing of a Big Data.

Thesaurus, taxonomies, and ontologies are created as a way to represent knowledge. In recent years, the efforts to create them are focused on automatic knowledge extraction. For this purpose, research in the extraction of concepts and semantic relations is needed. Concepts or entities extracted from unstructured data has a became a challenge for the research communities. Texts, specifically, scientific papers contain knowledge where experts express their contributions to the science, including: techniques, methods and claims. In order to describe them, experts use synonyms, which are relevant to consider whereas it is desirable to extract such type of contribution form scientific text. This paper describes a method based on patterns for semantic relations extraction from scientific texts. The semantic relations correspond to synonymy. We describe pattern acquisition and relations extraction as phases to data evaluation. The evaluation shows promising results with 75% precision, 58% recall and 65% F-measure.

With the recent outbreak of the novel Coronavirus (COVID-19), the importance of early and accurate diagnosis arises, as it directly affects mortality rates. Computed Tomography (CT) scans of the patients' lungs is one of the diagnosis methods utilized in some countries, such as China. Manual inspection of CT scans can be a lengthy process, and may lead to inaccurate diagnosis. In this paper, a Deep Learning strategy based on VGG-16 is utilized with Transfer Learning for the purpose of binary classification of CT scans; Covid and NonCovid. Additionally, it is hypothesized in this study that Single Image Super Resolution (SISR) can boost the accuracy of the networks' performance. This hypothesis is tested by following the training strategy with the original dataset as well as the same dataset scaled by a factor of ×2. Experimental results show that SISR has a positive effect on the overall training performance.

The reliability for the surfaces of friction directly depends on the shape and size of the roughness (and material of course) which there are on the surfaces of the mobile pair of friction (body and counter body). The movement even for the one part leads to detach small parts from the surface of friction. This process has the name wear/wear and tear or deterioration. Usually, this action is escorted with the physical phenomenon as the heating. In this case both the firmness and the wear resistance begin to fall/diminish essentially. In this article one important factor will be described in detail. It connects with the analysis how we can make all lugs in a roughness more stable during the real operation. Several ways will be demonstrated and described here. Moreover, applied calculations and figures confirm the theoretical positions as well. At last, some negative estimates during the processes of calculations and dimensions at the present time are given and the methods how to avoid them in practice using new achievements and recommendations.

With the rapid development of Deep Learning (DL) and Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) has become one of the leading trends in automatic trading. However, research of RL in portfolio investment has difficulties in distributing investment funds, controlling profit and loss, and exploring the unseen environment. This paper introduces an intelligent portfolio investment system based on the integration of DRL, Quantum Finance Theory (QFT). Our proposed system consists of two agents: 1) A trading agent based on Deep Deterministic Policy Gradient (DDPG) algorithm to generate continuous actions for investment weighting; and 2) A risk-control agent based on Policy Gradient (PG) algorithm produces discrete actions according to each day's Quantum Price Levels (QPLs). One significant merit of integrating two intelligent agents is that they can cooperate to make more reasonable and stable fund distribution adjustments in the portfolio investment. The experimental results reflect the flexibility and robustness of our system, as it achieves considerable profits in back-tests consisting of various combinations of FOREX products.

(Source code is available at: https://github.com/772435284/QF-portfolio-investment-system)

Modern neural network methods make it possible to obtain qualitative results that open the colossal potential for integrating them into industrial applications. Therefore, there is a need for systems like the OpenVINO Deep Learning Workbench to analyze and optimize neural networks' performance on target devices. As the primary tool for inference, the OpenVINO open environment is used, which provides a wide variety of options to speed up network execution on the target hardware.

In this paper, we implement sparse matrix-vector multiplication (SpMV) kernels in the ELLPACK format on graphics processing units using arithmetic libraries supporting multiple precision on CUDA. We evaluate the performance of the developed kernels and also provide an optimized SpMV implementation in which multiple precision floating-point operations are split into several parts, each of which is executed as a separate kernel. Experimental evaluation with various matrices from real-world applications and at various levels of numeric precision shows that, in many cases, the optimized multiple precision SpMV performs better than the other implementations considered.

The work aims to develop software for conducting research on the formation of the structure and training a multilayer neural network for analyzing and blood glucose level prediction. The paper analyzes the task, considers the tools, methods and algorithms for solving the problem. A model of an artificial neural network was investigated, the structure was developed and training was carried out. Summed up based on the results and assessed the effectiveness of such a system.

There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoML-augmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the "Census Income" dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.

The current level of development of technical means and technologies creates favorable conditions for obtaining better information about the drilling process. To do this, it is necessary to analyze the information received, which in turn requires the use of methods that allow making decisions under uncertainty inherent in the drilling process. The operational information obtained in this case is very important and fundamental, which is of particular importance when drilling wells in areas for which there is no information or it is available in insufficient volume. The paper is devoted to increasing the efficiency of studying geological sections and improving the quality of information obtained in the process of drilling wells using probabilistic-statistical methods and fuzzy logic, which in turn contributes to improving the quality of decisions made. In two figures, a comparative analysis of changes in the properties of rocks and the rate of penetration was demonstrated, homogeneous intervals were identified according to the properties of rocks and drillability in general, which makes it possible to trace the intervals of possible complications.

A novel deep learning (DL) method is proposed for binaural sound source localization with low SNR. Firstly, the binaural sound signals are decomposed into several channels by using Gammatone filter. Secondly, the 4 feature parameters of Head-related Transfer Function, interaural time difference (ITD), interaural coherence (IC), interaural level difference (ILD), and interaural phase difference (IPD) are extracted. Thirdly, ITD and IC go through a Deep Belief Network (DBN) to determine the quadrant of the sound source and reduce the positioning range. Then, ITD, IC, ILD, and IPD go through a Deep Neural Network (DNN) to obtain the azimuthal angle within 90 degrees. Experimental results show that the proposed algorithm can solve the front-back confusion, and obtain a superior performance with lower complexity and higher precision under low SNR conditions.

The spatial position of the seat base is complicated. The contour structure of arc groove is complex and its contour feature extraction is difficult. Aiming at the above problems, this paper presents an improved corner detection algorithm based on Curvature Scale Space (CSS), which uses adaptive local curvature and dynamic angle threshold to accurately extract corner points of arc groove contour. The correct corner positioning error of the algorithm proposed in this paper is only 0.99 pixel. The experimental results show that this method can effectively implement contour feature extraction.

Traditional method of insulator defect identification is manually operated, which has low efficiency and high cost. Therefore, an automatic method of insulator defect identification is proposed in this paper. Firstly, image segmentation was operated by classification method of Random Forest (RF) to realize the object recognition of the insulator. Then, the method of Convolutional Neural Network (CNN) was adopted to classify the normal and defect states of insulators, and finally, the location of self-explosion defect identification was realized by Faster Region-Convolutional Neural Network (Faster R-CNN). A large number of images of insulators taken by Unmanned Aerial Vehicle (UAV) were used as experimental data to verify the method. The results show that the method in this paper could efficiently identify the defects of insulators, and the recognition rate reached 89.0%. The results can provide some references for the research of insulator defect identification of transmission lines.

Recent advances in deep learning and convolution neural network have greatly improved the reconstruction performance of SISR compared with the traditional methods. However, complicated models and huge amount of parameters limit the application of those methods in real-world scenes. In our paper, we propose an efficient channel attention feature fusion method on the lightweight super-resolution network (ELSRN) for SISR. We reduce our network parameters through several modules, including binary cascading feature fusion. Besides, we propose to build efficient inverted residual block (EIRB) and stack several EIRBs to capture effective feature information of different scales. Last, we fuse multi-scale features in pairs step by step and finally refine final feature information with different scale features. Several experiments have proved that our EIRBs module and binary cascading method are effective and our network can achieve a great trade-off between reconstruction performance and model size.

The general single image super-resolution methods mainly extract features from the high-resolution (HR) space by the pre-upscaling step at the beginning of the network or from the low-resolution (LR) space before the post-upscaling step at the end of the network. The former way requires high computation as well as misleading the network by wrong artificial priors. The latter way cannot learn mapping well by only conducting simple operations in HR space. In this paper, we aim to utilize the features from LR and HR space more efficiently and propose the novel network, which applies a frequency-slicing mechanism to divide features into LR and HR space, a direction-aware fusion residual group to extract distinctive features in LR space and an attention fusion module to recalibrate features in HR space. The experimental results demonstrate that our model is superior to the state-of-the-art methods upon quantitative metrics and visual quality.

A coupled method combining the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) was developed to optimize a 2D design of aero-engine inlet anti-icing structure, which has a cover on the film heating ejection slot. The optimal goal is to maximize the heating effectiveness which was used to assess the performance of hot-air film. The film-heating ejection angle and the cover opening angle were selected as the design variables to be optimized. The training and testing samples employed in BPNN were obtained by numerical simulation, after which the objective function of GA was predicted. With a given flow rate of bled air, the optimal values of the two design variables were achieved as 22.6° and 15.1°, respectively. Compared to the previous optimal result of other researchers, the heating performance was improved by 16.7% with rapid progress. The result of this study illustrates that this hybrid optimal method can meet the accuracy requirements with high time-efficiency for optimization problems in aeronautics engineering.

In this paper, a thunder signal detection method is proposed based on the deep learning framework. The recorded thunder signal is segment-wise acquired, stored and pre-processed. In each frame, we use Mel Frequency Cepstrum Coefficient (MFCC) to extract the features of the thunder signal, which is consistent with the frequency characteristics of human perception. We then use the MFCC features derived in each frame to form a 3-channel tensor data, which is used as the further input to the designed convolutional neural network (CNN). The goal of CNN is to classify the existence of thunder for a single data frame. To improve the robustness of CNN, we included other confusing signals that are similar to thunder signals in the training and testing datasets. On the testing dataset, our proposed method outperforms the state-of-art methods in terms of accuracy, sensitivity, and specificity. Our proposed deep-learning-based thunder detection method not only increases the real-time performance of the lighting location system with thunder signals but also further improves the accuracy of other sound alarm systems.

Massive, multi-dimensional and imbalanced network traffic data has brought new challenges to traditional intrusion detection systems (IDSs). The detection performance of traditional algorithms is closely related to feature extractions, which are not effective in the massive and imbalanced data environments. In this paper, we propose an intrusion detection model based on synthetic minority oversampling technology (SMOTE) and convolutional neural network (CNN) ensemble. It converts original traffic vectors into images, designs a CNN structure, and combines SMOTE and CNN ensemble to solve the problem of imbalanced datasets. Using the standard KDD CUP 99 dataset to evaluate the performance of the proposed model and analysing the contribution of features to model decision-making show that the model's F1 score are better than traditional algorithms in the classes with few samples and the model improves the efficiency of network intrusion detection.

This paper studies an accurate counting model for dealing with highly crowded people, multi-column hybrid convolutional neural network model. The model is mainly composed of three parts. The first part uses the first ten layers of VGG-16 convolutional network for image feature extraction. The middle layer is a dilated convolution with three rows of "jaggy" dilation rates, and each row uses the Resnet-block connection method, which is used primarily to perceive human head features of different sizes. Compared with a variety of image up-sampling ways, in the third part of the model, this paper tries to use a combination of bilinear interpolation and convolution to up-sample image features, and research shows that this method effectively reduces the model error. In this experiment, the average absolute error (MAE), mean square error (MSE) and average relative error (MRE) are used as evaluation indicators, and experiments on the ShanghaiTech dataset proves that the network works well.

In this paper, we propose a novel dual branch attention network (DBA-Net), which can efficiently restore multiple degradation images. Most methods of image restoration focus on a single degradation factor, such as blur, noise, raindrop, etc. However, these methods frequently fail to real degraded images, because the real images usually contain different types of degradation. Our network backbone consists of two branches, the residual branch and the information distillation branch, which have different receptive fields. We design a gate module to choose useful feature maps from the two branches. Then, an improved multi-channel attention selection module is proposed to allow the network to learn more features of real results. On DIV2K datasets, which contains different degradation types (noise, blur, and compression loss) and diverse degradation levels (mild, moderate and severe), our results on PSNR and SSIM outperform other advanced algorithms. In visual performance, the texture of objects in DBA-Net images is more similar to the original image.

The roller is the core component of the flexible material roll-to-roll equipment, and its performance will affect the processing quality, so it is necessary to predict the health of the roller. In order to improve the prediction accuracy and effectively extract the time sequence information hidden in the signal, a LSTM-SVM-based processing roll performance degradation prediction model is proposed. By collecting the bearing vibration sensor data, extracting the characteristics of the vibration signal after normalization, using the extracted features as the input of the prediction model, inputting a part of the samples as the training set into the LSTM-SVM prediction model, and inputting the model in batches for network training, and Adjustment parameters. After the model is trained, use the test set for testing. Compared with support vector machines, the SLTM-SVM model is more effective in predicting the performance degradation of roll-to-roll equipment.

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

Speech animation has huge application potential in instant messaging and entertainment media fields such as videophones, virtual meetings, audio and video chats. The traditional voice-driven speech animation has the problem of a single adaptation language, and the performance-driven speech animation has the problem of high cost of capture equipment and difficult mass production. Based on the above existing problems, we propose a new method of speech animation generation, that is, given a static portrait of a person and a face-driven video, finally generate a face animation video of the character in the given portrait. The conversion system consists of two parts: face conversion and voice conversion. We noticed that the final generated face animation video has problems such as low definition, not smooth playback, and metallic sound. On this basis, this article proposes to increase the animation enhancement experiment and replace the encoder measures for improvement. Through comparative experiments, the above measures are proved to be effective.

Person re-identification (Re-ID) aims at matching images of the same pedestrian under different cameras. State-of-the-art unsupervised domain adaptation (UDA) methods for person Re-ID transferred the learned knowledge from the source domain and further finetuned with pseudo-label target domain via clustering. To reduce the influence of noisy pseudo labels, the Mutual Mean-Teaching (MMT) framework was proposed by conducting pseudo label refinery to better model inter-sample relations in the target domain. Currently, MMT requires the use of the two same deep models for cooperative correction of noisy labels. In this paper, we extend the MMT framework to use two heterogeneous models for cooperative error correction. Experiments show that the proposed heterogeneous MMT framework can work well with two different deep models and its performance, however, is largely limited by the weaker model.

