Table of contents

Organized by Beijing Jiaotong University, the 2021 2nd International Symposium on Automation, Information and Computing (ISAIC 2021) was held successfully online from December 3rd-6th, 2021. The technical program of ISAIC 2021 comprised one plenary session with 10 plenary speeches (40 minutes for each including 3-5 minutes of Q&A), 9 parallel oral sessions including 15 invited speeches (25 minutes for each including 3-5 minutes of Q&A) and 73 online live presentations (15 minutes for each including 3-5 minutes of Q&A), 54 pre-recorded video presentations (15-20 minutes) and 13 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 2021 gathered over 197 participants from 28 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 2021 and contains 134 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 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: https://submission.isaic-conf.com/

• Number of submissions received: 458

• Number of submissions sent for review: 400

• Number of submissions accepted: 134

• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 29%

• 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

Examination questions classification according to Bloom's Taxonomy uses Natural Language Processing (NLP) approach, a series of text processing approach that generally can divided into the keywords identification stage and then the identified keywords classification to Bloom's Taxonomy levels stage. Since this NLP approach is a pipeline processes, the keywords identification stage's performance in term of accuracy is affecting the subsequent stage - the identified keywords classification and subsequently limits the final accuracy performance of the questions classification. The keywords identification stage's performance is mainly depending on the effectiveness of Part-Of-Speech (POS) tagging. Thus, this paper aims to identify the best performing POS tagger in keywords identification stage and enhance the tagger's performance with rule-based approach to achieve high accuracy performance and benefit the subsequent keyword classification and then the questions classification accuracy. The Perceptron tagger and the Stanford POS tagger are selected to be evaluated their performance in identifying the keywords of the randomly selected 200 examination questions from STEM subjects. This paper has observed the Stanford POS tagger is the best performing tagger in POS tagging with accuracy of 80.5%. Some rules are applied to the POS tagging to improve the accuracy further to 91.5%.

Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provides automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesizes that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes.

The paper considers the development of a prototype of a user identification system by face. The system uses face recognition technology built on base points. Neural networks and machine learning methods are used for development of this system. The system is designed using principal component analysis, support vector machine classifier and deep artificial neural networks. We have developed a user-friendly interface. The system has been verified on test images and has been shown sufficient accuracy for identifying people by their photos.

It is an enormous challenge for intelligent vehicles to avoid collision accidents at night because of the extremely poor light conditions. Thermal cameras can capture temperature map at night, even with no light sources and are ideal for collision detection in darkness. However, how to extract collision cues efficiently and effectively from the captured temperature map with limited computing resources is still a key issue to be solved. Recently, a bio-inspired neural network LGMD has been proposed for collision detection successfully, but for daytime and visible light. Whether it can be used for temperature-based collision detection or not remains unknown. In this study, we proposed an improved LGMD-based visual neural network for temperature-based collision detection at extreme light conditions. We show in this study that the insect inspired visual neural network can pick up the expanding temperature differences of approaching objects as long as the temperature difference against its background can be captured by a thermal sensor. Our results demonstrated that the proposed LGMD neural network can detect collisions swiftly based on the thermal modality in darkness; therefore, it can be a critical collision detection algorithm for autonomous vehicles driving at night to avoid fatal collisions with humans, animals, or other vehicles.

Assessing the right amount of water needs for a specific crop is a key task for farmers and agronomists to achieve efficient and optimal irrigation scheduling, and then an optimal crop yield. To this end, the reference evapotranspiration (ET₀) was developed. It represents the atmospheric evaporation demand, and therefore an important variable for irrigation management. In this regard, several methods such as the FAO's Penman-Monteith and Hargreaves have been used to model and estimate ET₀. These methods use climatic parameters data for calculation procedures such as solar net radiation (R_n), saturation vapour pressure(e_s), and min-max air temperatures or a combination of them. In this paper, we investigated two proposed data-driven methods to predict ET₀ values in a semi-arid region in Morocco. The first approach is based on forecasting techniques and the second one uses end-to-end modeling of ET₀ based on meteorological data and machine learning models. The feature selection and engineering results show that solar global radiation (R_g) and mean air temperature (T_a) have a significance of more than 87% as relevant predictors features for the ET₀. We then used them as input to machine learning regression models. Regression evaluation metrics showed that The XGboost regressor model performs well in both cross-validation with R²=0.93 in the first fold, and in hold-out validation with R²=0.92 and RMSE=0.55. As a final step, we compared the univariate time series forecasting of ET₀ using the Facebook Prophet model versus the machine learning modeling method that we proposed. As goodness-of-fit measures, forecasting using machine learning modeling of ET₀ showed better results in terms of both R² and RMSE.

Today, the detection of non-communicable animal diseases, the analysis of experimental data and the construction of a mathematical model is one of the main problems. In the article a method for determining the type of disease in cattle using a fuzzy inference rule algorithm is discussed. In our country, diseases with ketosis, microelementosis, ostradistraphia and secondary ostradithraphy in livestock, especially cattle, are determined by errors in determining the type of disease due to their similar symptoms. Since the symptoms of these diseases are interrelated and one of them can cause the other, solving this problem using fuzzy rule algorithms helps to reduce the error in determining the type of disease.

In recent years, models that integrate multimodal information to control robots have been actively developed. Memorizing and Associating Converted Multimodal Signal Architecture (MACMSA) was proposed to integrate multimodal information obtained from robots with Hopfield networks as associators and independent feed-forward neural networks as encoders and decoders. The performance of MACMSA has thus far been investigated only using pseudo-data. Notably, MACMSA exhibits high resistance to noise. However, it cannot generate signals for robot control. The purpose of this study was to improve MACMSA to generate signals for robot control and optimize it using real data on reaching tasks. The results of the generated control signals on a real machine are presented to demonstrate that the improved model can be effectively used in a real environment. The results also show that the proposed model can perform well with real data.

In textile/leather manufacturing environments, as in many other industrial contexts, quality inspection is an essential activity that is commonly performed by human operators. Error, fatigue, ergonomic issues, and related costs associated to this fashion of carrying out fabric validation are aspects concerning companies' strategists, whose mission includes to watch over the physical integrity of their employees, while aiming at enhanced quality control methods implementation towards profit maximization. Considering these challenges from a technical/scientific perspective, machine/deep learning approaches have been showing great skills in adapting a wide range of contexts and, in particular, industrial environments, complementing traditional computer vision methods with characteristics such as increased accuracy while dealing with image classification and segmentation problems, capacity for continuous learning from experts input and feedback, flexibility to easily scale training for new contextualization classes – unknown types of occurrences relevant to characterize a given problem –, among other advantages. The goal of crossing deep learning strategies with fabric inspection processes is pursued in this paper. After providing a brief but representative characterization of the targeted industrial context, in which, typically, fabric rolls of raw-material mats must be processed at a relatively low latency, an Automatic Optical Inspection (AOI) system architecture designed for such environments is revisited [1], for contextualization purposes. Afterwards, a set of deep learning-oriented training methods/processes is proposed in combination with neural networks built based on Xception architecture, towards the implementation of one of the components that integrate the aforementioned system, from which is expected the identification of presence/absence of defective textile/leather raw material at a low-latency. Several models powered by Xception were trained with different tunning parameters, resorting to datasets variations that were set up from raw images of leather, following different annotation strategies (meticulous and rough). The model that performed better reached 96% of accuracy.

Traditionally, computer vision solutions for detecting elements of interest (e.g., defects) are based on strict context-sensitive implementations to address contained problems with a set of well-defined conditions. On the other hand, several machine learning approaches have proven their generalization capacity, not only to improve classification continuously, but also to learn from new examples, based on a fundamental aspect: the separation of data from the algorithmic setup. The findings regarding backward-propagation and the progresses built upon graphical cards technologies boost the advances in machine learning towards a subfield known as deep learning that is becoming very popular among many industrial areas, due to its even greater robustness and flexibility to map and deal knowledge that is typically handled by humans, with, also, incredible scalability proneness. Fabric defect detection is one of the manual processes that has been progressively automatized resorting to the aforementioned approaches, as it is an essential process for quality control. The goal is manifold: reduce human error, fatigue, ergonomic issues and associated costs, while simultaneously improving the expeditiousness and preciseness of the involved tasks, with a direct impact on profit. Following such research line with a specific focus in the textile industry, this work aims to constitute a brief review of both defect types and Automated Optical Inspection (AOI) mostly based on machine learning techniques, which have been proving their effectiveness in identifying anomalies within the context of textile material analysis. The inclusion of Convolutional Neural Network (CNN) based on known architectures such as AlexNet or Visual Geometry Group (VGG16) on computerized defect analysis allowed to reach accuracies over 98%. A short discussion is also provided along with an analysis of the current state characterizing this field of intervention, as well as some future challenges.