Automatic identification of modulated signals is an important technology of power line communication. Aiming at the problems of low signal recognition rate and characteristic parameter extraction in power line communication channel, this paper uses amplitude variance value of wavelet transform and higher order cumulant as the identification parameter, and designs a signal recognizer based on improved support vector machine. Under the condition of power line channel environment, the recognizer of this paper is less than the existing recognition method in the computational complexity, and has good robustness to power line noise. At the same time, it avoids the shortcomings of traditional neural network such as under-learning and over-learning. The simulation results show that when the SNR is 5 dB, power line communication signals through the recognizer, which the correct identification rate can reach 91%.

Event subject extraction was to extract subjects of specific event types. For the traditional BiLSTM network, the threshold is complicated, the required parameters are many, and the time cost is high. This paper is oriented to the financial field and proposes a method of introducing a multi-head attention mechanism based on the BIGRU network to extract event subjects. First, the text is vectorized, and then the word vector obtained is input into the BIGRU network to learn the context features, and introduce a multi-head attention mechanism to extract the depth feature values of the text. Finally, a comparative experiment is conducted on the data set. The method in this paper achieves an Accuracy value of 80.47% and an F1 value of 89.18%. The result is better than the control group, indicating that the model proposed in this paper can effectively improve the accuracy of extracting Chinese event subjects.

Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.

A large amount of data is generated during train operation. By using PHM technology, we can analyse the health status of train systems and equipment which guide train operation and maintenance scientifically and effectively. Based on the feedforward neural network, the paper introduces some research of train on health status, such as remaining life prediction. To better verify the method, an online health analysis equipment suitable for train CAN bus is designed. The method and equipment are tested based on the data of a real train braking system, and the results show that the remaining predicted mileage of the brake system decreased by 1.1%, and the remaining predicted maintenance time decreased by 2.3% because of the impact of fault injection technology on brake cylinder performance. The result proved the effectiveness of the method and equipment for the online health status analysis of trains.

Using text question and answer technology to improve the efficiency of judicial personnel in the process of case handling can greatly reduce labour costs. This paper proposes a hybrid neural network model that combines pre-training model BERT and Bi-GRU. The model first uses the pre-training model to learn powerful semantic capabilities, then combines Bi-GRU to learn the semantic information between the text and the question, finally gets the answer corresponding to the legal text. The experimental results on the CJRC data set show that compared with the basic baseline model, the algorithm in this paper can effectively improve the accuracy and F1 value.

To quickly and accurately identify the lawn area and boundary positions of different scenes, environments, and seasons, we propose a new semantic segmentation model PULNet and lawn boundary detection methods. Firstly, the ResNet50 network is improved to expand its effective receptive field, a Pooling pyramid (P) and an Upsampling dimensionality reduction structure (U) is constructed based on the Dilated_ResNet50 network. Secondly, a fast and accurate PULNet semantic segmentation network is proposed integrating the image Local detail information structure (L). Finally, an Eight-neighbor coding method is designed to accurately locate the border of the lawn. Experiments on the ADE20K dataset obtained the mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) 32.86% and 75.65% respectively. The average speed is 82.7 frames per second on a platform with GTX 1080Ti GPU. Compared with the Fully Convolutional Network (FCN) the mIoU and mPA are increased by 3.47% and 4.33% respectively, and the speed is 11 times higher. The proposed method can be used for fast and accurate lawn semantic segmentation and boundary detection.

How to arrange sales staff to visit offline stores reasonably is a critical task in the Fast Moving Consumer Goods (FMCG) industry. Based on the K-means and Dijkstra algorithms (K-D fusion algorithm), this paper proposed an algorithm to automatically allocate offline stores for sales staff and optimize the visiting path, thereby improving management efficiency. A new initial cluster center selection approach was proposed for the K-means algorithm to select its initial clustering center with the consideration of outlier points. The sales staff's visiting path to the store was planned by the Dijkstra algorithm. The performance of our K-D fusion algorithm was evaluated in terms of grouping rationality and path planning optimization. Experimental results show that our algorithm can comprehensively consider several factors such as the number of stores, store types, and geographical locations, and distribute the workload more evenly to all the sales staff. In addition, it can also optimize the visiting path for sales staff, which can effectively improve the efficiency of sales staff to visit offline stores.

This article attempts to delve a kind of quantities—tensor for a stochastic dynamic domain, making its principles of computations such as gradient, divergence and interaction. The notation "stochastic dynamic field (SDF)", proposed by the author, refers to such a dynamic field that it appears or annihilates, moves, acts or interacts at random. The study aims to consider not only randomness of vector fields in a continuous dynamic system, but also discontinuity with time variable t, i.e. more randomness for t, in a discontinuous dynamic system. To do so, representation forms of tensors areproperly transformed, which apply multiple-points tensor, and introduce the quantity of compound tensor like an expression of quantum state of combination of uncertainties. The work bears out some new results: (1) discontinuous mutation, even emerge or disappear, of a point in a tensor field, analyzed using a tensor with matrix structure, would not defer tensor computations of that field, while wouldn't be computed commonly using accustomed representation of tensor with only a single-point structure. (2) Random quantity relations between phrases, no matter smooth nearby, non-connected or not, all can be computed. (3) Some classical physical laws analyzing field properties of space-relevant factors such as Euler equation and Gauss theorem are available in new forms with a random nature.

In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant's demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.

A novel diagnosis method based on optimized subtraction clustering ANFIS (adaptive neural fuzzy inference system) algorithm is proposed in order to improve the accuracy and reliability of diesel engine fault diagnosis. The improved analytic hierarchy process (AHP) and subtractive clustering algorithm are combined to form a new ANFIS network suitable for multi information fusion diagnosis. The initial clustering centers of subtractive clustering algorithm and reasoning rules of ANFIS are automatically optimized by AHP algorithm without relying on expert experience. The effectiveness of the novel algorithm is investigated on the example of multi information fusion diagnosis of diesel engine, and the results indicate that the proposed method can eliminate the disadvantages of more inference rules, slow convergence speed and low diagnostic accuracy of the conventional ANFIS algorithm under multiple input parameters, which means this new method can effectively improve the accuracy of diesel engine fault diagnosis with the advantages of more fusion parameters and less calculation.

Some operators and laws of the interval-valued fuzzy soft sets are first introduced in this paper. Then an improved decision-making approach is proposed by employing the comparable table of interval-valued fuzzy soft set. At last, one numerical example is given to demonstrate the effectiveness of the presented decision approach.

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.

Because of environmental concerns, increasing attention has been given to developing energy-efficient technologies. As an essential public transportation method, metro transit is facing increasing pressure. Disturbances may cause the offline-optimized timetable and speed trajectory to be invalid. The present paper proposes a multi-agent system (MAS) train control method, in which each train is controlled by an agent. Each train agent has a simulation platform and an optimization platform. The simulation platform collects information such as track slop, speed limitation, speed trajectories of neighboring trains from a few neighboring agents. The simulation platform sends a signal with the collected information to the optimization platform when it detects a disturbance. Afterward, the optimization platform performs energy-aimed optimization to the timetable and corresponding speed trajectory based on a combination of a trained Neuro Network and a Mixed Integer Linear Programming (MILP) model. The test result shows an encouraging balance in optimization time and accuracy. The case study result proves that the proposed approach could provide a more energy-efficient control strategy when disturbances occur.

The traditional motor bridge circuit always has short circuit problems, and the smart car needs to be controlled by wire or remote control equipment. In order to study the application of automatic control technology in smart cars. Using the smart car model and simulation to study the smart car control system, with the mobile phone as the remote wireless control terminal, the smart car uses bluetooth communication and MCU as the intermediate bridge, the smart car achieves automatic direction control, voice control, gravity induction control, automatic tracking, automatic anti-collision and other functions. The motor circuit system is optimized by Simulation and experiment. It replaces the previous wired control and remote control equipment control, and effectively reduces the cost of equipment.

Unmanned surface vehicle (USV) has progressed quickly in recent decades, with widespread research and practical applications in academic and industry circles. Collision avoidance is a fundamental capability of USVs. It is extremely challenging to develop an ideal and sophisticated collision avoidance algorithm for USV in complex environments and practical offshore situations. However, a supervised learning method provides the USV a way of learning the process of avoidance and imitating a human pilot maneuvering to navigate in the real environment. This study analyzes the relative relationships and features of the own and target ships in the avoidance process firstly. And then a Gated Recurrent Unit (GRU) recurrent neural network model is constructed. Maneuvering commands during avoidance by human pilots are utilized as tags for training. Finally, the validity of method is proven by performing navigation experiments. In particularly, we also compare the effectiveness of GRU with Long Short-Term Memory (LSTM) network which is also a kind of recurrent neural network. The experimental results indicate that the proposed GRU model is better than the LSTM model, and the USV can autonomously navigate during the collision avoidance process by using the well-trained GRU model, reaching a level similar to that of human pilots.

Gene expression changes constantly with the occurrence and progression of diseases. The vast available gene expression data makes it possible for clinical researchers to understand the link between genotypes and phenotypes. However, it is still not an easy task because the information contained in the gene expression matrix is sparse. Gene set enrichment analysis is a powerful tool to meet the challenge of identifying complicated differential information underlying pathways. In this paper, we propose a method, called GSEMT, for gene set enrichment analysis by testing the correlation between a sample similarity matrix and a phenotype dissimilarity matrix. We implement experiments on knowledge-based gene sets and gene expression datasets for hepatocellular carcinoma. We justify the effectiveness and advantage of GSEMT by carrying out comparison studies. GSEMT outperforms GSEA and GSNCA in the classification performance on an experiment dataset and an independent validation dataset. The results show GSEMT is a useful alternative for gene set enrichment analysis.

In recent years, more and more malicious applications have appeared on mobile application platforms, and they are often disguised as social, communication, and game applications. If we classify applications by category when detecting malware, which can improve the accuracy of malware detection. Classification of applications' categories requires a large number of high-quality samples, but labels of applications' categories are vary widely in different app stores, and samples of the same function type cannot be obtained quickly and efficiently. This thesis proposes a method for constructing a multi-classification model by using the text content of application description information, and guiding the classification of application by the category of application description. This method collects the description of an application in different app stores, predicts the category of the description through the classification model, and obtains the application category by voting. The model is based on CNN and RNN, and its F1-score is about 3% higher than the text classification model such as textCNN, LSTM. Its training prediction time and memory consumption are only 6% higher than that of textCNN and LSTM models. We named it CRNN, this thesis constructs a data set that can be used for application classification. The data set is classified using application description to obtain each application description and its category.

Inspired by the way the human brain thinks, the neuromorphic system applies the principles of biological brains to computer architecture, providing low-energy, distributed, and massively parallel advantages for brain-inspired systems. This work presents an energy-efficient spiking long short-term memory (sLSTM) neural network hardware accelerator for sequence prediction applications, containing 256 neurons and 64k synapses in 0.96 mm² area. The sLSTM model can process time-dependent data and realize long-term and short-term memory to forget, memorize selectively. A leaky integrate and fire (LIF) neuron model is proposed to characterize the stimulation of neuronal membrane potentials using simple digital logic circuit without any multipliers, which extremely reduces the power consumption of the hardware system. Accordingly, the chip achieved an energy efficiency of 10.3 uj@50 MHz per sample and a predicting accuracy of about 93.2% in sLSTM neural network model using the stock price of Google from Yahoo finance, based on the modified LIF neuron.

Museums have become a hot spot of tourism. However, the uneven distribution of passenger flow makes it difficult for tourists to find out the shortest and most comfortable tour route. The multi-objective route planning strategy is proposed for the design of museum tour route. This paper presents an improved version of the NSGA-II algorithm, named adaptive 2-opt_integerated non-dominated sorting genetic (AONSGA) algorithm. Based on NSGA-II algorithm, the adaptive probability and 2-opt local search strategy is introduced. Then the computation results on benchmark multi-objective problems show that the AONSGA algorithm has better convergence and diversity performance than the NSGA-II. Whereafter, taking the Palace Museum as an example, a map is established and AONSGA is applied to carry out multi-objective guide route planning. Finally, according to different requirements of tourists, three kinds of specific schemes of the guide route in the Palace Museum are recommended.

The aim of this paper is to screen out abnormal data which is caused by human factors and natural factors. We proposed an algorithm of vehicle's data cleaning and monitoring. First, the valid data is filtered out by an improved DBSCAN method, through data analysis, and then get the threshold range. After that, it screens out the abnormal data in the valid data through the threshold range. Finally, the abnormal data is classified and counted according to the factors which was stipulated by the enterprise. The results show that the proposed algorithm can simpler and faster to process the abnormal data than the other similar algorithm.

Total electricity consumption is a barometer of a country's economy. Long-term forecasting of total electricity consumption in the whole society can effectively track a country's economic development and monitor the implementation of energy conservation and emission reduction policies. How to effectively forecast the long-term total electricity consumption is an important topic in the academic and industrial fields. The combined model of kernel principal component analysis (KPCA) and linear regression (LR) proposed in this paper can accurately predict the changes in total electricity consumption over time, even if the sample size is small. Meanwhile, the model results have strong interpretability and practical value. Further, through the correlation analysis of principal components obtained from KPCA dimensionality reduction, this paper finds that the most important features affecting the total electricity consumption are the economy feature and production efficiency feature. Finally, this paper predicts that China's total social electricity consumption will reach 1.83 trillion KWH in 2035, which is more optimistic than the prediction of Oxford experts, which is consistent with the reality that China has achieved an overall victory in the fight against COVID-19.

In recent years, the grid-connected solar power system has grown tremendously because of its cost-effectiveness and environmental friendliness. However, its interface to the existing power grid introduces frequency and voltage instability due to its lack of rotating mass and inertia response, unlike a conventional synchronous machine. To alleviate the limitations, a novel adaptive synchronverter is proposed to equip with fault ride through in a grid-connected solar power system. It enables continuous electricity generation even under overloading, voltage sag or short circuit fault. MATLAB^®/Simulink simulations are conducted, and it is validated by emulated hardware by Typhoon^® Hardware-in-the-loop (HIL) 402 real-time simulation. The results validated that the fault ride through capability enables the synchronverter to stay connected in short periods of lower electric network voltage.