Edge computing is an environment suitable for processing the workflow applications produced by the IoT devices to reduce time and energy consumption. The execution of the workflow task in the Cloud computing environment increases the consumption of both time and energy. In order to solve this issue, this paper proposes a new approach, namely, Decision making Regarding the offloading of A subset of the Workflow application (DRAW). In the DRAW approach, selecting the destination environment for executing the subset of the workflow application occurs in the Edge environment. The DRAW uses the genetic algorithm for offloading the subset to minimize the objective factors, including total execution time and energy consumption. It equally prioritizes both the objective factors for improving the execution of the subset in the Cloud environment using the improved genetic algorithm. The DRAW approach improves the genetic algorithm by removing its traditional limitations and produces an effective possible solution in terms of better offspring. Finally, the algorithm stops by attaining the best solution from the possible solutions. Thus, the implementation results show that the DRAW approach significantly outperforms the existing approach by minimizing execution time and energy consumption.

Breast cancer (BC) is a kind of malignant disease that represents the primary reason of women's death around the world, cancer cells form tumors which lead to weakening the functioning of the immune system. If the main risk factors are known and detected correctly, the cure rate becomes higher, and the inappropriate treatments which are the main cause of death will be avoided. Today, several avenues for advancing breast cancer classification research are being studied, in particular to strengthen screening and develop an early diagnosis plan. The purpose of this paper is to approach the unfolding of machine learning techniques in the clinical field to categorize and discriminate patients between malignant and benign groups. Modeling of cytological characteristics based on machine learning is proposed to improve predictive performance. In this work, three proposed algorithms of machine learning techniques have been used for the analyze and classification of Wisconsin breast cancer database, k Nearest Neighbors (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM). We will compare learning metrics of both, using train/test split and cross validation. The obtained results shows that KNN offers the best accuracy (97.07%), NB classifier (94.15 %) and SVM classifier (94.73%).

In the industrial control system of oil depot, the change of storage tank liquid level is closely related to the transportation process and site management of oil depot. It is of great significance to detect the abnormality of storage tank liquid level data for the safe and stable production of oil depot. A data-driven anomaly detection strategy is proposed by analyzing the non-periodic time series data of the oil storage tank liquid level. Based on the convolutional autoencoder algorithm to learn the features and patterns of a large number of samples, the strategy is carried out by reconstructing the samples and calculating the reconstruction error, which not only does not rely on the labeled samples, but also improves the detection precision. This paper chooses three algorithms of convolutional autoencoder, RNN (Recurrent Neural Network) autoencoder and LSTM (Long Short-Term memory) autoencoder for experimental analysis. Experiments were carried out on the oil tank historical data set and NAB (Numenta Anomaly Benchmark) simulation data set respectively. The results show that the accuracy of convolutional autoencoder is 98% and the F1 score is 82%, which is more practical for the scenes with real-time requirements.

DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.

Multi-layered networks have great advantages in portraying the multi-attributes of links and can describe complex real-life systems better. Link prediction and knowledge transfer in complex networks have been extensively studied, but link prediction and knowledge transfer on hierarchical networks are less of a concern. Based on the definition of hierarchical network, a random walk model including link prediction and knowledge transfer is proposed. The link prediction method is proposed from the structural similarity and knowledge compatibility, and then the knowledge transfer rules are proposed. This paper also proposes the evaluation indicators for link prediction and knowledge transfer. The experimental results by using real hierarchical networks show that the link prediction has obtained better results and the complexity has been reduced; the knowledge transfer efficiency has been improved. This study has important reference value for the development of multi-layer network theory.

With the development of artificial intelligence, computer vision has been widely applicated. The performance of these applications is closely related to the image quality, which is, however, affected by various lighting and weather conditions, such as rain and haze. The traditional methods focus on the dehazing problems under specific and idealize conditions, leading to the consequence that these methods can only be used in certain scenario on account of that indexes or hyperparameters need to be adapted according to the scene. This paper proposed an unsupervised learning algorithm for dehazing. We used the CycleGAN architecture to avoid difficulties when obtaining hazy and clear images in pairs. Besides, the identity loss was introduced for improving stability of the training procedure, and we used the reuse loss for sake of the steadiness of output hue. The experiments on public datasets such as RESIDE and I-HAZE show the effectiveness of the proposed method, which achieves comparable results to the state-of-the-art algorithms.

Recently, researchers have shown an increased interest in stock market prediction with neural networks. Stock market is affected by a multiplicity of factors with different active periods, thus financial time series possess multiscale frequency characteristics, which can be exploited to facilitate prediction of stock market. In this paper, we propose a stock market prediction model combining time-frequency analysis and convolutional neural network (CNN), in which the influence extent of different frequency components has been considered. We transform original financial time series into the spectrogram reflecting time-localized frequency information by short-time Fourier transform (STFT). The 2-dimensional time-frequency feature is obtained from the spectrogram by frequency bands extraction, which is then pre-weighted and input into CNN to forecast the future price change. The frequency bands extraction and pre-weight are set according to the frequency influence. The results of experiments on Shanghai Composite Index show that the proposed model with frequency bands extraction considering frequency influence achieves a 4% relative decrease in mean absolute error (MAE) compared with that does not consider the frequency influence. Moreover, the pre-weight gives an additional 3% relative decrease of MAE.

Multi-label image recognition is a practical task which aims to predict all concerned objects in an image, and the correlation between labels is considered as the key to solve multilabel problem. Previous approaches focus on exploring and exploiting the underlying relations between labels, by which the performance of most labels is improved significantly. But small object and label tail problems are ignored, and negatively influences the overall results. For ameliorating above problems, we propose a unified deep neural network named Attention-Based Dual-Branch Cascade Network (ADC Net), which contains Main Branch and Auxiliary Branch. ADC Net cascades to predict labels, and obtains the final result by element-wise adding. Attention modules in each branch contribute to recognizing small objects. Top-Down-Attention Module (TDAM) utilizes the preceding prediction map to guide the Auxiliary Branch. Besides, expanding training dataset is applied for learning the feature representation of tail labels. Our proposed methods are evaluated on two benchmark datasets: MS-COCO and VOC PASCAL 2007 datasets, and achieves state-of-the-art performance. Results of small objects, tail labels and visualization also prove the effectiveness of our method.

It is significant to automatically analyse video sequences for timely and accurate detection of abnormal pedestrian behaviour by using computer vision and analysis technology in public places. To realize the real-time monitoring of abnormal behaviour, this study uses a hybrid behaviour detection model based on the colour characteristics of the pixels and the gradient characteristics of the pixels mathematically. According to the hybrid behaviour detection model, the criterion of abnormal behaviour of the same pedestrian based on two frames of video images is proposed. This is conducive to realizing early awareness of emergencies caused by abnormal behaviours and taking early warning measures, reducing casualties, and maintaining public safety and social stability.

In order to solve the problem of the evaporation duct height (EDH) estimation, focusing on the propagation loss (PL) features with different EDHs, this paper proposes an evaporation duct height estimation method based on Deep Neural Networks (DNN). The theoretical basis is Universal Approximation Theorem. DNN computes the EDH value with the high-dimensional features and posterior probability of PL. A large amount of PL simulation datasets with EDH=15-25m are used for model training, and datasets with EDH=25-35m are used to verify the ability of the network. Experiments show that the model in this paper has good feature extraction capabilities for simulation datasets. Compared with the naive Bayes algorithm, the accuracy and anti-interference ability of DNN are greatly improved.

In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level.

With the booming development of modern electronic technology, artificial intelligence in the military field is showing a fast-rising trend, but at the same time, it is also facing the dilemma of a serious shortage of data. To address the current problems of low number and unbalanced categories of penetration multi-layer overload signals, a TransGAN-based penetration multi-layer overload signal generation method is proposed in combination with the fruitful neural network model of deep learning. Firstly, the dataset is built using overload signals that have been tested in practice at the range. Secondly, generator and discriminator are constructed. Generator borrowing the model structure of TransGAN, which consists of Transformer Encoder for learning the feature mapping of the overload dataset, and discriminator using a simple attention mechanism to reduce the complexity of the model. Finally, the generator and discriminator are trained and optimized using a generative adversarial network to achieve the penetration multilayer overload signal. Finally, the generative adversarial network is utilized to train and optimize the generator and discriminator to achieve penetration multilayer overload data generation. Experimental results show that the method can generate effective overload data with a different number of layers, which can address the issue of the lack of penetration multilayer overload signals to a certain extent.

Medically speaking, moderate fatigue is the embodiment of workload, and excessive fatigue will cause adverse effects on the human body. Therefore, real-time monitoring of human fatigue is of great significance. In order to detect fatigue in real time and conveniently, a speech fatigue detection method based on deep learning is proposed in this paper. The main work of this paper is to screen the useful speech segments of the fatigue corpus created in the previous work; Secondly, the speech signal is preprocessed and the analog speech signal is digitized; Thirdly, deep learning can effectively reflect the phonetic features of fatigue information; Finally, the fatigue degree is classified and recognized by classifier. The best classification and recognition rate of speech fatigue detection based on BLSTM network recommended by the author can reach 92.7%, which realizes the effective recognition of sports fatigue.

Human emotion judgments usually receive information from multiple modalities such as language, audio, as well as facial expressions and gestures. Because different modalities are represented differently, multimodal data exhibit redundancy and complementarity, so a reasonable multimodal fusion approach is essential to improve the accuracy of sentiment analysis. Inspired by the Crossmodal Transformer for multimodal data fusion in the MulT (Multimodal Transformer) model, this paper adds the Crossmodal transformer for modal enhancement of different modal data in the fusion part of the MISA (Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis) model, and proposes three MISA-CT models. Tested on two publicly available multimodal sentiment analysis datasets MOSI and MOSEI, the experimental results of the models outperformed the original MISA model.

In the field of human pose estimation, most of the existing methods focus on improving the generalization performance of the model, while ignoring the significant efficiency issues. This leads to an increasing amount of model parameters and needs to take up more and more computing resources, which greatly reduces the practical value of the model. In order to solve this problem, we propose a novel lightweight network structure called Effective and Lightweight Pose Network (ELPN). At the same time, for the sake of alleviating the difficulty of lightweight model training, we propose a Multi-Angle Pose Distillation (MAPD) model training method that can more effectively train particularly small pose network models. In quantitative experiments, our models have excellent performance on two mainstream benchmark datasets: the MPII and the COCO. In qualitative testing, our models can accurately locate the keypoints of complex human movements. These fully demonstrates the efficiency and effectiveness of our methods. Our models have the characteristics of high precision, small size and fast inference speed. It is a cost-effective model with greater practical value.

Investigating the dynamic characteristic of hydrological processes is of vital significance for environmental protection. In this study, the stepwise cluster analysis (SCA) method was used for monthly streamflow simulation in a hypothetical case. According to SCA, a cluster tree was formulated through training the data of monthly temperature, precipitation and streamflow from 2004 to 2010. Then, the generated tree was used to reproduce monthly streamflow in calibration period (i.e., 2004-2010) and validation period (i.e., 2011-2013). A comparison of SCA and multiple linear regression (MLR) was conducted to reflect the complex relationship of meteorological parameters (e.g., precipitation) and hydrological parameters. Model performance was assessed using Nash-Sutcliffe efficiencies (NSE), the determination coefficient (R²), the root-mean-square error (RMSE) and the mean absolute error (MAE). NSE and R² obtained from SCA are higher than that obtained from MLR. RMSE and MAE obtained from SCA are smaller than that obtained from MLR, indicating a better coincidence between simulated streamflow and the observed values in SCA. Results indicated that SCA has advantage in revealing the nonlinear relationship among precipitation, temperature and streamflow.

The precise coagulation add-in in the wastewater process treatment is key for efficient contamination removal. However, the complexity of the coagulant chemical theory and affected by many factors (turbidity, pH, conductivity, flow rate, etc.) that it is difficult to determine the optimal dosage. The traditional method in the production process, such as PID controller had a bad adaptability on the complex systems and high performance required systems due to its inefficient parameter coordination, and it has a large time delay, difficult to achieve precise control. Excessive dosage will lead to waste and cost-waste, insufficient dosage could not guarantee the quality of effluent water. In this research study, we proposed an intelligent precisely dosing prediction algorithm based on LightGBM, using the characteristics of the influent water quality parameters PH, turbidity, electrical conductivity and flow rate to predict the dosage of coagulant. Perform experiments based on the actual data collected from the sewage treatment plant. Compared to experimental results with the optimal dosage solution, it demonstrated that the proposed approach could predict the dosage more accurate, resulting in intelligent and precise dosing add-in in water treatment process.

Since the discovery of the laser more than 60 years ago, research has begun on its application in various fields of technology. One of the most commonly used applications of laser technology is the cutting and marking of various materials. And while the use of different metals has been studied and used much better, the use of lasers in non-metallic materials has been studied much less. Research has been conducted with a CO₂ laser on various materials -various types of textiles, felt, transparent and opaque Plexiglas and other materials that have wide application in technology and economics. The optimal parameters for performing various operations on this type of materials have been found and are shown in tabular and graphical form. Studies with different power and speed of the laser beam have been made. Based on the obtained results, the relevant conclusions are shown. These materials are widely used in industry, the manufacture of automobiles, aircraft, ships and other vehicles and machinery and the results prove the effectiveness of laser cutting and marking compared to the methods used so far to perform these operations.

The article presents a method of holographic research of non-stationary deformations of a surface by method of two exposures, one of which is carried out in its static state, the other in motion. The derivation of formulas for quantitative analysis of the obtained interferograms for a number of typical cases of surface motion is given, as well as a general algorithm for determining the displacements from the interferogram. The features and results of approbation of the method are given. It is shown that the method is very broad in the application of holographic interferometry. Its application makes it possible to obtain highly informative qualitative and quantitative data on the field of unstable displacements of the test element at any moment of its deformation, using for this purpose low-power continuous-wave lasers (instead of expensive high-power sources) and, accordingly, relatively inexpensive and compact holographic measuring devices.

This study aims at finding the most advantageous algorithm for dynamic simulation of the propagation of electromagnetic waves inside buildings. The method is based on fast estimation of Wi-Fi signal strength when passing obstacle within the building and the modified Smart3D algorithm. A software application for dynamic simulation of the best wireless network coverage under restrictive conditions was developed in Autodesk® Revit® environment. The application calculates Wi-Fi signal attenuation of buildings, estimates position of signal sources to get the best network coverage.

Mathematical and computational modeling has been a great ally in the development of new drugs. It helps by providing preliminary results that ultimately guide the path that the tests should take. Thus, the objective of this work was to develop the PharmaLab computational tool, which simulates the pharmacological action in the mouse ventricular myocyte using the mathematical model of Mullins & Bondarenko (2013). Having as one of the main differentials the integration with a model for the force of contraction, at PharmaLab it is possible to perform pharmacological tests by changing parameters of the mathematical model and registering drugs to be used in the simulations. The computational tool has its own interface for plotting results and support material for educational use. As a demonstration of the tool, a validation was performed, comparing simulations with experimental data for the drug Niferidil and two "in silico" tests: SS-68 correcting the "Catecholaminergic Polymorphic Ventricular Tachycardia" (CPVT) and Niferidil correcting the Short QT Syndrome (SQTS). The drugs showed good results in the correction of arrhythmias. Finally, it can be concluded that the PharmaLab computational tool has resources for use in both research and teaching.

Currently, the industry is linked to technological advances, and the maintenance area plays an important role in this regard. Good practices are being refined, and predictive techniques have been implemented within the maintenance plan. These techniques allow decisions to be based on made equipment condition, which optimizes costs. One of these techniques is the mechanical vibrations analysis, applied commonly rotating equipment, where is common the situation of expensive measuring equipment. For this reason, this report provides a problem basic solution, through the economical measuring equipment design for industrial applications.

A Physics Concept Space is constructed using Fischer's Thought Space, a general thought space that explains how individuals continually construct thoughts and modify them through conscious actions on events (Aes) in Physical Sociocultural Environments (PSCEs) from birth throughout the life span. Thought Developmental Equations (TDE) that operate on the physics concept space take two physics concepts and construct new concepts using the operations augmented-intercoordination (⊗), compounding (⊕), and shift of focus (<>). Diffisimliar metric d_p, a measure for the number of similar, different, and non-diffesimilable properties and an individual's comprehension of thoughts at a given time, reveals the development and connectedness of the concept space through the life span. At any given time, only the part of the space associated with actions (Aes) is active while they are being performed. No two concept spaces activated simultaneously or at two different times are the same, but the construction mechanism is the same. Granott's transition process and the d_p explain the underlying cognitive process. Furthermore, several properties of physics concept space such as near and far connectedness, continuum space, bases, subspaces, multiplicity, product spaces, and dynamic nature, along with the skills required to construct physics space, are discussed. The properties of the space show that it is a highly connected space, supporting a tenet of the epistemology of physics.

Microorganisms often move through heterogeneous fluid medium composed of multiple materials with very different properties. Biological locomotions are significantly influenced by the physical compositions and rheology of the fluidic environment. Some micro-swimmers are able to exploit nearby deformable interfaces to enhance their speed. Through computational simulations, we investigate the movement of a finite-length undulatory swimmer near interfaces within a viscous two-fluid media. Our results show that significant speed-ups can be obtained only if the active swimmer has a large body elasticity.