The extraction of wind energy from more reliable deep-water offshore wind resources can be advanced by using a multi-wind turbine platform that can reduce mooring and installation costs. This paper describes the results of research aimed at designing a novel semi-submersible platform for placing multiple wind turbines. The offshore floating multi-wind-turbine platform (OFMWTP) hosts five 8 MW wind turbines which is proposed for installing on the gulf coast of the United States. The principal problems that have been addressed here include analyzing the effects of blade pitch control on the performance of the OFMWTP in the above-rated wind speed operating conditions. In this process, the adaptive control techniques vary the blade pitch angle for generating optimum power production and reduce platform motions. The coupled dynamics of the wind turbines with the platform are formulated considering the aerodynamic, hydrostatic, hydrodynamic, and mooring forces. In this study, the adaptive control algorithm designed in previous work is used for the blade pitch control. The wind speed distribution with 15% turbulence intensity and irregular waves with zero degrees incident wave angle is used for simulating the operating conditions. The performance of the adaptive control is compared to a baseline proportional-integral (PI) controller. Simulation results showed that the adaptive controller significantly improves the rotor speed regulation and reduces the fluctuations in generated output power under varying operating conditions.

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

As a consequence of the increase of air traffic, the innovative topic of the More Electric Aircraft (MEA) has received increasing attention. In this paper, the control of a bidirectional DC/DC converter for battery management in the MEA framework is described. A detailed simulator and simulation campaign have been designed in order to verify the satisfaction of the MIL-STD-704F standard which regulates the behaviour of electric devices on-board the aircraft.

In order to assess the effects of Yanmenguan HVDC grounding electrode on buried metal facilites, it is necessary to build up the soil model of Yanmenguan converter station. Soil resistivity was measures by Wenner and magnetotelluric method. Then the soil structure of Yanmenguan converter station was inversed based on the experimental data. It was shown that the soil structure of Yanmenguan converter station was three-layer horizental soil. The resisitivity of three layers was 101.7·m, 1037.5·m and 76.4·m. The thickness of three layers was 400m, 4600m and infinite.

It is of importance to assess the electromagnetic influcence of HVDC converter station on buried metal facilities of AC substations, fossil-fuel plants, windfarms and pipelines. Electromagnetic interference model was established based on the experimental measurement of soil resistivity and actual physical location. It was shown that the grounding potential rise (GPR) of Yijing 220kV substation, Banjing wind farm, Nanhuashan wind farm, Jiyangshan wind farm, Liugou wind farm and Limin wind farm exceeded the standard. The effects of distance, soil resistivity and injected current on GPR were also investigated. The results can provide reference significance for design and maintance of HVDC transmission engineering.

Cascading failures occur frequently in many network systems. In this paper, we propose a hyper-network Capacity Load Model based on hyper-edge degrees load to explore the influence of cascading failures on some K-uniform hyper-networks. In simulation, we attack the hyper-edge which has largest hyper-edge degree. When the hyper-edge fails, its load will be equally distributed to adjacent hyper-edges. Through simulation analysis, we get the relationship between the capacity-load model parameters and the robustness of the k-uniform hyper-network. The results show the robustness of the hyper-networks is enhanced with the increase of its hyper-edge capacity. In addition, simulation result show that the robustness of K-uniform hyper-networks is strongest at α=1.

There was a west-east gas transmission pipeline nearby the Yanmenguan DC grounding electrode. In order to assess the electromagnetic interference of Yanmenguan DC grounding electrode on the pipeline, an electromagnetic simulation model was established. The pipeline to earth potential was analyzed. The effects of insulation layers and insulation joints on the pipeline to earth potential were investigated. It can provide theorical support for the pratical operation of gas pipelines.

At present, grounding potential rise is often used as the evaluation index to assess the electromagnetic intererence induced by HVDC grounding electrodes. It will lead to too stringent standards, for HVDC grounding electrodes are considered as long-term operation. Actuallly, HVDC grounding electrodes operates only in the test run stage, planned maintence and forced outage. In this paper corrosion depth is adopted as the evaluation index to assess the electromagnetic intererence. The method to calculate the corrosion depth of wind power generators and AC substation is described. The corrosion depth of windfarms and AC substation nearby Yanmenguan converter station is derived. The effects of material properties, distance and soil resistivity on corrosion depth are also studied.

Due to the complex structure, nonlinearity and tolerance of IF conversion circuit, the failure probability of IF conversion circuit is greatly increased. To solve the above problems, a differential evolution adaptive grey wolf algorithm (DE-GWO) was proposed to optimize the parameters of support vector machine model, and the model was applied to the fault diagnosis of IF conversion circuit for the first time. Firstly, the energy feature of the output signal of IF conversion circuit was extracted by wavelet packet decomposition, which effectively reduced the dimension of feature vector. Secondly, the improved grey wolf algorithm was used to optimize the parameters of SVM model and establish DE-GWO-SVM fault diagnosis model; Finally, taking the IF conversion circuit as an example, the fault diagnosis experiment was carried out and compared with other methods. Comparison results show that the fault diagnosis accuracy rate of this method reaches 97.33%. Compared with the traditional methods, this method can better improve the fault diagnosis rate and shorten the diagnosis time.

The expressions of the air gap flux density and secondary current density of long primary double-sided linear induction motor (LPDLIM) with arbitrary secondary length are derived by the numerical method, which with the secondary as the moving reference frame. The effects of the forward and backward components of end effects on the normal travelling wave and the secondary current density are analyzed respectively, and their temporal and spatial distributions are presented. And then the electromagnetic thrust and its ripple expression considering the longitudinal end effect of the air gap magnetic field and the components of the secondary current density are solved. The slip characteristics distribution of each electromagnetic thrust and thrust ripple components, and the contribution of end effect to resultant thrust and force ripple of the motor with arbitrary secondary length are investigated. Finally, the thrust characteristics of the LPDLIM with arbitrary length secondary are verified by the results of the FEM model. The calculation shows that the appropriate secondary length can reduce the thrust ripple of the LPDLIM.

Memristor crossbar holds promising potential in massively parallel data processing, as computing tasks beyond vector-matrix multiplication typically requires additional complex data conversion modules. In this work, we report a memristive spiking computing network (MSCN) without data conversion. Two memristor crossbars are used to perform vector-matrix multiplication, while spiking neurons are used to integrate the signals from each column of the memristor crossbars. Input and output signals are binary spikes (spiking or non-spiking); thus data conversions between the analog and digital domains are avoided. The MSCN is suitable for accelerating artificial neural network (ANN). A multi-layer perception (MLP) consisting of four layers is simulated based on the analog MSCN, and recognition accuracy of 98.55% under the MNIST test set is achieved. However, in the practical memristor, it is challenging to adjust conductance analogously and even challenging to implement multi-bit conductance states. Therefore, the memristor conductance states are further reduced to 3 bits and 1 bit. Encouragingly, recognition accuracies just slightly decrease to 98.36% and 97.96% for 3-bit and 1-bit conductance states, respectively, which is cost-effective considering the hardware reliability improvement.

With the continuous development of the rail transit field, the communication between trains and vehicles has received extensive attention. Therefore, various international organizations have studied and formulated various communication network standards. At present, the field buses used in rail transit at home and abroad (except Japan) are mainly TCN (Train Communication Network), WorldFIP bus and LonWorks bus. This article describes and compares these three types of field buses.

For the hard-switch applications of the half-bridge circuit based on SiC MOSFET, the crosstalk generation mechanism of the half-bridge circuit in the switching on and off process is analysed. The influence of the driving resistance and stray inductance of driving circuit on the crosstalk is simulated and analysed. In the single-chip half-bridge circuit, the Miller clamp method with BJT + diode is used. By increasing the driving resistance and reducing the clamp circuit inductance, the crosstalk suppression effect can greatly improve. It is an effective way to optimize the clamping circuit and reduce the clamp circuit inductance without affecting the switch speed.

The peak of power load brings great challenges to the safety, stability, and economical operation of the modern power system. Building air conditioning refrigeration is one of the main reasons for the peak load. In this paper, we make use of an ice storage tank to smooth the air conditioning load as far as possible so as to achieve the purpose of peak filling. A mathematical model of quadratic programming is established to characterize the trade-off between the lowest energy storage cost and smoothness via a receding-horizon optimization approach. Numerical examples are given to analyze the relationship between ice storage cost and peak filling effect.

Conceptual design of automated control systems (ACS) is an important stage in their development and making. The miscalculations admitted during this stage, as a rule, couldn't be compensated for in the subsequent stages of the project. At the same time, despite the great importance of this stage, nowadays there is no well-developed theory of conceptual design for ACS making and improvement. In generally, it all comes down to either using the prototype, or the talent and insight of the designer. In this article an attempt was made to develop a methodology for the conceptual design of ACS. A template for creating parametric concept of ACS with its subsequent detail through the use of the method of the categorical analysis is proposed. A method allows to form a fairly complete information space of control, taking into account all possible information interactions between groups of parameters based on the generated sequence of categorical subsets of the control process with their subsequent interpretation is described. An example of method using is demonstrated for development of the ACS concept by a group of unmanned aerial vehicles. A constructive scheme of control information, including data groups and relationships between them, is considered. An interpretation of categorical subsets, an example of detailing and data of the state of the control object and the definition of some information transformation procedures for one of the formed subsets are proposed.

Designing with a Programmable System on a Chip (PSoC) allows for entire circuit designs to be implemented with a single commercially available chip and eliminates the need to physically assemble electronic components. This is possible as the PSoC incorporates a microcontroller with digital and analog components into a single package. While this design allows for much easier implementation of circuits, there are some drawbacks. In this paper we will demonstrate one of those drawbacks, high routing resistance in parts of the analog mesh that connects the analog parts and external pins. We show how this resistance can cause measurement errors when the PSoC is implemented as a single chip potentiostat. As the internal analog routing resistance is in the kΩ range, measuring currents in the Kॅ range can cause mV errors, leading to lose of voltage control during electrochemical experiments. We also demonstrate a calibration routine to compensate for this voltage error that reduced the error by over 90%.

The article discusses the solution to the problem positioning a movable mobile object in a controlled area. The proposed solution is based on the construction of a sensor network from Bluetooth Low Energy radio beacons in the controlled area. The interaction of the mobile device and the sensor network is proposed to be carried out via a Bluetooth communication channel of specification not lower than 5.0 by obtaining the values of the RSSI signal levels of each radio beacon. The room has its own absolute coordinate plane. According to the multilateration method, each triplet of beacons forms its own relative coordinate plane. The developed algorithm also considers the issue of converting relative coordinates to absolute ones. At the end of the study, a description is given of an experimental study on positioning a mobile phone in a 5 by 10-meter room.

In wireless sensor network (WSN), It is a substantial affair to obtain a convenient assessment metric for evaluating created positioning systems. Mean square error (MSE) is one of very paramount assessment metrics utilized for such problem. The conventional mathematical formula of MSE such as the built in MSE function in matlab is utilized only when the real and measured values of the position are available, which means utilizing the MSE is potential only after building the system not while building it. In this paper, for positioning purpose, we offer a mathematical derivation of an MSE formulation based on least square (LS) approach for three-dimensional positioning system without utilizing the real and measured position. The created MSE uses only the measured distance between anchor nodes and a mobile station, that needs its position to be calculated. The formed MSE has the ability to assess a positioning system online based only on the estimated distances, and then the user can modulate his system until obtaining a system ambidextrous to match the requested positioning accuracy. The derived MSE has accuracy 100 % equals to the conventional mathematical formulation such as the built in matlab MSE function.

The paper is aimed to analyse the labour mobility in the region using GIS services. Within the framework of the project, a methodology for continuous automatic collection and accumulation of information on the state of the transport network and weather conditions was developed, tested and implemented. Based on this information, it is possible to analyse the important factors of the regional economy: temporary and financial losses of residents of the Moscow Region on the home-to-work paths, as well as identify patterns of traffic congestion from various factors. The first results of the analysis of the accumulated data are presented, which demonstrate the commuting effects in the regional transport network.

Flexible exoskeletons, also known as exosuits, are robotic wearable devices intended to help healthy and unhealthy subjects in different tasks, such as daily life activities, load lifting or rehabilitation. A position control is required to assure stability and compliance in all assisted movements. In this paper, the authors propose the use of a flexible position sensor based on differential capacitance measurement as position feedback for the elbow joint in the flexible exoskeleton LUXBIT. This exosuit is controlled in position by a super-twisting sliding mode controller (SMC), which is robust against disturbances. Different movements involved in rehabilitation therapies are performed over three healthy subjects in order to evaluate the exosuit performance using this motion sensor. The measures given by the flexible sensor are compared to the ones obtained by OptiTrack motion capture system, which are used as ground truth.

The complexity of the new consumer market requires an equally complex investigative method to observe and analyze experiences, identifying neurophysiological patterns and their relationship with the choice of a product. The observation of the multisensory experience was made using fMRI, EEG and Fuzzy logic. For this project, the coffee consumption experience was analyzed and the neurophysiological responses observed, during the consumption of 3 different flavors, prepared in the same way. Based on the data and processes, a sensory mapping of the observer was generated in which the neurophysiological responses can be verified during the experiment. The data were processed and quantified in order to have a mathematical answer to the experience of each of the flavors and people, in addition to allowing an understanding of the importance of these standards for the selection of the winning option. Especially considering that a verbal response is not always the same as a neural response to the same stimulus.

Due to the outbreak of coronavirus pandemic (COVID-19) in recent times, almost all countries have been affected in a drastic way, impacting delivery mode of education in world everywhere. Several schools, colleges and universities are under lockdown or have been forced to provide online education. To meet the existing challenges due to COVID-19, it has become highly critical that educational institutes become more efficient in virtual delivery of quality teaching services. In here, cloud computing provides an excellent platform for educators to improve their teaching practices and productivity. Cloud computing presents an ideal opportunity as it not only saves costs, but also power, due to the simultaneous usage of infrastructure by several stakeholders for the purposes of teaching, learning, and research. This is a short survey paper which explores the applicability of cloud computing within the context of educational settings. It also describes a few applications such as cloud rendering, gamification and collaborative e-learning technologies. In the end, a few challenges have also been presented.

Measuring quality is extremely important while developing a software product, but there is a lack of knowledge of which are the ISO/IEC 25010 quality measures that are currently being used in the industry. Through a literature analysis of 27 studies, the present article introduces a catalogue of 269 quality measures applied in an industrial setting, which is formed by a subset of 81 metrics defined by GQM approach, a subset of 81 specific metrics for quality characteristics, and a subset of 86 metrics defined through ISO/IEC 25023 and 21 through IT-CISQ. In conclusion, it can be said that GQM is the most widely used method, regardless of standards. Likewise, Maintainability, Performance Efficiency and Usability are the quality characteristics that have shown the highest degree of interest because they are more receptive to the end user.

Currently there are two types of interaction in web systems mediated by synchronous and asynchronous communication, each of them with its programming and interaction paradigms. Although intuitively we can say that asynchronous communication is more efficient in data transport, there is not much research on this topic. We present a comparison of these two types of communication by requesting the same data from a server and receiving a client. The requests are through the traditional web forms and an experimental semantic interface, which only needs the data in plain text as a response from the server. The results found are of interest for the problem of excessive traffic on the network. This research can open new lines of investigation or generate new paradigms and programming models to improve communication between client and server.

The paper reveals the functional, structural and logical organization of new generation virtual streaming computing systems. Such systems allow realizing a new paradigm of computational intelligence and accessing new intelligent computing capabilities. The paradigm that allows realizing new advantages of virtual streaming computing systems is considered. These capabilities are implemented based on information models of a neural network and non-traditional principles for the development and implementation of various forms of computing. New forms of computing make it possible to control the mechanisms of parallelism, virtualization and intellectualization on the information dynamics of objects in the computing environment.

Artificial intelligence applications provide tremendous opportunities to improve human life and drive innovation. AI systems/applications which operate in a real-world environment have to encounter an infinite set of feasible scenarios. Conventional testing approach to test the AI application allows only limited testing and does not allow taking the different contexts into consideration and may lead to insufficient validation and characterization. Therefore, to ensure robustness, certainty and reliability of AI applications, the authors applied classification-based AI software testing framework and 3D decision tables to generate test cases. Moreover, the authors compared the quality assurance metrics (accuracy, correctness, reliability and consistency) of AI and non-AI functions in the AI mobile application scenario. Our results indicate and confirm that complete AI function validation is not possible with conventional testing methods, but AI software testing strategy proposed based on classification framework and 3D decision tables has a good effect.

The globalization has pushed to change the organization of every companies, even the hospitals. The principal phenomenon in that period and fundamental today again, has been the Supply Chain Management (SCM), with which the company is no longer seen as an isolated entity but active part in an extremely complex supply network. In fact, the only way to guarantee the competitiveness of businesses in the new world economy is through the cooperation and the integration between customers and suppliers. The present work analyses the drugs flow of three Italian hospital: the Cardarelli Hospital in Campobasso, the Veneziale located in Isernia and the San Timoteo site in Termoli. The data was provided by MOLISE DATA SPA that collected the information from all ASREM with particular interest in the already mentioned hospitals. Particularly, will be highlight, using simulation model, the benefits deriving from the implementation of a new Supply Chain, creating a collaboration along the entire logistics-production chain. Thanks to a more efficient management of drugs will get a reduction of business costs and an improvement of the health services offered.

Nowadays, companies are experimenting novel organizational solutions to efficiently operate in uncertain and highly dynamic scenarios. As a potential solution, this paper proposes a new business model for a multi-echelon Supply Chain inventory management pattern. Specifically, an inventory model with proactive lateral transshipments was developed and subsequently tested carrying out 288 experiments with the aim of assessing transshipments impact on the performance of a two-echelon Supply Chain. The final goal was to investigate the potential reduction of the overall cost of the enterprise and, conversely, whether this approach could promote significant improvements in the level of service, achievable through a more efficient management of resources. The analyses and simulations indicate the use of large batches and/or low-cost products did not demand the necessity of transshipment events. These preliminary findings could be potentially validated and tested in the future considering more complex networks or multiple products.

With the evolution of operator network, the optical transmission network (OTN) technology which integrates the cross-function of Virtual Container (VC) has become a hot research topic. In this paper, the application scheme of VC-OTN technology including network structure planning, network evolution steps and protection scheme in transmission network is proposed. The pilot project proves that the proposed scheme can significantly guide the operator network construction.

For the fault line selection of single-phase-to-earth network, the instantaneous characteristic of the arc extinguishing coil is worth expecting. Focused on the transient characteristics in fault line selection technique, the sinusoid adaptive filter (SAF) and the sinusoid approximation of instantaneous signal (SAIS) are adopted in this paper. SAF is used to obtain the grounding moment, while SAIS is applied to obtain the direction and amplitude of the zero-sequence fundamental current when grounding happens. As for eliminating the influence of current transformer (CT) polarity, the compensation feature realized by SAIS is effectively adopted in selecting process. The techniques and methods in the paper have been applied to Chongqing Electric Power Co. for 5 years. In 5 years, hundreds of grounding faulted line has been chosen correctly, 6 wave records of which are shown in the paper.

This paper mainly introduces the key information extracting method from User Equipment (UE) test data, the synchronous algorithm of various interfaces X Detailed Record (XDR) data, the key information filling technique of multi interfaces X Detailed Record (XDR), the analytical procedure based on big data is descript in details. An example is given to explain that more accurate reason for Voice over Long-Term Evolution (VoLTE) call not connected can be found with big data from core network, compared with UE data only. With this method VoLTE problem can be quickly solved, it is very useful for wireless operator to improve the user voice service awareness rapidly.

In order to solve the problem that the LTE communication system cannot be easily deployed, we design a test platform of LTE-U communication system based on FPGA. The test platform includes mobile terminals, base stations and base station networking system. It supports 5G unauthorized frequency band, WIFI and 100M network. A single base station can support up to 8 terminals with a distance of 1 km. It uses an omnidirectional antenna to communicate with the terminal and uses a gigabit ring network between base stations. In the process of mobile communication, we use RSSI (received signal strength indication) and bit error rate as the evaluation criteria and test the 32 terminals covered by the chain network and 4 base stations. The test platform can complete the LTE-U test well.

In the transmission system, the continuous load operation and the external environment will make the equipment fault hidden danger, which will affect the stable operation of the transmission system. In this paper, an edge intelligent analysis system of transmission equipment defect image recognition is proposed. The system migrates the cloud image recognition to the edge. Feature Pyramid Network(FPN) is introduced into Single Shot MultiBox Detector(SSD) object detection algorithm based on MobileNet v1 feature extraction network to detect defects of different sizes in large-size, small-size, tower and insulator equipment. So as to realize the rapid positioning of defects and upload the results to the cloud. The results of system application show that the accuracy and recall rate of the proposed system are high. In the case of ensuring the detection accuracy, it meets the real-time requirements of detection. The system can effectively improve the automation level of transmission equipment operation and maintenance. While improving the operation and maintenance efficiency of transmission lines, the safe operation of the transmission system is ensured.

This work deals with source localization with time-difference-of-arrival (TDOA) measurements. Recently, a multidimensional scaling (MDS)-based iterative localization scheme is introduced in the literature, where an MDS-based cost function is defined as the norm of the difference matrix between two scalar product matrices. The minimizer of the MDS-based cost function which is computed by Newton's iteration is considered as the estimate of the source position. However, the scalar product matrices of the MDS-based cost function are of high order, which need a lot of computations in each step of the Newton's iteration. In this paper, a computational-efficient iterative localization scheme is proposed. In the proposed scheme, a simplified MDS-based cost function is constructed from two low-order matrices that are converted from the high-order scalar product matrices by a few steps of Lanczos iteration, and then the Newton's iteration is applied to find the minimizer of the simplified MDS-based cost function. Simulation results show that the localization accuracy of the proposed scheme is nearly the same as that of the original scheme, whereas the computational complexity of the proposed scheme is about 20% as much as that of the original scheme.

In order to assign an objective and compre-hensive weight to each index for evaluating the GSM-R network quality of service, this paper used the combination of subjective and objective weight value to overcome the singularity of determining the weight from one aspect. The entropy weight method was used to determine the objective weight, which was assigned to each index by the entropy calculation. The subjective weight was calculated on the basis of the improved analytic hierarchy process (AHP) and the scale construction method was used to construct the judgment matrix, which reduced the computational complexity and redundancy. In addition, three kinds of calculation methods were proposed for combining multiple experts' opinions. Combining the subjective and objective weight vectors obtained by different combination methods, the results of multiple integrated weight vectors were obtained. Then the average value was calculated for each index. By comparing the correlation coefficient between each integrated weight vector and the average weight vector, a better multiple combining calculation method was determined to calculate the weight of each index in the evaluation system. This paper combined the actual test data of ZX section of XL high-speed railway and the experts' questionnaires to obtain the weight assignment of GSM-R network quality of service indexes. It can be seen from the results that the voice quality, switching success ratio and average handover delay account for a large proportion, which is in line with reality. The judgment method of weight proposed in this paper is objective and feasible.

With the rapid development of high-resolution remote sensing camera and the rapid increase of the number of remote sensors, telemetry data and image data have the characteristics of large amount of concurrency and high real-time. These characteristics bring great challenges to the test, interpretation and processing of massive data, which requires a lot of manpower and time resources, meanwhile, it is easy to cause repeated test work because of the errors occur during operations and interpretations. In order to improve work efficiency, test coverage, test accuracy, comprehensiveness, and human resources, developing automatic and intelligent test equipment is becoming much more particularly urgent and necessary. The test system proposed in this paper uses the on-line monitoring and diagnosis system to analyse the running state of the equipment in real time, find out the system fault in time, locate and eliminate it. The system can put forward the network protocol of data processing and storage, command control, real-time analysis and transmission of telemetry information and display the realization way of the test system workflow. It is expected that the system test efficiency will be increased by more than 5 times. In addition, the efficiency of fault diagnosis and data processing will be increased by more than 3 times. The automatic test system can solve the conflicts between test quality and human resources, moreover, the architecture patterns based on Ethernet is easy to expand and reconstruct.

At present, the common problems facing the low-voltage power distribution system lie in plenty of equipment and wide range of power distribution. In order to address these problems, the overall architecture of the cloud-pipe-side-end is introduced through the IoT system in electric power distribution, where the end device is connected to the low-voltage power distribution system via the serial bus of the edge device and carries on data collection and real-time edge computing. As the core node, the edge device is required to have excellent real-time capability of timely detection and real-time response to the fault of various low-voltage power distribution facilities. In consideration of this, this paper proposes an on-demand serial port interrupt threading design to ensure the real-time performance of an electrical edge smart system. According to the experimental results, as the number and load of serial port devices increase, the real-time task dispatching rate continues to decline until the transformation, and the impact on the real-time task dispatching rate is limited after the threaded interrupt. That is to say, the method is effective in ensuring the dispatching rate of real-time tasks for the edge smart terminals.

Microbial metabolism network is significant for the study of the microbial community, which is crucial for microbiome related diseases, such as inflammatory bowel diseases (IBD). In order to understand the difference of gut microbial communities between IBD patients and healthy people. Firstly, metabolic bipartite networks—microbes-compound graph are proposed and then built for healthy people and IBD patients respectively, which preserve more metabolic information than the traditional unipartite network. Secondly, with the use of the community detection of LPA in weighted bipartite graphs, the community modules of the two networks are obtained. Finally, two networks are compared to analyse the differences between healthy people and IBD people from several perspectives, such as NMI, centrality, clustering coefficient, species and compounds related to IBD disease, and cross-validation is performed to prove that all results are reliable and robust. The result shows that the gut microbial communities of healthy people and IBD patients are quite different, and the diversity and stability declined. From the clustering results, it can be judged that the distribution of disease-related bacteria changed.

The safety of equipment operation is important for the operation of rail transits. The lifetime of equipment and its improvement methods have become one of the key issues in the operation and maintenance of rail transits. Based on the fuzzy comprehensive evaluation process and evaluation result data, the decision-making algorithm for improving the lifetime of the equipment was discussed, including the strategy recommendation algorithm based on assessed data and the strategy matching algorithm based on case reasoning. Taking the actual data of onboard ATC equipment of Line 11 in a city as an example, the effectiveness of the two methods was verified. The methods in the study can support the decision-making process for maintenance departments.

The TCP protocol performance over Wireless Local Area Networks with 802.11b is analysed in this paper. Based on the communication system consisting of a large number of source-destination TCP pairs, a model for a TCP connection is proposed. According to the principles of TCP and RTS/CTS Access mechanism, the normalized throughput and the utilization of time slots are conducted. Based on the relevant parameters at TCP and MAC layers, TCP performance are analysed, and factors which determine TCP performance are explicitly obtained.

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

Maneuver detection is crucial to improve the performance of space target tracking. A real-time maneuver detection method based on ranging measurements is proposed in this paper. The prediction residual of ranging measurement is selected as the test statistics. Then the maneuver and the outliers can be distinguished by setting multiple hypothesis tests. The traditional strong tracking filters can be improved by applied this detection method. The superiority of the proposed method is verified by a simulation case. Simulation results demonstrate that the new method can provide higher tracking accuracy than the traditional methods when the maneuver and the outliers both exist, and the maneuver and the outliers can be detected exactly.

After more than 100 years of development, motion capture technology has been advanced greatly. In the initial stage, the motion capture technology was only capable of capturing a rough range of body movements. But now it is sensitive enough to capture the movement of the whole body. Motion capture technology has also been developed into different types: optical and non-optical systems including inertial, magnetic, and mechanical. There are also hybrid and depth sensing camera-based systems. Nowadays, with the combination of the technology and arts, computer software and internet technology have become a new territory for dance performance. Motion capture plays an increasingly important role in dance creation. Meanwhile, the combination of motion capture and virtual reality technology enables the audience to freely choose different viewing angles and moving positions in a totally virtual space, which can present stronger sense of tension and cultivate more immersive experience than live dance performances do. On the Internet, virtual reality dance theaters connect dance performers and audiences from different geographical spaces. The virtual reality dance heaters build up virtual performance venues, paralleling with the real theater performance in the physical world.