This research presents the study of heat release in the iso-butanol ignition process at temperatures T < 900 K, applying low-temperature chemistry and starting from a proposed kinetic scheme of 12 elementary reaction stages. A numerical analysis of the thermal energy release available for ignition is developed, where the formation of aldehydes is important in this combustion phenomenon. The low temperature kinetics for this alcohol were found to be unable to maintain the reactivity of the system. The OHs generated by low temperature chemistry react mainly to produce iso-butanal aldehyde instead of consuming the main fuel which inhibits NTC (Negative Temperature Coefficient) behaviour. To maintain the reactivity of the system, the reaction pathways of hydrogen peroxide H₂O₂ (HO₂→H₂O₂→OH) are added, obtaining a short kinetic mechanism of 14 reactions that generates a good fit for the experimental developments of ignition time at lower temperatures. of 1000 K.

An integral method for solving the problem of longitudinal flow of an inclined flat plate located in a stream of air flow is presented. As a result of the integration of the equations of motion over the thickness of the boundary layer, the two-dimensional problem becomes one-dimensional. The problem was solved numerically in the Mathcad software package. The graphs of the dependence of the thickness of the boundary layer and friction on the longitudinal coordinate at different angles of inclination of the plate are presented. The flow mathematical model can be used to determine the characteristics of two-dimensional boundary layers with arbitrary boundary conditions.

Much research has been devoted to bee overwintering because it is the most dangerous period of the bee family's life. The critical period for bees is the end of winter and the beginning of spring. During this time, there are large fluctuations in air temperature, and bees are forced to change their state from passive to active. By the end of winter, the club rises to the top of the hive, and the honey supply decreases significantly. Thus, this article considers a model that takes these changes into account. According to the results of the simulation, it was found that the movement of the bee club and the decrease of honey reserves in winter increase the fluctuations of the air temperature inside the hive. This affects the internal temperature regime of the bee club. At the same time, the stock of heat energy in the honey decreases and smoothes temperature fluctuations within the bee aggregation less. It is proposed to add a hive weight strain gauge to the previously developed electric bee heating control circuit, which will allow to correct the PWM output voltage coming to the heaters.

The paper considers the problem of determining properties of an underwater moving source by analyzing the perturbation that it creates in electromagnetic or hydrodynamic fields. The computer program has been developed to simulate the spatial propagation of gravitational waves from the mass source moving along an arbitrary trajectory in a stratified fluid. The calculation results are in good agreement with analytical results obtained in the far-field approximation and with the results of experiments on the flow around underwater obstacles, moreover, the proposed technique allows to simulate any arbitrary motion of the source. Two new approaches to solving the inverse problem of determining the characteristics of the source of disturbances are proposed. The first one is based on the analysis of the signal received by radio-sensors, that scan the surface of the ocean. The second one uses data obtained from sensors installed directly in the water column.

In this work, a method for finding nonlinear heat-conducting characteristics of soil is developed. Two-layer complexes of containers were created, the side faces of which are thermally insulated, so the 1D thermal conductivity equation is used. A temperature sensor is placed at the junction of the two media, and a mixed boundary value problem is solved in each area. In order to provide the inverse coefficient problem with initial data, two temperature sensors are used: one sensor was placed at the open border of the container and recorded the soil temperature at this border, and the second sensor was placed at a short distance from the border, which recorded the air temperature. The measurements were carried out in the time interval (0,4t_max). First, the initial-boundary value problem of thermal conductivity with nonlinear coefficients is investigated by the finite difference method. Two types of difference schemes are constructed: linearized and nonlinear. The linearized difference scheme is implemented numerically by the scalar Thomas method, and the nonlinear difference problem is solved by the Newton method. The solution of the linearized difference problem was taken as the initial approximation of the Newton method. To find the thermophysical parameters, the corresponding functional is minimized using the gradient descent method. In addition, all thermophysical characteristics (8 coefficients) were found for a two-layer container with sand and chernozem.

In this paper, we will explain to you how to procedural generate realistic aggregates. The program requires two or more 3D models, and the number of elements and friction values. There are two steps: the first requires user-input elements and a provided shape and fills the defined shape with non-overlapping elements; the second, together with user-input friction data, is a physical simulation to generate a more realistic pile.

The paper develops methods for finding the thermophysical parameters of a two-layer soil. The difference scheme for the equation of quasi-linear thermal conductivity is taken as the basis for the study. Two-layer complexes of containers have been created, the side faces of which are thermally insulated. Measurement work was carried out to obtain values at the two end borders, the environment and at the contact boundary of the two containers. This circumstance makes it possible to solve the inverse coefficient problem in each container independently of each other. We have developed methods for finding all the thermophysical parameters of the soils in both containers. A rational method of choosing the damping coefficient is also proposed, which provides an indicative rate of convergence of the approximate value of the functional to zero. Computational experiments were carried out on the basis of the developed methods and measured data. The results of which show the viability of the developed iterative methods.

Upcoming exascale applications could introduce significant data management challenges due to their large sizes, dynamic work distribution, and involvement of accelerators such as graphical processing units, GPUs. In this work, we explore the performance of reading and writing operations involving one such scientific application on two different supercomputers. Our tests showed that the Adaptable Input and Output System, ADIOS, was able to achieve speeds over 1TB/s, a significant fraction of the peak I/O performance on Summit. We also demonstrated the querying functionality in ADIOS could effectively support common selective data analysis operations, such as conditional histograms. In tests, this query mechanism was able to reduce the execution time by a factor of five. More importantly, ADIOS data management framework allows us to achieve these performance improvements with only a minimal amount of coding effort.

This study investigates the influence of residual stresses induced by the manufacturing process, on the fatigue life prediction of micro gas turbine blades manufactured by laser metal deposition additive manufacturing technique and heat-treated at different heat-treatment parameters. Finite element modelling, using commercial software Abaqus CAE ® and FE-Safe ®, was used to simulate the turbine blade and disk assembly's operations and analyze induced stresses, as well as estimate the life cycle of the assembly. Results show internal stress build-ups, up to 400 MPa in magnitude can be induced in engineered components right from the point of additive manufacture, the effects of which can become amplified to as much as double that scale or more by the time the component is in operation. As much as between 75% to 300% fatigue life prediction error reduction can hence be attained by simply accounting for induced stress contribution of the process of its manufacture, rendering cost-saving condition-based monitoring and maintenance/overhaul decision making options.

The development and integration of an electroencephalographic signal acquisition and stimulation system is proposed to support the detection of pathologies related to the optic nerve. It is based on the extraction and analysis of a characteristic waveform called Visual Evoked Potential immersed in the electroencephalographic register. Some parameters of this potential, as latency and amplitude, are affected by the optic nerve pathologies, and measurement of changes from its normal states can be used as indicators of abnormal visual functionality of a patient. The proposed stimulation system can generate Visual Evoked Potential and multifocal Visual Evoked Potential which can help to locate pathologies in different areas of the visual field.

The response surface method (RSM) model as a general metamodel has already widely been applied to many practical problems. However, the majority of studies only focus on the 2nd-order terms RSM model instead of how to determine the high-order terms. This paper introduced a novel progressive response surface method (NPRSM) combining optimal Latin hypercube design (OLHD), moving least square method (MLSM) and statistical test. This NPRSM model can decide the high-order terms by progressive procedure. Then three numerical function can be proposed to validate the accuracy and the approximated performance of the NPRSM model.

When gas insulated metal enclosed switchgear and controlgear (GIS) has internal discharge, some faults are difficult to find during inspections due to the enclosed metal casing. This paper introduces the event of the floating potential discharge inside the GIS equipment of a 500kV substation found by GIS on-line monitoring system. Through on-site ultra-high frequency detection and precise positioning of the oscilloscope to determine the position of the discharge source at a certain switch, combined with the disassembly of the equipment, it is determined that the equipment defect is the discharge of the floating electrode of the switch fork.The results show that the on-line monitoring of GIS partial discharge has an important reference role in discovering the internal discharge of equipment during the test interval, and the monitoring and maintenance of GIS partial discharge on-line monitoring system should be strengthened in daily work.

In order to study the safy of secondary cables in substation, the influences of soil resistivity, short circuit current, distance, grounding grid parameters and cable parameter on the core-to-sheath potential difference and shielding layer current are investigated. It is shown that the core-to-sheath potential difference and shielding layer current change little with soil resistivity, and increase linearly with the increase of short-circuit current, and decrease with the increase of distance. The core-to-sheath potential difference increases with the increase of side length of grounding grid and cable length.

Secondary cable is an important equipment for ubiquitous power Internet of things to transmit information. When the substation grounding grid is struck by lightning, part of the lightning current flows through the shielding layer of secondary cable and produces core-to-sheath potential difference, which seriously threatens the safety of the secondary system. Fourier transform was used to decompose the lightning current in frequency domain, and the core-to-sheath potential difference at the end of the secondary cable was obtained by calculating the shielding layer current and transfer impedance at each frequency component. The effects of soil resistivity, lightning current waveform, distance between lightning stroke point and cable and grounding grid on the core-to-skin potential difference of secondary cable were studied. It is shown that the core-to-sheath potential difference of secondary cable increases with the increase of soil resistivity, the peak value, and the tail time of lightning current. The core-to-sheath potential difference decreases with the increase of the distance, while the core-to-sheath potential difference increases with the increase of conductor spacing of the grounding grid. The core-to-sheath potential difference with copper grounding grid is smaller than that with steel grounding grid.

Ensuring the operation safety of secondary cable is an important part of substation grounding system design. In some cases with high soil resistivity and large grounding grid, the safety of secondary cable can not be guaranteed by optimizing the design of grounding grid. This paper compares the core-to-skin potential difference and the shielding layer current of secondary cable with or without drainage line. It is shown that the drainage lines, which are laid in parallel with the secondary cable, can significantly reduce the core-to-skin potential difference and shielding layer current of secondary cable during power frequency short-circuit and lightning stroke. When the radius of the drainage line reaches 5mm, core-to-skin potential difference and shielding layer current of secondary cable will be reduced to 1/3 of those when it is not laid.

At present, most studies focused on the formation and accumulation of space charge under DC stress, relatively few studies focused on dynamic characteristics of space charge under AC stress. However, space charge dynamics under AC stress is of significance because the majorities of polymer insulation systems are subjected to AC voltage. In order to study the effect of charge injection mechanism from the electrodes on dynamic characteristics of space charge under AC stress, bipolar charge transport model are used to simulate the current density, electroluminescence intensity, space charge densities in polyethylene with different charge injection mechanisms. It is found that the simulated electroluminescence intensity and space charge densities under AC stress assuming the exponential functional approximation injection is more reasonable than those assuming the Schottky injection.

Charge packet phenomenon is a sign of the impending breakdown of the dielectric materials. In order to discuss the formation mechanism of charge packets, space charge characteriscs in polyethylene under the electric field of 150kV/mm are simulated based on the bipolar transport model assuming hysteresis in the barrier height for holes. It is found that if hysteresis in the barrier height is not introduced, net charge densities in the vicinity of the electrodes increase with the increase of polarization time. However, charge packets could not be reproduced without hysteresis hypothesis. When hysteresis loop in the barrier height are incorporated, charge packets are generated and propagated with the velocity about 3μm/s. The dying away of charge packets is not observed, which disagrees with the experimental data.

Micro-channel heat exchanger is a popular device for the electronic cooling. Heat transfer of flow boiling in micro-channel is a research hotspot. For the convenience of the theoretical research on micro-channel, a high efficiency simulation model on flow boiling and heat transfer of micro-channel was established. In the simulation research, the influence rules of operating conditions and thermal properties of refrigerants (working fluids) on the flow boiling were studied; the distribution rules along flow direction of characteristic parameters such as vapor quality, Heat Transfer Coefficient (HTC), pressure drop and bottom wall temperature of micro-channel T_w were analyzed; the key factors on the distribution of characteristic parameters were proposed. The characteristic parameters based on the refrigerants R134a and R1234ze(E) in micro-channel were compared and researched. According to the simulation results, HTC and pressure drop of R134a are larger and smaller than those of R1234ze(E) respectively, the uniformities of HTC and T_w of R134a are all better than those of R1234ze(E) respectively.

After power system short-circuit fault occurs, there is a large amount of residual magnetism in current transformer core, which will lead to wrong operation of connected relay protection and affect its reliability. Based on the direct current method, this paper puts forward two kinds of current transformer residual flux suppression strategies, which are constant voltage variable frequency (CVVF) and variable voltage constant frequency (VVCF), the magnetic flux of the core is gradually reduced by controlling the polarity of the output voltage of the switch module. The principle of residual flux suppression is introduced, and the method of parameter selection is given. The suppression effect of two residual flux suppression strategy is simulated and analyzed by experiment simulation. The results show that the two residual flux suppression strategies can demagnetize the current transformer core during automatic reclosing, but the constant voltage CVVF residual flux suppression strategy is simpler and more efficient.

In recent years, rare plants on the earth have gradually disappeared, so it is urgent to inspect and protect their health. A rare tree monitoring system compatible with NB-IOT (Narrow Band Internet of Things) is designed based on the low-power data transmission technology of LoRa (Long Range Radio). The system takes stm32f103c8t6 as the core of the controller, collects information through temperature and humidity sensor, attitude sensor, infrared pyroelectric sensor and sound sensor, builds a server to store the information and data back to the server, uses the terminal to access the server, and reminds the operator when the amount of information exceeds a certain threshold, Finally, the real-time monitoring of rare trees in the field is realized.

Hoisting at night has low visibility and high operational complexity, which has the problems of large cognitive load and high risk. With the help of digital twin (DT) and mixed reality (MR), the article designs and implements a MR night hoisting assistance system driven by DT. The system is developed on the Unity3D platform and based on the UNet networking communication mechanism. It realizes the functions of fusion highlighting, motion synchronization, multi-terminal collaboration and error display. It also has multiple interaction methods such as voice interaction, gesture recognition, and sight tracking. The research results have positive significance for improving the effect of night hoisting.

In this paper, a lever structure is proposed to enlarge the range of inertances of existing semiactive inerters. A semiactive inerter with the lever structure would be called lever-based semiactive inerter. The transmission ratio of the lever-based semiactive inerter can be changed by setting a different ratio of the arm of force of the lever, and then the range of inertance of the lever-based semiactive inerter can be enlarged. This paper also presents the mechanical model of the proposed lever-based semiactive inerter. The lever-based semiactive inerter is applied to an adaptive tuned vibration absorber. The frequency-tracker-based control algorithm is derived to maximize vibration reduction of the absorber. It can be shown that the vibration absorber with the lever-based semiactive inerter can adapt to a broader range of frequencies of vibration compared with that of the classical semiactive inerter.

This article discusses and verifies the accessibility and safety of internal spherical parts in A-C double turntable five-axis machine tools. The inner spherical surface can be machined completely by controlling the tool axis vector. The theoretical calculations determine the safe height range of the fixture to avoid collision between the tool and the workpiece and the Y-axis overrange. The correctness and reliability of the calculation are verified by machining simulation and actual machining of typical inner spherical parts.

Reliability modelling for digital instrumentation and control systems in nuclear power plants is one of the new trends. The containment spray system in the digital I&C system of nuclear power plant is selected, and the generalized stochastic Petri net model is used to model the reliability of the containment spray system. A single-cycle simulation of the generalized stochastic Petri net model is carried out under consideration of a variety of dynamic events, and the probability of system start-up failure is obtained. The results show that the generalized stochastic Petri net model can well reflect the impact of various dynamic events on system security. Therefore, the generalized stochastic Petri net model is suitable for reliability modelling of I&C systems in nuclear power plants.

To resolve the problem that the conventional direction relation matrix (DRM) models are not sensitive to the location characteristics of the target object, a DRM model that takes into account the location characteristics is proposed. Based on spatial cognition, the concept of taking the direction field of the target into consideration is provided, and a probability density function of the direction field is established. The cumulative probability of the region where the target object is located is used to replace the area in the conventional DRM model, and two methods are given for cumulative probability calculation. The cumulative probability is used to assign values to the direction relationship matrix. Experimental results show that, compared with the conventional DRM model, when the target is present in multiple regions, the method proposed in this paper leads to results that are more consistent with human intuitive experience and describes the direction accurately. When the target is only present in a certain fixed region, the proposed method has the same performance as the conventional DRM model. Therefore, the method proposed in this paper alleviates to a certain extent the problem that the conventional DRM models are not sensitive to location characteristics.

As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners' English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746.

Abnormal behaviors of pedestrians in crowd gathering public places are important factors affecting the stability of crowd flow. Pedestrian abnormal postures are important manifestation of abnormal behaviors, which often leads to local turbulence, disturbance and density-speed fluctuations. It is urgent to discover the disturbance mechanism of abnormal pedestrian posture on the stability of crowd flow. This study intends to establish machine vision, kinematics, dynamic models and crowd confluence dynamic models for typical abnormal pedestrian postures in public places. We mainly use computer vision related technology based to recognize abnormal postures of pedestrians in videos, constructs a network matrix of key posture nodes, and studys the kinematics characteristics of abnormal posture nodes. Considering the number of pedestrians and the characteristics of the architectural scenes, we design a workflow to select the appropriate macro or micro dynamic model to build the crowd flow model. To validate the propuesd model, case in Shanghai Hongqiao railway station is studied.

Attribute reduction is a common technique and has made breakthroughs in many aspects. One of the major development directions of attribute reduction is the extended fuzzy rough set models, which is embodied in the selection of fuzzy similarity relations and operators, eventually the derived membership functions. In view of the relatively simple selection of implication operators in related research, this article discusses the impact of different implication operators in the fuzzy rough sets. Secondly, the rough set model that relies on Lukasiewicz implication operator is further improved, and the proof of the closure of the new operator on the positive field is given. Finally, a new algorithm is given, and experiments are designed to prove the feasibility of the new algorithm based on eight public data sets.