Electric energy is an important part of building energy consumption, and air-conditioning accounted for most of the consumption of electric energy. Ice storage air conditioning is widely concerned because of its strong adjustability. Due to the limited space, it is often necessary to use multiple ice storage devices simultaneously to meet the application requirements. In this paper, a distributed coordination method for multiple ice storage devices is presented, which can allocate storage tasks according to the capacity of the device. Each device only needs to communicate with its neighbouring cold storage device, and finally achieve the control goal through distributed iteration. The convergence of the proposed algorithm is analysed by rigorous mathematics. If the communication topology between devices is connected, the algorithm can converge to the expected point. Finally, the validity and suitability of the algorithm and the correctness of theoretical analysis are verified and evaluation by digital simulation.

Illegal behaviours often occur in using public cars in the enterprise. To detect abnormal vehicle usage, we propose an intelligent vehicle monitoring system. This system adopts the architecture of separating the front end and back end. Automatically alarm for exceptional vehicle usage by data analyzing and show the statistical results in the form of dynamic charts. This system enables enterprises and institutions to effectively grasp the shared data resources and promotes the informatization, standardization, integration and intelligence of vehicle management, so as to improve work efficiency and economic benefits.

This paper introduces various common defects and corresponding screening methods of electronic components. Aiming at the temperature cycle and random vibration methods with good screening effect, the calculation formula of accelerating factor and screening strength in highly accelerated stress screening (HASS) test is introduced. Based on the above discussion, the stress screening test steps of electronic components are summarized to guide the selection of reasonable stress screening items and levels for electronic components.

We present a very lightweight encryption method based on multiple substitution tables. The method does not use sophisticated integer arithmetic and is therefore suitable for small embedded systems and other applications using simple processing elements. Plain text is encrypted one byte at a time. Each byte is replaced by the substitution element in the current table, and the plain text byte also determines the table to be used for the next byte. Encrypting and decrypting one byte involves only a table look-up, no arithmetic or bit manipulation. The same plain text byte has very small probability of being encrypted the same way twice in a row. We also present additional techniques for making the method secure against attacks when used in applications where the messages may have special or restricted forms.

The work presents the results of the development of requirements for the content of a digital passport and design solutions. The composition of each of them is listed, taking into account the specifics of the activities of a particular enterprise and the design tasks to be solved. On this basis, an execution sequence for creating and using a digital passport is proposed.

The design procedures for the analysis of the components and parameters of a digital passport is a main part of the development of design procedures for the analysis of design solutions. Its main idea is to clarify the models of components and their parameters, formed at the stage of creating a digital passport, and use them to find the desired solution. Further, the development of rules for the selection of digital passport components descriptions in the form of fuzzy variables is performed. As a result, for each component, a range of values is determined that is comparable to a similar range specified by the requirements for the design solution. The results obtained in this case are used in the development of a similarity criterion, as well as an algorithm for forming a design solution.

In machine learning, a fundamental challenge is the analysis of data to identify feelings using algorithms that allow us to determine the positive or negative emotions that people have regarding a topic. Social networks and microblogging are a valuable source of information, being mostly used to express personal points of view and thoughts. Based on this knowledge we propose a sentiment analysis of English tweets during the pandemic COVID-19 in 2020. The tweets were classified as positive or negative by applying the Logistic Regression algorithm, using this method we got a classification accuracy of 78.5%.

In the last decades, the computerized handling of data has played a fundamental role in different economic areas, since there are several technological instruments that help to speed up a number of processes, so it is of utmost importance to learn how to use these tools, otherwise the information would be compromised. This paper aims to present a model for small and medium enterprises, through a pro-cess of identification and analysis of security measures in companies, based on the study of each company and in relation to the size of each organization, allow-ing to know the concepts of network monitoring that are applied in the creation of strong security policies for their facilities.

Bug tracking systems (BTS) are a resource for receiving bug reports that help to improve software applications. They usually contain reports reported by the end-users or developers. Bug Reports contain some suggestions, complaints, etc. The problem is that every submitted bug report is not accepted to implement. Mostly bug reports are rejected because they are incomplete, duplicate, expired, etc. while only a few are accepted to implement. But the developers have to check every bug report manually that needs many resources (i.e. labour, money, time). In this study, we proposed a convolutional neural network (CNN) based approach to automatically classify bug reports as accepted and rejected. Results show that the proposed approach achieves the highest performance as compared to closely related works.

Cybercrime perpetuates as a major threat to the reputation and economy of a nation. There is consensus in the media and academic journals alike, that cybercrimes have incremented over the years. During the coronavirus pandemic in the year 2020, a global lockdown imposed by governments forced people to be homebound, leading to a subsequent surge in online users. This resulted in cybersecurity risk hitting close to home, with an upsurge in cybercrimes. This article attempts to make sense of the elevated cybercrime landscape. For the last two decades, criminological research have attempted to explore variants of technology-enabled crime and have examined theories to account for offending. This study proposes 'Routine Activity Theory' to explain the magnitude of cybercrimes during this unprecedented period that reshaped people's routine lifestyles. Routine activity theory predicts that changes in opportunity situations (increase in online users), potentially escalates the convergence of motivated offenders and suitable targets in the absence of capable guardianship. Guardianship (security measures) is pivotal in this theory, to protect the targets (online users) from offenders (cyber threat actors). Furthermore, this paper offers practical security measures, in light of the specified cyber-attacks during this unprecedented period. If this theory is empirically validated, it offers opportunities to mitigate the extent of cybercrimes with an upsurge of online users, and more so for cybercrimes in general.

The statement of the basic control problem of a situation with many active stakeholders using cognitive maps is presented. Approaches to the analysis and resolution of interest conflicts in such situations based on the basic control problem are proposed. These approaches make it possible to take into account different interests, identify contradictions and, if possible, find ways to overcome these contradictions by choosing different strategies for interaction between stakeholders. The problems systematization of conflict analysis and control in such situations at different phases of the conflict is presented. The systematization purpose is to organize monitoring, analysis and control of conflict situations using cognitive maps and scenario modeling tools based on them.

The paper presents the idea and implementation details of a novel tool intended for detection of plagiarism in mathematical content of LATEX files. The proposed algorithm compares LTeX files and detects possible similarities in mathematical formulas, providing an objective similarity score. It offers two modes of comparison: direct (symbolic) mode and verbalized math mode, where the mathematical expressions are first verbalized to spoken language form and compared as such. The solution has been tested against identified various types of plagiarism specific to mathematical symbolic notation and LATeX features, and proved to be an efficient tool for plagiarism detection.

Request food online is daily present in the life of people, however, it is necessary to send delivery information to different restaurant systems and sometimes these restaurants share customer sensitive information with their partners without the authorization of the customer creating a security risk with customers data. Also, in most cases when a person travels to a different country and wants to request food online, it is necessary to use the local currency, requiring a currency exchange operation. This paper evaluates a proof-of-concept (POC) of the integration of blockchain in the food industry, allowing customers to order food online through a sidechain, an exclusive blockchain with specific custom transaction types for restaurants. The FoodTransaction type is responsible to order food and store cryptographically customer sensitive information that only the customer itself and the requested restaurant can read, hence it respects customer privacy. Furthermore, the blockchain cryptocurrency can be used anywhere in the world. The performance evaluation shows a great advance in throughput when compared to the use of the payment solution on Bitcoin applications. Finally, the results show the possibility to extend the restaurant sidechain business to several restaurants in the world and in different time zones.

Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.

The rapid development of the information Internet has promoted the development of electronic contracts, but user identities have been stolen and electronic contracts are easily tampered with. Seriously affected the fairness and security of online electronic transactions. For the main body of the transaction, how to confirm that the data identity of the transaction body has not been misused and the transaction electronic contract has not been tampered is the key problem which needs to be solved first. This paper designs the electronic contract platform by combining user identity authentication, encrypted transmission of electronic contracts and blockchain ledger storage. Ensure user identity uniqueness, contract transmission data integrity and non-tampering, and traceability of the signing process, thereby achieving security and fairness.

In the digital era, a large amount of data is generated every day and recorded on the storage system. Access control is an important mechanism for ensuring data privacy and security. Data accessing control model of traditional storage system is centralized, which has threatened the information security of users. As blockchain is emerging technology which is widely used in the area of data privacy preserving, in this paper we proposed a decentralized access control model via blockchain based smart contracts implementation. Once satisfying the conditions set by users, visitors could apply to the blockchain for authentication, then obtain permission to access user data and operate the data, finally achieving secure access control of user data. In our experiment, we build the blockchain system based on Hyperledger Fabric project and evaluate both the efficiency of writing and the verification of access permission. The result shows our model's usability and efficiency.

High Performance Computing (HPC), also known as Supercomputing, is widely applied in various fields, which have the advantage of high processing capability and mass storage. Various open business services bring great threat to the network and data security of HPC cluster. This paper proposes a feasible scheme to realize supercomputing information security through multi-layer defense and overheads control according to the experience of operation, management and maintenance of Supercomputer Center for many years. First, this paper puts forward the principle of moderate risk, and gives the principles of design and the implementation for the security protection of Supercomputing Center. Second, this paper presents a security system architecture in coordination with deep collaborative defense. Finally, this paper designs a 4-layer protection scheme on account of the latest network security solutions and the operation and maintenance characteristic of our own supercomputer. Experiments on multi-level defense and overheads control show that the designed scheme takes into account the cost and network security, and obtain prospective results., which is especially suitable for the network security protection of small-scale or medium-scale high-performance computing cluster for engineering application.

Aiming at the problem that the data cannot pass the consistency test in the analytic hierarchy process (AHP), an improved AHP is proposed. The implementation scheme of the improved AHP based on D-S evidence theory and gray theory is designed, and matlab is used for simulation experiment. The results show that the improved method can effectively enhance the reliability of network security risk assessment results.

Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.

As one of the key technologies of fifth-generation mobile communication, device-to-device communication technology can not only realize direct communication between neighboring devices, but also reduce the delay of local services provided to users and increase the communication rate. Nevertheless, the special structure of the device-to-device communication network makes it more vulnerable to attacks from inside and outside the network. This paper summarizes the security issues faced by device-to-device communication networks, analysis the secure routing designed for different types of attacks, and discusses future research directions.

Nowadays, the application prospect of cloud storage is more and more extensive, and the security problem of cloud storage has become one of the hotspots of research. Searchable encryption can make full use of the huge computing resources of cloud servers and solve the problem of secure search after data encryption and upload. However, the existing searchable encryption schemes have the problems of high computational cost, low security, and insufficient flexibility in the way of searching keywords. In order to solve these problems, we propose a new certificateless multi-user fuzzy keyword searchable encryption scheme, which introduces the key distribution center, effectively reduces the computational cost, solves the problem of certificate management and key escrow, and uses the optimized wildcard technology to realize the fuzzy search of multiple users, which is not only brings convenience to users, but also realizes flexible search methods. Finally, the analysis results show that our scheme has indistinguishable security under keyword guessing attacks. Simulation experiments and performance analysis results show that our scheme has higher computational efficiency.

The identity-based signcryption scheme combines the advantages of the signcryption scheme and the identity-based cryption scheme, which has high calculation and communication efficiency and easy key management. In order to effectively solve the problems of high latency and data information privacy during the secure transmission of drone data to cloud servers, we propose an identity-based proxy signcryption scheme that supports outsourcing decryption and member revocation. The complex bilinear pairing calculation is outsourced to the cloud server to reduce the user's computing overhead. When an illegal user wants to access the data, the illegal user's identity value ID will be put into the revocation list and the update private key algorithm is executed to generate an update password for the unrevoked user Key, revoked users lose the ability to decrypt because they cannot obtain the updated key that matches them. The identity-based signcryption scheme protected by the proxy can satisfy unforgeability and forward security. Finally, the comparison results show that this solution is superior to previous work in terms of function and calculation time.

The sharing of electronic health records (EHR) has shown significant advantages in the accurate diagnosis of patients and the development of medical institutions. However, due to the privacy and sensitivity of medical data, it is easy to cause security issues such as difficulty in data sharing among different medical institutions and easy leakage of data privacy. Because the blockchain has the characteristics of non-tampering, anonymity, and decentralization. We propose a blockchain-based searchable proxy re-encryption scheme for EHR security storage and sharing. First, we use blockchain and cloud server to store encrypted EHR together to prevent EHR from being tampered with and leaked. Secondly, we use a certificateless encryption and proxy re-encryption based on identity and type scheme as a data sharing protocol. Meanwhile, searchable encryption technology is used to generate a keyword index. Moreover, the proxy node is selected by the delegated proof-of-stake (DPOS) consensus algorithm, which ensures the privacy, immutability and security. It realizes the safe access of third-party data users to medical health data. Finally, security analysis and evaluation show that our scheme can resist identity disguise and replay attacks. In addition, it has stronger security and higher efficiency.

In the cloud storage environment, how to efficiently and dynamically complete the integrity verification of multi-user and multi-replica data is a challenging problem. Based on the bilinear mapping signature mechanism and the multi-branch authentication tree, we propose an identity-based integrity verification scheme of multi-replica data for cloud, which solves the problem of large public key storage and management overhead in signature algorithm based on traditional public key cryptosystems. The proof shows that our scheme satisfies the robustness and has the function of protecting privacy. The results of analysis indicate that, compared with the existing similar schemes, our scheme has higher communication efficiency and computing performance.

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.

The research of intrusion anomaly detection in industrial control system is facing the problem of few attack samples. This paper proposes an attack data sample generation algorithm based on dual discriminant model. Firstly, a few attack samples are generated by multiple attacks based on the original data set. Secondly, the sparse matrix algorithm is used to expand the data set, and the original data features are distributed to each extended data to ensure the authenticity of the seed data. Finally, a large number of attack samples are generated by the dual discriminant model to complete the sample expansion. The experimental results of Mississippi SCADA natural gas pipeline data set and an industrial control system data set show that, based on keeping the data in a reasonable range, the algorithm can obtain an effectively expanded sample data set. SVM, Random Forest and XGBOOST are used to classify the dataset. The results show that AUC index is better than the negative sample data set generated by traditional GAN algorithm, and is similar to the initial negative sample index.