To decrease end-to-end delay for streaming media over internet, the transmission method based on best-effort model is always employed in Real Time Communication (RTC) system. However, the packet-loss, jitter and congestion occur frequently because the quality of transmission cannot be guaranteed, which causes lag and splash of video. Therefore, an optimized hybrid Automatic Repeat Query (ARQ) solution with adaptive receiving buffer duration is proposed to guarantee the quality of video transmission. In the method, the loss rate was used to adjust the buffer duration and calculate the redundancy for Forward Error Correction (FEC). The results show that the method in this paper gains lower end-to-end delay and better Mean Opinion Score (MOS) values while ensuring higher fluency in weak network scenarios.

Based on the analysis of measured data, a two-dimensional hydrodynamic Salinity Transport Model in the Yangtze River estuary is established by using Mike21 model. This model is used to study the average daily distribution and changing patterns of the saline water mass in the South Branch that are from the North Branch while only the saltwater intrusion from the North Branch is considered. It determines the intrusion process of saline water mass from the North Branch and the area that are affected by saline water intrusion. In addition, through the research of salt water mass core moving down and salinity variation in the south branch channel, the flow pattern and mechanism of salt water mass in the south branch channel are revealed. The saltwater intrusion from the North Branch has a great impact on the vertical, horizontal and vertical salinity fields of the Yangtze River estuary. The variation trend of statistical variance, deviation and standard deviation of the half month time series of salinity at each point along the route is basically the same, showing a gradual decreasing trend towards the downstream. Those show that the salinity dispersion is larger in the upstream estuary and lower outside the estuary. Combined with the salinity kurtosis and skewness, the deviation degree and direction of the salinity distribution at each point along the route can be judged, which can reflect the comprehensive process of runoff, tidal current and topography. Therefore, the kurtosis and skewness statistics can effectively describe the law of transport and dynamic characteristics of intrusion saline water masses in the Yangtze River Estuary.

In order to implement the strictest water resource management system and ensure the sustainable utilization of water resources, it is necessary to carry out the work of limiting pollution in water function area. The calculation of water pollution capacity is the basis of pollution limitation in water function area. Code of practice for computation on allowable permitted assimilative capacity of water bodies (GB/T25173-2010) is the main reference basis for the calculation of water pollution capacity. There are some limitations in the practical application. In this paper, the zero-dimensional model is modified based on the current code of pollutant carrying capacity. And the determination of designed hydrological conditions was discussed. This would give reference for the calculation of water pollution capacity.

To satisfy the requirements of underground engineering applications, an anti-radon coating was developed by selecting suitable modified polymer emulsions, element materials, pigments, fillers, additives, etc. Experimental results on radon mitigation performance show that for a coating thickness of 2.0 mm, the radon mitigation efficiency reaches 95.1%. In addition, an anti-radon coating has a very low content of toxic substances and excellent water resistance and durability. Using the coating in an engineering application showed that the coating has the advantages of fast drying speed, no peculiar smell and excellent radon mitigation performance, where 2-3 coats produce good radon mitigation in damp and sealed underground engineering environments.

The adsorption process of methylene blue (MB) onto blast furnace slag (BFS) based zeolite (BFSZ) was discussed. Via the analyses of XRD, FE-SEM, XRF, CEC and SSA, the main composition in in the product was Na-A zeolite. And the Na₂O content, CEC value and SSA value of the BFSZ samples was significantly increased to 19.61 wt.%, 3.06 meq/g and 27.55 m^2.g¹ respectively. Moreover, the adsorption kinetic for MB onto BFSZ was examined and best represented by Pseudo-second-order kinetic model. Moreover, the adsorption capacity of MB from aqueous solutions reaches up to 21.89 mg^.g¹ at 30 °C.

The pollution index method and Hakanson potential ecological risk index method were used to study and analyze the pollution factors (Hg, Cu, Pb, Zn, Cd, As, Cr) in surface sediments of the sea near Bohe Port of Maoming. The results show that the overall contamination level and potential ecological risk of typical pollutants are at the low level in this sea area. The sequence of the pollution degree of heavy metals in surface sediments is As> Cr >Zn> Hg>Cu>Pb>Cd; and the potential ecological risk sequence is Hg>As>Cd>Cu>Pb>Cr >Zn. Hg is the major factor of potential ecological risk in surface sediments of this sea area.

Through macroscopic and microcosmic analysis of fracture, chemical composition analysis, fracture microscopic analysis and other methods, the causes of longitudinal and transverse fracture (only longitudinal cracking commonly through thousands of columns) in hydraulic burst test of L360M LSAW pipe were analyzed. Test results showed that the causes of transverse cracks in the steel pipe were the poor toughness of the pipe material which was related to the serious ferrite-pearlite banded structure. The serious band structure in the pipe was related to the high manganese content in the steel, which leaded to manganese segregation. We can use some measures to reduce the banded structure including controlling the element manganese content reasonably, reducing the final rolling temperature, controlling cooling rate and micro alloying.

This paper studies the experimental free bulging and buckling of bi-segmented cylinders. Two cylindrical shells are manufactured using argon-arc welding and analyzed for manufacturing errors. Next, free bulging tests and hydrostatic pressure tests are conducted to verify the feasibility of the free bulging technique and to confirm the destruction form of the bulged bi-segmented barrel. At the same time, the reasonableness of the circular thick plate is examined, which is determined according to the deformation consistency. The results show that the bi-segmented cylindrical preforms exhibit good welding quality by TIG welding technology. It has the same critical pressure load as a single cylindrical preform. Nevertheless, the bi-segmented cylindrical preforms have larger internal volume and better buoyancy capability than the single cylindrical preform. The free bulging bi-segmented shell has good symmetry along the axial direction. It has larger internal volume and higher critical buckle capacities than the non-bulging one.

The soil on the west bank of the Xiangjiang River in the main urban area of Changsha, Hunan Province is referred to as shore soil, and the soil on the mid-levels of the Yuelu Mountains in Changsha is referred to as offshore soil. To stabilise the heavy metals in the soils, which do not readily migrate by pyrolysis, these soil samples were heated at 450°C for 3 hours in a muffle furnace and removed after natural cooling. These heated and stabilised soils were analysed by inductively coupled plasma emission spectrometry (ICP-OES), scanning electron microscopy (EMS) and XRD diffractometry respectively. It can be found that: (1) There is a difference in the heavy metal content between the shoreline soil and the offshore soil of the Xiangjiang River. (2) The scanning electron microscope shows that the microstructure of the soil is altered by prolonged river water infiltration and washing. (3) Both onshore and offshore soils are a mixture of crystalline and non-crystalline materials, with less non-crystalline material in the onshore soil compared to the offshore soil. (4) The main crystalline material in both onshore and offshore soils is SiO2.(5) Soil samples containing metallic elements are mostly in non-crystalline form.

Objective: Air particulate matter concentrations in Changchun City, Jilin Province, may change around the autumn heating day. The aim of this study was to provide data references for environmental protection, detection and regulation in Changchun. Methods: Atmospheric particulate matter samples were collected using an airborne particulate matter sampler on the roof top of the Civil Engineering Teaching Hall on the campus of Jilin University of Construction; free settling dust of Atmospheric particulate matter was collected using metal trays. Atmospheric particulate matter concentrations were analysed by manual detection methods (weight method), carbonaceous fractions by total organic carbon analyser, and atmospheric fallout material composition and crystal structure by XRD diffractometer. The physicochemical properties of fine particulate matter around the autumn heating day in Changchun were investigated. Conclusions: (1) The daily average concentrations of various types of atmospheric particulate matter PM₁, PM_2.5 and PM₁₀ generally increased after the start of the heating period. However, air quality is influenced by a combination of meteorological factors, of which emissions of air pollutants from urban heat generating plants during the heating period is only one aspect. So there is a situation where the average daily concentration of atmospheric particulate matter is lower after heating than before. (2) Analysis of the atmospheric its particulate matter PM_2.5 samples collected around the heating day showed that the daily average concentrations of organic carbon (OC) and elemental carbon (EC) of atmospheric its particulate matter PM_2.5 increased significantly after the heating day. (3) There was no significant difference in the main components of atmospheric dust fall before and after the heating day in Changchun, with the main components being crystalline SiO₂ and a small Number of impurities.

Blind Write Protocol allows more transactions to be executed at the same time. It is achieved by removing the locking phase to any entity while the write operation is performed. To have a fair throughput comparison with the Locking Protocol then this paper introduces three different approaches using Blind Write Protocol that can preserve consistency. The throughputs are presented and compared with Locking Protocol. We found that preserving consistency using Blind Write Protocol needs more operations. As a result, it consumes more resources and makes the response times much slower compared to Locking Protocol. If Blind Write Protocol ignores consistency, then it shows better response time compared to Locking Protocol. The Blind Write Protocol aims to achieve availability rather than consistency.

Currently, with the fast modernization of wireless networking technologies as well as the very complex needs of the network users, multimedia data takes for a large proportion in the types of network services, this type of data requires Quality of Service (QoS) differs from traditional data, by ensuring the listening and viewing elements of the user. In this paper, we analyse the effect of video and voice data on QoS of wireless network services based on the establishment of an experimental evaluation (emulation) system or testbed, thereby providing empirical analysis on the effect of multimedia data on mobile wireless QoS.

One of the most significant steps in solving multi criteria decision-making (MCDM) problems is the normalization of the decision matrix. The consideration for the normalization of the data in a judgment matrix is an essential step as it can influence the ranking list. This study investigates the effects of normalization on an AHP-VIKOR hybrid method in the selection of Web services. The Web services considered in this research offer similar functionalities but with different Quality of Services (QoS). For the purpose of this study, ten web services were selected. Each of these services were evaluated against five difference QoS requirements namely, response time, throughput rate, latency, availability, and reliability to determine the most appropriate Web services. The five normalization techniques employed are linear sum, linear max, linear max-min, enhanced accuracy and vector normalization. It was observed that different ranking lists are produced when applying different normalization techniques to the VIKOR method and normalization has an influence on the final ranking list.

The paper presents a modelling of the communication system between the TI F28379D microcontroller and the sensors responsible for measuring the flight parameters of the missile. Data acquisition in aviation and their detailed interpretation are very important for the correct operation of the system. One of these devices is the AHRS module (Attitude and Heading Reference System) which was used for the test. Communication between devices is carried out via the CAN bus. The MATLAB Simulink environment was used to create the model, which made it possible to design the program and then implement the generated code on the processor. The article presents how the individual tasks have been performed by the designed controller. Then an interface was created to allow quick change of basic parameters without in-depth interference with the program. The developed program can be used as one of the autopilot subsystems and used for real-time tests.

In the last years, many solutions have been developed based on the Internet of Things (IoT) applied to several fields such as agriculture, road safety or electric lighting, among others. These devices are usually located in places that are not easy to access, which makes their software difficult to update. These updates should be carried out to improve the software of the devices to include new functionalities and/or solve security problems. This paper presents an Over-the-Air (OTA) programming system for devices that do not natively integrate a wireless update service. A description of the hardware used is included, as well as the update management application developed to carry out this task. The approach proposed has been validated by updating a microcontroller-based system applied to the area of road safety. The validation consisted in measuring the additional consumption required by the auxiliary update system compared to the base consumption, as well as determining the time required to update an IoT node in both wireless and wired mode. The results obtained show a reduction in consumption of 577% and a reduction in the updating time of 66%.

The Internet of Things (IoT) is growing globally at a fast pace. However, the expansion of the IoT paradigm has brought with it the challenge of promptly detecting and evaluating attacks against these systems. The Message Queuing Telemetry Transport (MQTT) protocol is one of the most used protocols in the IoT scenario due to its lightness. The MQTT-SN protocol, which is an even lighter version of MQTT, is specially designed for embedded devices on non-TCP/IP networks. This paper presents an exhaustive assessment of the MQTT-SN protocol and describes its shortcomings, which can allow an attacker to compromise the security of the entire IoT infrastructure. We have designed 3 different attacks in order to allow us to evaluate the different security impacts on a real MQTT-SN network. All the attacks were implemented and tested, and we show how they work and their impact on performance. Furthermore, a non-attacked scenario was also implemented to allow us to compare the performance of an attacked system with that of system without attacks.

A new framework for adopting inertial sensors in a clinical test was proposed and tested in this study. A self-assembled-and-coded, inertial sensor-based wearable system was validated by comparing it with a commercialized optical tracking apparatus. Twenty-five post-stroke patients were enrolled in a clinical walk test while wearing this validated system to simultaneously assess the affected lower extremities' functional walk competency through extracted kinematic parameters. Their average walking speeds were correlated with various gait parameters, such as the ranges of motion of the individual joints along the sagittal plane and the low back motion. The validation results proved this developed system is precise and accurate. The average walking speeds showed a modest correlation with the range of motion of the hip (r = 0.33) and a moderate and negative correlation with the motion along the coronal plane of the low back (r = -0.55). Thus, this framework supports a new method to adopt wearable devices for clinical application. It also broadens the application of the clinical walk test as an integral assessment tool for assessing functional walking competency and gait parameters, which is feasible for rehabilitation canters to monitor post-stroke patients.

The usage of the Internet increases significantly especially regards COVID-19. The lifestyle relies of the networks and the Internet to do all tasks such as job, meetings, and education. The mobile ad hoc network (MANET) is a type of wireless network that nodes can communicate without the occurrence of an administration point. Nodes in MANET can join and leave the network frequently, so the scenario of networks merging and partitioning can occur. In this study, the detection of Denial of Service (DoS) attack which degrades the performance of the network will be illustrated in the situation of network partitioning. The detection will be based on MrDR method and puzzle map method, the puzzle concept will help to complete the network partitioning and avoid the DoS attack. The proposed method is simulated using NS2 and the results shows the effectiveness of the puzzle map method to increase the performance of network during the partitioning scenario.

The number of cyber incidents in which an Internet of Things (IoT) device or system is present is increasing every day, requiring the opening of forensic investigations that can shed light on what has occurred. In order to be able to provide investigators with proper solutions for performing complete and efficient examinations in this new environment, IoT systems and devices are being studied from a forensic perspective so that tools and procedures can be designed accordingly. In this article, the IoT version of one of the most used Linux distributions, namely Ubuntu, is studied to determine in what way a forensic investigation of this system should be performed, detailing how to approach the acquisition and analysis phases. In addition, both the volatile and non-volatile artifacts that might held useful information are listed and described.

The global 5G commercial era is approaching. The throughput and number of connections of 5G will be 100 times that of 4G. The processing power of massive data will determine the experience of users surfing the Internet. In order to cope with the geometric growth of network data, the computing power and storage capacity of 5G data centers need to be correspondingly improved. With the surge of servers, the energy consumption of the 5G data center has also been greatly improved. How to effectively improve the performance of the server and reduce the energy consumption has become an extremely urgent problem. This paper focuses on the management method of the data center server in the context of 5G network transformation, and proposes effective solutions in combination with the application scenarios of large-scale 5G data centers.

In order to discover the impact of intelligently-configured cloud service capacity for high performance, this paper proposes a multi-agent model that simulates industrial manufacturing activities via a cloud platform via XaaS (Anything-as-a-Service). The multi-agent model is adopted to simulate cloud manufacturing based on two groups of service providers with different configurations on service capability. The capability configurations have been conducted against 8 kinds of service demands with the aim at the impact on how to configure cloud service capacity for high performance. Furthermore, the intelligent configuration is proved with high relevance of better task responses to diverse service demands. In the meantime, the intelligent configuration is also characterized with flexible adaptability and service balance. As a result, service performance has been effectively improved and significantly optimized by adjusting the service sequence within a given period that reduces the switch frequency among diverse service types.

At present, China's Internet of Vehicles (IoV) industry is booming and constantly promoting the society to the intelligent era. However, the development of network technology not only brings the opportunity of technology promotion for the IoV, but also brings new network security risks. Therefore, it is urgent to further strengthen the construction of the protection system for the IoV. Based on the original architecture of the IoV, this paper first analyses the security risks of the terminal layer, the network layer, the platform layer and the application layer under the background of the electric internet of things. According to the new security threats faced by the four layers of the IoV, the network security protection system is constructed, which strictly follows the overall information security protection strategy of the State Grid Corporation of China and the new requirements of "network security level protection 2.0". Deepening the construction of the intelligent defence system of the IoV would be significant for the development of coordinated network security and the IoV, and would continuously improve the protection ability of the IoV.

DEM is an important data source to describe the surface morphology, but it is not a real 3D model, which can not meet the requirements of true 3D description under the ground. LIDAR point cloud data is a new true 3D data with high precision and high density. Based on the analysis of the differences between DEM and point cloud data in acquisition method, data structure and model construction, this paper proposes a 3D point set data model based on regular grid 2D data field, as well as the idea of regional modeling, and tests the feasibility of the data model through the upper and lower boundary modeling method. The experiments show that: (1) the 3D point set data model based on regular grid 2D data field is compatible with complete DEM data and simplified point cloud data, and has good expansibility; (2) the newly-built data model can complete the true 3D modeling of simple underground entity with high efficiency when the amount of data is only doubled; (3) The new data model can be generated by inputting DEM data, point cloud data and simplified algorithm of point cloud data under the same coordinate system. It has the potential of large-scale, multi-scale and automatic output processing, and has a good prospect of popularization.