Max-plus algebra is the structure that doesn't have an inverse of additive. Therefore, there exists an equation that doesn't have a solution. For example, equation 3 ®x=2 has no solution because there is no x such that max(3,x) = 2. The max-plus will have an inverse element of addition if that structure is extended to the symmetrized max-plus algebra. The expansion into a larger system is the same as the expansion of the natural number into an integer number. This paper describes the necessary or sufficient condition of the eigenvalue of matrices over the symmetrized max-plus algebra using the linear balance systems A®xV b with V as a balance relation.

The importance of occupational guidance for individuals has been highlighted in the article. Modern tasks and challenges of occupational guidance have been presented and conditions of a personality's successful self-realization have also been analysed. The article deals with the application of modern methods and forms of occupational guidance for students. The authors present the results of approbation of the computer-based testing complex "ProfVybir", performed during 2015-2019 for the diagnostics of occupational interests and inclinations. It is used as a separate information service for students of 8-11 grades. The complex was created in Centre of Psychological Diagnostics and Training Technologies "Insight" of Pavlo Tychyna Uman State Pedagogical University. During the mentioned period 780 students took part in occupational diagnostics. The following algorithm of work with the computer-based testing complex consisting of three blocks is presented. The article defines that the method of occupational guidance can be used to implement the policy of the Ministry of Education and Science in the development of occupational guidance, can be applied by a career advisor at school, while the computer-based testing complex may be an additional component of the occupational training program.

The problem of poor systematic support of the stage of ideation in practical engineering design is addressed. Mathematical modelling of process's dynamics is used to generate alternative architecture of a technological unit. The approach is applied to the design of mixing-averaging complex, where the analysis of statistical dynamics equations paves the road to new design concepts. The parametric analysis and optimization of new solution demonstrates potential benefits and savings. The study suggests an iterative scheme of conceptual design, where quantitative and qualitative design tools work together.

This article discusses the impact of human-computer interaction in the pedagogical design of open textbooks affordable into a diversity of learning environments. An exploratory study is conducted, wherein an online questionnaire is administered to the second-year master's degree students from the Faculty of Psychology and Educational Studies. The results show that students are aware of the importance of metacognition in developing effective learning strategies. Thus, they use metacognitive strategies when taking notes, doing reflections, and employing mnemonics to enhance understanding. However, despite the current practice of using open didactical materials, students feel that the open textbooks approach needs to be relooked and changed to be more effective; it could be equipped with appropriate learning tools to provide more opportunities for immediate feedback and collaborative assessment. Individualization and collaboration are the key factors in developing metacognitive experience. These findings indicate that students are cognizant of the important role of open textbooks in promoting learning.

In order to determine the values of the dependent variable called "Color of the mango puree", first the peel and the seed must be extracted by pulping and secondly refined again on a fine mesh sieve to obtain the puree. Five mesh sizes were evaluated from the first independent variable called "Sieve size" and corresponding to the second independent variable "Interaction time" ten different times were evaluated every 15 minutes from zero minute to 135 minutes. As for the dependent variable, the Hunter Lab coordinates were evaluated, using a Minolta CR-400 colorimeter, with three levels of the variable, which are the brightness, the a-coordinate a (a+: yellow/a-: blue), and the b-coordinate (b+: red/b-: green). As far as the results of the colour coordinates are concerned, during refined pulping for the same time, predominantly the smaller the hole size (larger mesh size) the lower the values for luminosity and the b-coordinate (b+) will be obtained: yellow colour; whereas as the interaction time increases from 0 minutes (freshly refined) to 135 minutes (final interaction time), predominantly lower values will be obtained for both brightness and b-coordinate, and there is no marked difference to the a-coordinate values (a+: red colour).

The article follows the previous work of the authors. The paper aims to elaborate ontology into a solid form using directly for the production factory. The authors divided the article into two parts. The first part explains the basics of conceptualization, and the second part is devoted to creating and describing ontology using the RDF model. As an example of ontology's practical use, there are codes in the programming language #C.

Research development on mathematical modeling for thermochemical treatment especially in low temperature started from the empirical-based research which focused on conducting many experimental studies to gather metallurgical data. Several thermochemical treatments have been developed experimentally using various process parameters such as temperature, treatment time and gas compositions to understand the effect of certain factors towards the resultant layer. The availability of these experimental data was a crucial factor to obtain precise simulation of the diffusion process by thermochemical treatments in the stainless steels which can reduce the trials and errors of the experimental woks. However, the lack of the metallurgical data remains an obstacle to correlate the empirical and simulation-based research. The recent additive manufacturing research area is also an attractive challenge to generate a mathematical-based simulation of thermochemical treatments of additive manufactured specimen. In this paper, a historical review in connection with modeling development in low temperature thermochemical treatments is presented including the suggested future works to attain commercial software availability in progressive industrial development.

In the present work we employ a load balancing scheme involving an adaptive and dynamic workload redistribution both along Space and Time directions for solving Data Assimilation problems where the observations are non-uniformly distributed, general sparse and its distribution changes during the time. We will consider the Constrained Least Square model (CLS) as prototype of Data Assimilation problems and we will validate the proposed approach on different configurations. Validation is performed using Parallel Computing Toolbox of MATLABR2013a on high performance hybrid computing architecture.

Lighting design today is one of the most actively developing types of design project activities. The definition of the profession reflects a wide range of established practice, which implies the presence of various theoretical positions. However, all experts without exception note the multi-level structure of the new profession, its pronounced interdisciplinary nature, which presents an additional burden for the designer. The scope of creative, engineering and social tasks, as well as IT technologies related to lighting design, reflects the nature of a complicated stage in the formation and development of the profession. The purpose of this publication is to acquaint the professional audience with the complex nature of light design, to identify a number of disciplines that make up its basis. This will allow to outline the ways of formation of new kinds and genres of lighting design, to identify its main priorities. The task of the designer is to choose a direction of activity at various levels: functional, applied, or easel direction; adhere to traditional urban light, work in the genre of multimedia light show, author's light sculpture, etc. The multidisciplinary nature of the profession is united methodically by the scientific platform of creative activity. The design and artistic component becomes a priority in the hierarchy of innovative professional synthesis of that particular direction. Thus, lighting design is included in the sphere of project creativity, including the scientific and technical base of the new profession. The innovative potential of that activity has a significant impact on the process of its development, on the creation of a product and - as a result - on the quality of life of a wide range of consumers. In the work on the material, an analytical method of art history research was chosen. This allowed us to describe the emerging multidisciplinary problems of lighting design in a fairly broad and comprehensive manner and to outline ways to solve its current problems. The vocational education system is called upon to play a special role in this process.

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, or simply to conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of meters of elevation gain. Estimating thermal locations is not an easy task, pilots look for different indicators like color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the conditions for thermals by estimating solar heating of ground (cloud cover and time year/date) and also the lapse rate and dew point of air. In this paper, we present a Machine Learning based solution to forecast thermals. Since pilots in general record many of their flights locally and sometimes upload them to databases, we use the flight data uploaded to determine where the pilot encountered thermals and together with other information (weather and satellite images corresponding to the location and time of the flight) train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information (obtained from Google Earth Engine). Results show that our model is able to converge on the training and validation set with a loss bellow 1%.

International cargo containerization system continues progressive development supporting increase of multimodal transport traffic. Containerized commodity transportation schemes are highly efficient for majority of transcontinental and long-distance deliveries optimizing costs, time and quality of transport operations basing on exact forecasting of container turnover. Following these headline routes, research article represents actual methodology of forecasting cargo volumes in accordance with Big Data stated in Transport and Economic Balance of the Russian Federation (TEB), as per spatial input-output predictions of freight traffic between regions of the country by rail, road, inland water and maritime transport by types of commodities. Expanding transportation network is linked to the core freight multimodal transport and logistics centers (TLC), connected with 12 transport hubs having strategic value for Russian economics. Represented research algorithms consider cargo base for 12 TLCs in backbone network subject to types of commodities, growth production and consumption, import and export balance in the strategic timelines of 2024 and 2035. Methodology of forecasting container traffic balance across the country is based on coefficients of container demand for each category of cargo as well as transport modes and transportation schemes. Container traffic forecast indicated by scenarios of TEB model reflect strategy of development and optimization for the freight flows in TLC network. These information models, due to their complex structure and rich semantics, are more likely to belong to the class of models based on knowledge, than on data, that requires further improvement of forecasting methods using intelligent processing of Big Knowledge-Based models.

This research is a secondary analysis of a multivariate dataset coming from a large-scale assessment, coordinated by UNESCO in 15 Latin American countries (LAC), which was carried out for estimating the level of learning achievements of students at the end of primary education. The objective is to estimate the lack of quality in education across the region, based on the sociological concepts of absolute and relative deprivation, through a new learning deprivation index, as well as to offer robust evidence on the deep inequalities in learning outcomes among groups of population. Main results show strong evidences on how big gaps in learning outcomes are associated with socioeconomic status of students, pointing out that the profound lack of equity in LAC has become structural inequality beyond borders. This study establishes three milestones in educational research using the best available data. First, provides a direct method for estimating learning deprivation. Second, intra-and inter-country inequality is estimated from comparable socioeconomic deciles at regional level. Third, it was found that the intensity of learning deprivation, as a function of the SES, has an interplay with the wealth of the country and it is not possible to apply same strategies in all countries for developing inclusive and equitable educational for all.

Cities play a pivotal role in the global economy as they are the hubs of trade, industry as well as social and cultural activities. Nowadays, more than 50% of the world's population live in cities and this number is steadily rising throughout the upcoming few years leading to a highly significant change in the world's demography. This remarkable growth in urbanization will make cities struggling to provide the basic services to their citizens and will encounter enormous challenges such as traffic congestion, more pollution, excessive energy consumption, extreme water usage, more waste, etc. Consequently, a smart mechanism needs to be implemented in-place to effectively manage the natural resources and assets within cities to ensure the sustainability of services and to provide a high quality of life to residents. Hence, the concept of smart cities has been introduced and becomes one of the most focal and hottest research areas in recent years. Generally, smart city is a combination of urban systems that are interconnected to each other using Information and Communication Technology (ICT) along with Internet of Things (IoT). The shift from a single system to systems of systems would accelerate the development of various sectors including healthcare, education, transportation, economy, energy, housing, etc. Nevertheless, this transfer is extremely challenging due to various aspects related to infrastructure concerns, data storage, subsystems integration as well as managing data in decision support systems (DSS) for end-users. This paper elaborates on the current state-of-the-art in the Systems of Systems Engineering in the context of smart cities. In addition, it highlights some of the smart cities long-term challenges, potential solutions along with new research directions.

Artificial Intelligence can provide intelligent solutions for the educational system. This article uses an object detection algorithm to explore physical surroundings and extract geometric shapes from two- and three-dimensional objects in real time within or outside a classroom. Having the shapes, the geometry lesson begins. We named it "Convergent Classroom". We have conducted a post-test only group design in which first-year pupils from secondary school participated in the sessions. Our main results show substantial statistical evidence to support pupils' higher geometry acquisition engagement using computer vision algorithm, compared to those who did not use it. This nature-to-digital-to-cognition engagement can be further explored by neuroscience measurement to understand what happens in pupils' brain when they connect geometrical shapes from their surroundings to geometric cognition. Furthermore, these observed significant differences call for teachers to implement the already known algorithms in future classrooms.

The aim of this study is to analyse the characteristics of polish Covid-19-focused twitter posts identified via Covid-19 hashtags. The use of the popular service gives us an opportunity to monitor changing trends in users' opinions, and changing focus of the most popular topics in the Covid-19-oriented tweets. As part of the analysis, an exploratory sentimental analysis was used to identify the main topics, emotional characteristics, and possible changes over time in the tweeter statements. The emotional characterization of the statements allowed to classify them as negative, neutral or positive. The analysis used wide range of classical text mining, sentiment analysis and latent semantic analysis methods. The use of the methods allowed obtaining the most accurate picture of the tweets. The obtained results indicate the importance of social media in creating opinions.

Scientific publications are the most important resources available to the research communities. Researchers want their work to be widely recognized and available and also need powerful search engines to identify other publications and researchers working in the same area. Therefore, a good representation and organization of scientific products is crucial for an accurate retrieval of information. This paper describes an approach for automated population and semantic enrichment of an ontology model that represents scientific publications. Specifically, the type of enrichment used in this approach consists of implementing semantic similarity measurements between publications. Several experiments were performed to identify the best similarity measurement, using a statistical approach and the precision of the measurements.

In this paper, a physical model of functionally graded materials (FGM) with a linear change of volume fraction of ceramic and metal in its thickness direction is established. The thermal conductivity of the FGM under a linear heat source is studied by a hybrid numerical method. Based on the weighted residual method, the heat conduction equation under a third boundary condition is studied using a hybrid numerical method which takes into account both the accuracy of the analytical method and the efficiency of the finite element method. A linear heat source is applied to the FGM, the temperature of the heat source changes linearly with time, and the temperature distribution in the space-time domain is obtained by finite element discretization in one direction and Fourier transform in the other. The results show that the closer to the heat source, the greater influence of the heat source it is. The influence of the heat source is local, which is similar to the influence of the force field on structure described by Saint Venant theory. In the heat transfer process, the heat transfer efficiency of each surface is different, that is closely related to the material properties. Using this proposed physical model, it is demonstrated that FGM can avoid the sudden change of temperature and relieve the thermal stress.

According to the relevant theoretical methods of omnidirectional mobile mechanism, the kinematic model of omnidirectional mobile platform under theoretical conditions is established, and the dynamic model of omnidirectional mobile mechanism in actual environment is analyzed according to the specific situation of platform body structure and actual influencing factors. Based on the kinematic model, a backstepping based trajectory tracking control method for omnidirectional mobile platform is designed, and the trajectory tracking error equation is solved. In order to further improve the stability of the system, a fuzzy control system based on backstepping method is designed based on the dynamic model, and the control torque of the platform is selected. Finally, the effectiveness of this method is verified by numerical simulation.

In order to obtain the dynamic characteristics of a ramming mechanism, the multi-rigid-body and rigid-flexible coupling dynamic models were established in Recurdyn. Considering the structural characteristics of the link, the deformation motion of the link is described by using the Reissner-Mindlin plate theory, and the dynamic model of a bomb feeder is established considering the influence of non-ideal factors such as transmission clearance, friction and so on, the dynamic characteristics of a bullet-feeding machine are simulated and analyzed. Compared with the multi Rigid body dynamics model, the simulation results of the rigid flexible coupling model are more consistent with the experimental results, in addition to the polygon effect, other factors such as transmission gap and so on are also the causes of the fluctuation of the chain head speed of the ammunition conveyer. In addition, with the periodical movement of the Ammunition Conveyer, the wear between the pin holes of the connecting links will become more and more serious, and the closer you get to the end of the chain, the greater the trend

Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented.

To address the robust control problem of turbofan engine with slow actuator dynamics and uncertainties, we present a two degree-of-freedom (TDOF) μ synthesis control scheme. The actuators with slow dynamics and parameter uncertainties are considered in the control design. To handle the problem of rapid change of reference command, feed forward control (one freedom) is adopted, and feedback control (another freedom) is introduced to ensure good servo tracking and disturbance attenuation. The performance and the control output weighting functions are properly devised to guarantee servo tracking and disturbance attenuation performance of the devised controller. The D-K iterative algorithm is adopted to calculate the TDOF μ synthesis controller. A turbofan engine with slow actuator dynamics and parameter uncertainties is adopted for simulation. The effectiveness and superiority of the devised TDOF μ synthesis controller are verified by a series of simulation analyses. The simulation analyses show that the TDOF μ synthesis control scheme developed in this paper can handle the robust control problem of turbofan engines, despite the negative influence of the slow actuator dynamics and uncertainties. The comparison shows that the TDOF μ synthesis controller provides better performance than the PI controller and the LMI optimization gain scheduled controller do.

Visual question answering (VQA) requires both inferring and providing correct answers based on the semantics of the question and the content of the image. We propose a co-attention spatial reasoning model, it models the co-attention, and spatial reasoning attention for the VQA task. The model maps the question text information to image region to reason the fusion features, which contain fine-grained semantic information of two models. Meanwhile, it can obtain the spatial relationship between question words and image regions. The model was trained and verified on the VQA-v2 dataset, the best single model achieves an accuracy rate of 69.55% on the test-dev set.

As a main form of checking out learning and teaching effects, exam has traditionally been used in schools. Accurate and objective assessment of students' learning effects through examination performance is an important part of evaluation of students' education. This paper proposes a comprehensive evaluation model of evaluating examination performance based on quality of examination paper, explores the application of artificial neural network technology in students' comprehensive education evaluation, focusing on BP neural network, some works can also be introduced by Python. The process is to compose input vector of BP neural network using the four evaluating indicators of quality of examination paper, namely reliability, validity, difficulty and discrimination, and raw score, namely the students' score of examination paper. Take valuation namely the quantitative value of learning effect as the output vector of BP neural network. Design a reasonable network structure and training sample, put the training sample in the network for processing till until systematic error meets the specified requirements. By so, the obtained network model is the required comprehensive evaluation model of examination performance. At last, the paper analyzes the simulation of the evaluating model's feasibility using MATLAB software, obtains satisfying results, and proves the feasibility of the proposed network model.

Aiming at the missile longitudinal motion model with obvious aerodynamic nonlinearity, this paper proposes a two-timescale design method of missile autopilot based on suboptimal control. By dividing the system states into fast and slow states based on different timescales, the corresponding fast and slow control inputs are designed. This paper introduces suboptimal control and robust control to enable the system to have good dynamic response characteristics, and the system's ability to suppress various uncertainties is guaranteed. By applying the Lyapunov method, the stability conditions of the system are given. Finally, the simulation results show that the system has good dynamic characteristics and robustness.

Inconsistency among different acquisition devices severely affects the performance of palmprint recognition systems. When the same palm is enrolled by one capture device and verified by another device, the inconsistency between the two devices may lead to recognition failure. The installation of camera and the setting of lighting are two main reasons for inconsistency problem in palmprint recognition systems. In this article, we proposed a calibration approach for the inconsistency problem regarding palmprint acquisition devices. Our calibration approach includes image quality assessment, rectification and normalization. Experimental results demonstrate that the proposed approach is effective, decreasing the equal error rate (EER) of palmprint recognition from 0.653% to 0.627%.

The conventional Kalman filter demands complete knowledge of the actual filter model, which is usually inaccessible in practical problems. When dealing with the filtering problem with uncertainty, any improper prediction of the uncertainty may degrade the filtering performance and even lead to divergence phenomenon. In this paper, the innovation feedback Kalman filter, which introduces the innovation feedback controller to the Kalman filter equations, is newly proposed on the basis of automatic control theory to address the filtering problem with uncertainty and is different from other filtering methods. By studying the estimate error equations, the estimate bias is extracted and its propagation mechanism is formulated. The estimate bias propagation equations reveal that eliminating the estimate bias essentially equivalents to an output regulation problem with uncertain exosystem. And a concise yet effective innovation feedback controller is subsequently given as an example. The proposed method is applied to one-dimension target tracking scenario and its high estimation accuracy performance and good stability are simulated through a comparative analysis with the ideal filtering results.

Let G be a nontrivial edge-colored connected graph. A rainbow edge-cut is an edge-cut R of G, and all edges of R have different colors in G. For two different vertices u and v of G, a u-v-edge-cut is an edge-cut separating them. An edge-colored graph G is called strong rainbow disconnected if for every two distinct vertices u and v of G, there exists a both rainbow and minimum u-v-edge-cut in G, and such an edge-coloring is called a strong rainbow disconnection coloring (srd-coloring for short) of G. For a connected graph G, the strong rainbow disconnection number (srd-number for short) of G, denoted by srd(G), is the minimum number of colors required to make G strong rainbow disconnected. In this paper, we first characterize the graphs with m edges satisfing srd(G) = k for each k ∈ {1, 2, m}, respectively, and we also show that the srd-number of a nontrivial connected graph G is equal to the maximum srd-number in the blocks of G. Secondly, we study the srd-numbers for the complete k-partite graphs, k-edge-connected k-regular graphs and grid graphs. Finally, we prove that for a connected graph G, computing srd(G) is NP-hard. In particular, we prove that it is NP-complete to decide if srd(G) = 3 for a connected cubic graph. We also show that the following problem is NP-complete: given an edge-colored (with an unbounded number of colors) connected graph G, check whether the given coloring makes G strong rainbow disconnected.

We have proposed the SEFP (Same Entrance Full-Pass) method in our previous research work in order to avoid the congestion at the key spot in the regional road network. The SEFP method, which can make all vehicles going to the critical intersection pass the stop-line with no stop during each release phase, reducing the vehicle delays and stops greatly. While the green time is underused in this method and the vehicle throughput at the critical intersection can be further increased. On this basis, we propose the improved SEFP method, which can formulate signal offsets control schemes at the upstream intersections by means of traffic wave theory, guaranteeing all the vehicles leave the critical intersection at saturated flow speed. In the meantime, the closure control is adopted at upstream intersections timely in light of the queuing length on the critical intersection lanes, avoiding the spill-outs effectively. This new method can improve the traffic throughput of the critical intersection while decrease the vehicle delays and stops, preventing the critical intersection from traffic over-saturation effectively. The simulation results of an actual critical intersection in Mianyang city demonstrate the validity and feasibility of the improved SEFP method.

It is difficult to understand a complex software program if its detailed design is not available. A model-rebuilding approach is presented to recover the design on an abstract level. In this approach, by static analysis of the program, a design is constructed and described in an abstract program which includes all behaviour that is observable at a set of selected program points. The approach provides an effective way for the understanding of complex programs.

PSO algorithm, as one of the best swarm intelligence algorithms, has a strong optimization ability. It has obvious advantages to apply PSO algorithm to the optimization of resource scheduling strategy of cloud computing platform. Through the research on the application of PSO algorithm in cloud computing, effective methods are provided for the improvement of efficiency of cloud computing platform. This paper analyzes and studies the application of PSO algorithm optimized by cloud model in business scheduling of big data cloud storage platform.

The phase-shifting full bridge digital power supply based on Fuzzy PID control is difficult to realize the full mapping from the fuzzy universe to the actual output domain, resulting in the loss of fuzzy control quantity, resulting in the dynamic performance problems of large amplitude and long time output fluctuation. In this paper, a fuzzy adaptive PID control algorithm with improved quantification factor is proposed. The online adjustment function of quantification factor in fuzzy universe is introduced in the quantification link. The function can adjust the fuzzy quantification factor in real time according to the current sampling error and error change rate of the system. In order to verify the effectiveness of the improved algorithm, the traditional PID control, the traditional fuzzy PID control and the quantification factor improved fuzzy PID control are applied to the phase-shifting full bridge small signal disturbance model respectively. The results show that the fuzzy adaptive PID control algorithm with the quantification factor online adjustment function has higher dynamic performance.

Here are many challenges for target detection in the complex scenes. In the real scene, such as the diversity of the type, quantity and shape of the parts on the workpiece plates, the model construction and rapid detection need to deal with the complex and changeable scene, and the large-scale massive point cloud data challenges the operational efficiency and accuracy of visibility analysis technology. In this paper, the 3D modeling method based on computer graphics is proposed by using the displacement laser sensor as the digital representation of 3D complex scene on the scanning point cloud, which realizes the fast extraction of semantic target feature parameters under complex conditions. The multi-dimensional parameter-based saliency feature and fusion multiple filtering algorithm is built to obtain a fast and high-precision intelligent template recognition detection method. Based on SOC embedded engineering technology, the detection algorithm is realized by software programming. The two-dimensional mechanical motion platform is developed to test the workpiece plate applied to wheel hub of high-speed rail train, and the recognition accuracy is more than 99%.

The subsystem is k-out-of-n: G configuration, and the subsystem has component mixing (that is, subsystem components can be selected from different kinds of components), and the minimum number of normal working elements (k value) that can be selected as well as can change RAP problem. On the basis of giving a solution construction method and generating a new solution by symmetric bit transformation technology, an iterative DPSO (discrete particle swarm optimization) algorithm with fixed-compression coefficient and dynamic inertia weight is designed to solve the model. The calculate results indicate that the algorithm can effectively give the optimal solution for each test case (which is consistent with the existing optimal results in the literature). PSO algorithm can effectively solve the RAP problem of subsystems with mixed components and varying k values. When the size of subsystems (k-out-of-n: G) is large, and the solution of the model is limited by microcomputers.

Topological sonic crystals are artificial periodic structures that support robust edge or interface state. Most previous studies on interface state in one-dimensional system heavily rely on Su-Schrieffer-Heeger (SSH) model, which modulates inter and intra hopping strength to yield a nontrivial topological phase. Whether it is possible to achieve interface states by connecting two trivial phases remains a question. To this point, in this paper, we propose a novel method of breaking the inversion symmetry of diatomic and elaborate the underlying mechanism using a spring-mass model. Instead of modulating spring stiffness corresponding to hopping strength which is intrinsically requested in the SSH model, we perturb the masses to break inversion symmetry while springs remain unchanged. Although breaking inversion symmetry in diatomic does not lead to a nontrivial phase, it is found that the interface state would still emerge within the chain formed by connecting two different configurations. Subsequently, this mechanism is applied to a one-dimensional acoustic resonator system connecting two different configurations to realize interface state. Simulation results reveal that acoustic wave has strongly localized at predicted interface frequency. Our study provides a novel approach of producing interface states in the one-dimensional acoustic system.

In the paper, Ni45 coating was manufactured by laser cladding on 42CrMoA alloy steel using the 5 kW CO2 laser, four-axis CNC machines and the coaxial powder feeding system. It was investigated that the effects of nano-WC on microstructure and wear resistance of the Ni-based coating. The macromorphology and microstructures were analyzed by SEM, XRD and microhardness tester. The wear tests were conducted on the Friction and Wear tester. The microstructure was first refined and then coarsened as the increase of nano-WC addition. When the nano-WC addition is 3.0 wt.%, the coating showed the finest microstructure. The clad layers including γ-Ni, N3B, M23C6 and WC, W2C. Meanwhlie, the wear rates of coating were gradually decreased.

Since the rapid development of technology advancement and accelerated upgrading rate of mechanical, electrical, and electronic equipment, dealing with the recovery, recycling, and remanufacturing of end-of-life (EOL) products are of increasing importance. The disassembly line balancing problem (DLBP) aims at addressing such concern in the most efficient and effective manner. In this paper, a meta-heuristic approach involving both multi-criterion decision making (MCDM) and variable neighborhood search (VNS) is proposed to the U-shaped layout of the disassembly line. Following the MCDM method combining fuzzy set theory, grey relational analysis, and Choquet fuzzy integral, an improved VNS approach with a specially designed decoding method is utilized to balance a U-shaped line with three hierarchical objectives. Finally, computational experiments validate the effectiveness of the proposed approach.

For the scenario where the target can actively defend against the attacking missile, an optimal guidance law for the attacking missile that can simultaneously evade the defending missile and intercept the target is proposed. To increase lethality of the attacking missile, the terminal angle constraint is considered. Assuming that the defending missile and the target is using a known control strategy, the guidance law design problem for the attacking missile is formulated as an optimal control problem with inequality constraint. General solution of the problem is derived for arbitrary order dynamics of the adversaries. Then first-order dynamics of the three players are considered and an analytic solution is presented. Numerical simulations are carried out to demonstrate the high performance of the proposed guidance law.

In December 2019, China's research travel market plummeted as a result of the novel coronavirus epidemic. In the context of a depressed research market in the Information Age, the concept of "Internet + research" emerged. First, according to symbiosis theory, the symbiotic units of smart research travel are determined. Then, based on a structural design of smart research travel data system, key technologies in big data, cloud computing, and Internet of Things are described in detail. Finally, based on the traditional browser/server architecture model of computers, a smart research travel system architecture suitable for the epidemic situation is constructed. The purpose of this study is to build a new ecology of research travel, optimize resource allocation, and promote the healthy, stable, and sustainable development of research travel in the country.

For the flexible job shop scheduling problem with the goal of makespan, a mathematical model is established and a hybrid algorithm (GIWO) based on gray wolf and invasive weeds is proposed. In this paper, the intrusive weed optimization algorithm is embedded on the basis of the gray wolf algorithm to improve the algorithm's global search and local search ability. And an effective initial population generation mechanism is added to improve the quality of the initial solution. A variable neighborhood search structure that adaptively changes according to the search is proposed. Finally, other improved gray wolf algorithms are compared with the algorithm in this paper through different benchmark instances. Experimental results show that the proposed algorithm performs better than the other algorithms in effectiveness and superiority. The algorithm can effectively solve the flexible job shop scheduling problem.

Kanade-Lucas-Tomasi (KLT) optical flow algorithm based on the brightness constancy assumption is widely used in visual simultaneous localization and mapping (SLAM) and visual odometry (VO). However, the automatic adjustment of camera exposure time, the attenuation factor of sensor irradiance caused by vignetting, and the nonlinear camera response function will cause the same feature point to have different brightness values on different image frames, thus breaking this assumption. Hence, we propose a gain-adaptive KLT optical flow algorithm with online photometric calibration, and on this basis, design a monocular visual-inertial odometry which is insensitive to brightness changes. This method can calibrate the photometric parameters online in real time, meet the assumption of constant brightness in practical applications, and make the algorithm more robust and accurate in the case of dynamic changes in brightness. Experimental results on the TUM Mono and EuRoC datasets show that the proposed algorithm can reliably calibrate the photometric parameters of any video sequence and perform well in the environment with varying brightness.

It is difficult for a robot to capture object which is thrown out in the environment with natural wind. Equation of state and measurement of the object in the environment with natural wind is studied. Using the measurement equation and the state equation when there is no natural wind, the average acceleration of the object caused by natural wind can be estimated. Moreover, the variance of the object acceleration caused by natural wind can be estimated using the statistical characteristics and probability distribution of natural wind. Then a method for robot capturing the object based on Kalman filter is proposed. Simulation results show that the trajectory of the object changes obviously when two groups of typical natural wind are used. The object trajectory can be tracked by the robot accurately after it deviates from the object trajectory for about eight sampling periods.

To address the issue of the flexible joint (FJ) manipulator control with actuator saturation, singular perturbation method is adopted in this paper which decompose the manipulator model into two subsystems in terms of time scale transformation, the fast one represents flexibility and the slow one expresses rigidity, and the control law of which is designed respectively. For the fast subsystem, a velocity-difference-based feedback control is designed to lower the oscillation caused by joint flexibility, while for the slow subsystem, the control performance of the system is enhanced by a combination of a class of nonlinear integral sliding surface and backstepping global sliding mode. The radial basis function (RBF) neural networks are utilized to compensate the actuator saturation, the disturbance and the modeled uncertainties. Based on the Lyapunov theory, the tracking convergence of the closed-loop system is proved rigorously. The simulation result shows that the designed control law can maintain good rapidity and accuracy, moreover, chattering is eliminated.

Based on the theory of random response analysis, a road noise prediction method is proposed. A finite element model of acoustic-structure coupling for the full-vehicle was established, and the road surface texture measured in the actual vehicle test was converted into a power spectrum, which is the excitation of FE model. Under the conditions of coast down from 80km/h to 20km/h on Belgian pavement, the prediction of road noise in the 20- 200Hz frequency band was achieved. The driver's ear sound pressure obtained from the simulation is in good agreement with the test results, which verifies the effectiveness of the simulation model and prediction method. The sensitivity of the driver's ear-side sound pressure level to the stiffness of the suspension mounts was further analysed. The stiffness of the bushings that had a large impact was determined. The Response Surface Methodology was used to optimize road noise. The results show that the average sound pressure level near the driver's ear is reduced by 1.81 dBA. The simulation analysis and optimization methods introduced in this paper can provide a reference for the virtual development process of vehicle road noise.

The effect of energy distribution of turbulent vortices in atmosphere on the evolution of wake vortices is studied using the numerical simulation approach. Solution-Based Dynamic adaptive mesh method is applied to compute the wake vortex evolution using FLUENT. Two series of cases are designed to study the role of larger and small-scale vortices in the decay of wake vortex, respectively. The results show that energy distribution of different scale vortices has a significant effect on the onset time of vortex linking and the decay rate of wake vortices at the turbulent diffusion phase and fast decay phase.

System simulation technology is currently the mainstream technology for system design optimization. Parametric design can greatly improve the speed of model generation and modification by establishing various constraint relations. In this paper, the parameter design method is improved and combined with the system simulation technology, and the variable parameter and real-time co-simulation method is proposed. In order to verify the feasibility of the method in system dynamic performance analysis, this paper builds a real-time co-simulation platform based on AMESim, and applies it to the analysis process of train dynamic air consumption law. The results prove that the method is beneficial to reduce the workload of designers, and can be applied to the design optimization, analysis and tracking of complex systems.

In order to meet the requirements of high-precision target displacement measurement, this paper proposed a measurement method based on monocular vision measurement system by which the advantages of simple structure, convenient debugging, easy to install and calibrate is recognized. This method first uses the bundle adjustment algorithm with conditional constraints to optimize the solution results of the orthogonal iterative algorithm, then calculates the relative poses of the optimization results, finally establishes an error correction model to perform secondary optimization on the relative poses. The experimental results show that the method proposed in this paper can reduce more than 54% average error of displacement measurement results, and the error between the final displacement measurement results and the actual displacement values is controlled at 0.4mm level, which proves that the method has good practical value.

At present, researches on micro-course mainly focus on design, development or application, and rarely involve evaluation of micro-course, especially from the perspective of user experience. To this end, from the perspective of user experience, we developed a theoretical model of user experience evaluation of micro-course. Firstly, we introduced the concepts of user experience and micro-course. Secondly, we discussed various research methods, including literature research, expert consultation, questionnaire survey, analytic hierarchy process and other research methods. Finally, based on the above discussion, we developed the theoretical model of micro-course evaluation based on user experience, and then discussed the model in detail.

In robot golfing games, how to identify and track small balls is a critical step for scoring. Considering uneven illumination and intensity, irregular distribution, and blurred borders, an integrated detection method based on the random Hough transform and the Kalman filter is presented to improve the recognition accuracy, i.e., guaranteeing the detection accuracy and the stability in shooting. Experiment on the NAO robot is performed to show the effectiveness of the proposed detecting method. Both theoretical and experimental results suggest that the proposed recognition method can reduce the calculation time of the Hough transform and has a certain degree of robustness against uncertain environments.

In this paper, the emergence of the contradiction oneself of object u is not only attributed to a mechanism that opposite transmission between the function x(t) that carrying the location information of u and its returned back x⁻¹ (t⁻¹) and is meeting at a contradiction point, but also is conversed into the questions: "How long time will be taken for the function x(t) transports from own position to v with the velocity $\dot{x}\,({\rm{t}})$, and return back?" It is shown that not only a pair of dual categories and their presheaft and topos can be induced by x(t) and x⁻¹ (t⁻¹), but also a Möbius Entangled Polar Coordinate System can be established by an entangled pull back and a dual of mirror manifolds. In this way, a framework that runs through Philosophy, Information, Mathematics, Physic, Logic to Neural Network can be constructed too.

The grid has appreciable impact on the simulation of the wake vortex. There is a big difference between the traditional aerodynamic estimation grid and the grid used for capture the main parameters of the trailing vortex in the far field. This paper compares the similarities and differences between these two kinds of grids. The main parameters of the coarse grid are quite different from those of the refined grid simulation in the far field. Sufficient quantity grids are required to simulate the flow field in the parameters of wake vortex prediction. Otherwise, it may draw the reverse conclusions with the physical truth.

As the application field of robots expands from industry to agriculture, medical rehabilitation and service industries, its working environment becomes dynamic and not good. The end effectors of robots also need to be more flexible to adapt to changing operating objects. Based on the existing research of our group, a multi-finger dexterous hand included two kinds of bending joints is designed which used human hand as the prototype and combined with the characteristics of human hand joint movement. The EMG signal is proposed as the control source to improve the diversity and flexibility of dexterous hand movement. In the link of EMG signal processing algorithm, EMG signal feature value of the independent and complete action is discretized to form a continuous characteristic parameter curve. And multi-point continuous myoelectric control which is used to characterize the action process can be realized. The real-time adjustment of the driving air pressure is realized by the control system. And the system can control the different action forms of multi-finger dexterous hand. Finally, the relative error of proposed control algorithm and its system was verified to be about 3.23% by the joint position control test.

With the computer gradually become an essential part of People's Daily life. Long periods of sitting can lead to headaches, dizziness and cervical discomfort. In this paper, a manipulator massage system based on STM32F103 chip is designed to promote the intellectualization and automation of human cervical vertebra massage. Based on the study of human neck and shoulder massage points and massage techniques, the mechanical structure of the manipulator is designed, and SolidWorks is used to design the 3D model.

In this paper, a nonsingular terminal sliding mode control method is applied to design an attitude control law for flexible spacecraft. First, the Lagrange method is applied to establish the dynamic equation of the flexible spacecraft and the vibration equation of the flexible appendages. Then, by designing a nonlinear terminal sliding surface function, the spacecraft terminal sliding mode control law is designed. In order to solve the singular problem, a nonsingular terminal sliding mode control law is further designed. At the same time, the saturation function is introduced to replace the sign function to suppress chattering problem of flexible appendages. The Lyapunov method is used to prove that the involved nonsingular terminal sliding mode control law can realize the precise attitude control of the flexible spacecraft. Finally, a numerical simulation example verifies the effectiveness of the control law designed in this paper.

Based on aperiodic complementary sequence (ACS) sets and orthogonal matrices, the construction of Gaussian integer (GI) zero correlation zone (ZCZ) periodic complementary sequence (ZPCS) sets is proposed. The constructed GI ZPCS sets can achieve the theoretical bound, and the length of ZCZ can be chosen flexibly. The GI ZPCS sets obtained by this paper can be used in multi-carrier code division multiple access system to obtain higher spectrum efficiency and eliminate interference.

Due to the particular nature of ownership, the motivations for state-owned enterprises to participate in the mixed ownership M&A include not only the realization of corporate capital appreciation and value creation, but also some strategic factors at the national level, such as concentrating state-owned economic power to promote the development of the industry and maintaining stable social development. Based on the characteristics of the self-development of state-owned enterprises and the maximization of interests, this paper constructs the evaluation system of trust mechanism from five dimensions of shareholders', creditors', suppliers', buyers' and enterprises' sincerity; constructs the evaluation system of innovation mechanism from three dimensions of technological innovation ability, market competitiveness and sustainable development ability; and the evaluation system of the mixed ownership M&A value of state-owned enterprises from four dimensions of Tobin's Q, operating cash flow, return on total assets and corporate governance.

The recent pandemic of COVID-19 is reported as a pandemic and spreads globally. As known, COVID-19 is caused by SARS-CoV-2 and human ACE2 has been reported as the receptor of SARS-CoV-2. Nowadays, as reported, variants in the coronavirus can lead to the transmission across species, in turn, variants of ACE2 may affect the susceptibility of SARS-CoV-2. In this work, we collected and selected critical missense variants in ACE2. Thereafter, we predicted the changes of protein-protein binding affinity, corresponding to each missense variant. According to the results, thirteen variants in human ACE2 exhibit obvious differences. More specifically, six variants (D38E, M82I, Y83F, K353H, R357A, and R357S) in ACE2 are predicted to enhance its interaction with the coronavirus spike protein. In comparison, seven variants (S19P, K31D, Y41A, M82N, M82T, D355A, and D355N) are predicted to inhibit such kind interaction. Accordingly, the final findings of our work may provide evidence that the potential relationship between COVID-19 susceptibility and human ACE2 genetic variants.

As for the transmission of human to human, the recent pandemic of COVID-19 is now spreading among the population, which has already led to 1.86 million deaths. As recently reported, it is the SARS-CoV-2 that causes COVID-19. Furthermore, human ACE2 is the receptor of the coronavirus. Nowadays, it has been reported that missense variants in ACE2 may affect the coronavirus susceptibility. In this work, we explored the functional impacts of missense variants in human ACE2. Briefly, we initially collected the variant in human ACE2, which have been labeled as important sites of ACE2 or the critical binding sites with the spike protein. Thereafter, applying the technology of protein structure homology modeling, we constructed the molecular spatial structure models of the variants. Next, variants molecular models of ACE2 were superimposed over the wild type of ACE2, to observe the structural changes. As experimental results demonstrated, the overall structures of ACE2 variants are similar. However, several variants (i.e., G173S, V184A, I233F, D355N, R357A, R357S, and G575V) in ACE2 are predicted to decrease the stability of human ACE2 protein and/or to be harmful to human health. Accordingly, the final findings could also provide a functional and structural basis for the potential pathogenicity of ACE2-driven viral infections.

Under the premise of limited node energy, an efficient and energy-saving wireless sensor network routing algorithm was proposed to prolong the lifetime of overall network. By analysing the operation pattern of wireless sensor network, the energy model and transmission model was introduced, and BTPHS algorithm based on the above two was proposed. BTPHS algorithm divided the network lifetime into 3 periods. According to the characteristics of the energy distribution and energy consumption in different periods of the network, two new node selection strategies were proposed and a new objective function as a reference standard was provided. The adjustment process of the HS algorithm is abandoned, which makes less parameters in proposed routing algorithm. The experiment results indicate that BTPHS algorithm can prolong the lifetime of overall WSN network effectively.

The propeller excitation can be estimated by measuring the vibration response of the shafting, but there are many difficulties, such as difficult to implement, difficult to determine the boundary conditions and so on. For this reason, the waveguide model of different shaft segments on the shafting and the waveguide transfer relationship between the connected shaft segments are established based on the waveguide theory, and the waveguide coefficient is estimated combined with the vibration response observation data. The estimation of longitudinal oil film stiffness of thrust bearing and admittance of thrust bearing housing and hull connection structure is avoided, and the method of propeller longitudinal excitation based on waveguide transfer relation is verified by a specific example. The results show that the method is feasible and effective, and provides a new method for accurately grasping the actual propeller excitation.

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