Feature pyramid network is wildly applied to predict the targets at different scales in many one-stage detectors. However, most of them treat all feature levels without level-wise attention. As a result, different levels may capture the same object and predict totally different labels, which lead to lower AP on evaluation. Besides, many classic detectors aim at relieving the imbalance between hard and easy samples on classification task, while we find the fact that hard-easy imbalance also exists in localization. To general detection task, bboxes regressed at high IoU are much less than those at low IoU so that detector can easily be biased towards suboptimal bboxes and localization accuracy will be harmed. Based on these two points, in this paper, we propose a Scale-Adaptive Selection Network (SASNet) with a novel Dynamic Focal IoU (DF-IOU) loss. The Scale-Adaptive Selection Network introduces multi-scale attention mechanism into feature pyramid so as to assign attention weight for feature maps on each level, which enables the network to select the dominant levels for prediction and alleviate the prediction conflicts between different levels. Furthermore, we design Dynamic Focal IoU loss to increase loss contribution of easy targets so that the coordinates of these easy targets can regress better and the bounding box will fit tighter. Our experimental results show that our SASNet with DF-IoU loss can increase average precision of objects at small, medium and large scale on the MS COCO and CCPD dataset.

On the basis of traditional unequal clustering protocol for wireless sensor networks, an energy-balance unequal clustering routing protocol is proposed. In the clustering stage, the entire network is divided into clusters of different sizes based on the remaining energy and the distance from the sink node to the node. In the cluster establishment phase, each node calculates the cluster head selection time based on the relative remaining energy, the position, the average distance between the nodes in the cluster. The cluster head is selected by time series, and non-cluster head nodes are selected to join the cluster based on the distance between cluster heads and the remaining energy of the relay cluster head. In the route establishment phase, an optimal transmission path based on minimum spanning tree is built, based on the distance between cluster heads, the residual energy and the distance from node to the sink node. During data transmission, a single hop within a cluster is adopted, and a minimum spanning tree multi-hop method is used between clusters, and the data is finally transmitted to the sink node. The simulation shows that the routing protocol can effectively balance energy consumption and save energy, and the life cycle of the network is prolonged.

With the continuous development and progress of the Internet, computing power is gradually moving from the center to the edge. In order to perceive, measure, schedule and manage the distributed computing resources, it is necessary to model and evaluate the node capability. Based on the development trend of network computing convergence and the demand of network computing service scheduling, this paper studies the unified measurement model and resource allocation algorithm for the integration of network computing and heterogeneous resources. The node resources are divided into four dimensions: computing, communication, memory and storage, and on this basis, the comprehensive ability evaluation index is proposed. Through simulation analysis, the effectiveness of the resource allocation algorithm based on node capability modeling is verified.

Whether in the power grid planning, design or operation stage, the identification of fragile component in the power system is always one of the core issues of power grid security analysis. In order to realize the accurate positioning of fragile lines, firstly, considering the overall distribution characteristics and local fluctuation characteristics of the power flow under the disturbance of the breaking, a composite entropy evaluation model of fragile components based on the distribution entropy and transfer entropy of the power flow is proposed. Secondly, the power flow betweenness evaluation method based on the power transmission transfer distribution factor is used to measure the transmission contribution and carrying capacity of the branch in the power grid. Through normalization, a comprehensive evaluation index for vulnerable branches is established. Aiming at the proposed identification method, the IEEE10 machine 39-node calculation example and comparison with other methods show that the method in this paper can more comprehensively and accurately identify the fragile branch of the power grid.

The trusted cross-domain sharing capability of the Internet of Things (IoT) terminals needs to be improved urgently. Aiming at the technical issues of routing overhead and authentication security of data sharing of IoT devices, this paper proposes a trusted transmission architecture for network-chain collaboration. Introduce the mobile edge computing (MEC) platform in named data network (NDN) to deploy blockchain light nodes and improve the PBFT consensus algorithm. Besides, a cross-domain authentication algorithm based on elliptic curve encryption is proposed. Simulation results show that the improved PBFT and cross-domain authentication algorithm can effectively improve IoT cross-domain sharing capabilities.

It is generally the dynamic behavior of multiple information in the network. Based on the principle of propagation dynamics and mathematical model, this paper simulates the dynamic process of information in the network, and analyzes the influence of network structure and propagation dynamics on the dynamic behavior of information in the network through the simulation results. By simulating the dynamic process of communication, we find that the location and release time of intervention information in the network will have an impact, and we can control the dynamic behavior of information in the network by controlling the location and release time of intervention information.

Nowadays, the technical and technological capabilities achieved in the field of agriculture permit to implement quickly convertible (adaptive) crop production systems. This, along with increased efficiency, also greatly complicates the management process. Automated control systems (ACS) with adaptive properties are needed. The concept of creating an ACS assumes the implementation of the principle of modularity and flexible changes in the structure of the management hierarchy, powers, tasks and functions for geographically distributed decision-making centers and executors. Key technologies in the design of such ACS are: synthesis of a temporary control loop from geographically distributed elements and synthesis of procedural regulations for a decision-making center included in the temporary control loop. The article formulates the problem of synthesis of procedural regulations in the form of a tensor transformation problem of the original electrical network (library of procedures) into a network with specified properties. A method for obtaining the tensor equation of transformation of the original network is presented based on the requirements for the composition of the input and output data of the synthesized procedural regulations. The solution obtained after tensor transformation has the form of a matrix of connections and a vector of voltages on the intermediate coils of the original electrical network (library of procedures). This forms structural links for the synthesized procedural rules. The demonstrated approach can be extended to solve a wide range of problems of adaptive changes in the structures of organizational management systems.

Various previous works have sought to achieve hand prostheses with natural movements. It has been employed tools to recognize patterns of surface electromyographic signals associated with each move. Although many successful studies classify some types of hand movements with high performance, the results show that the speed and strength must be analyzed so that the resulting moves are like those of a natural hand. This study evaluates 23 healthy subjects at two different speeds and six types of movements (pronation, supination, ulnar deviation, radial deviation, flexion, and extension -276 records of SEMG and Velocity). The objective was to obtain a model (transfer function) that would allow the relation of the velocity profiles with the tone of the forearm SEMG signals. The results show models with an average RMSE of 18.55% for slow movements using low-order systems (2). The parameters of the models between subjects are very different, with high coefficients of variation and standard deviations, which implies that the fitting must be for each subject.

Industrial automation has become an area in full development and of great importance for all industrial sectors since it allows to improve the productivity of companies and the quality of their products. Currently, factories (i.e., small, or medium) must implement new technologies to increase its production and provide qualified products to enter with force in regional markets and nationals. This article shows the design and start up of an automatic palletizer which will have a manual mode in which each actuator in the system can be operated independently; and an automatic mode that will organize the input elements in the previously established way. For companies that produce in batches, is important to reduce storage spaces in warehouses, dispose of their products in an orderly and practical way for their subsequent dispatch. This last process must be effective and fast so as not to generate inconveniences in the delivery of orders to buyers. Our project is focused to the footwear sector, especially, how to seek greater development and performance of these companies where the packaging time of their products can be optimized so that it can be used in other tasks within the company. Also, the result of this investigation shows the engineering process carried out to obtain a prototype of a functional, automatic, productive, quality, and economic product that meets the customer's requirements. Improvements that will do and future work are included too.

We address the problem of the stable reverse driving of a vehicle with multiple trailers. By itself, the reverse driving of a multiple trailer vehicle is highly unstable, so a proper steering control is needed. First, the straight reverse driving is analysed and a stable solution to the problem is explicitly calculated. The solution depends on the specific geometry of the vehicle, namely on the lengths of the tractor and the trailers, and on the set of parameters that determine the rate of convergence of the reverse driving towards the straight reverse driving. We describe two different approaches towards the derivation of the solution of the stable reverse driving. We show that the solution to the problem is uniquely determined and finally we show that the proposed solution can be easily applied to the reverse driving of the tractor along an arbitrary, gently curved, path.

This paper presents the development of two dual-loop control strategies for an underwater Remotely Operated Vehicle, ROV. The first strategy consists of two PID controllers while the second one features a LQG controller in the inner loop, and a PID controller in the outer loop. The transient response of the two strategies is compared when an anti-windup gain is applied and when there is no anti-windup gain, and a way to tune this gain in the case of statespace controllers is proposed. For further comparison, the strategies are simulated for a variable set point, for the three variables to be controlled, x, z, yaw, so that both their tracking and the effect of coupling can be seen. In conclusion, the use of anti-windup gain is recommended for control systems that reach saturation, so that they can react quickly when they are in an operating zone, and the use of a random matrix for the calculation of this gain in state-space controllers is suggested.

A model reference controller for a powered wheelchair is described. The chair is fitted with sensor systems to assist a disabled user with steering their chair. The controller can cope with varying circumstances and situations. Non-linear terms are compensated for using an adaptive and automatic scheme. Consistent and dependable veer-controll is considered and the system was able to deal with uncertainties, for example changing surfaces, different shifting weights of users, hills, bumps, slopes and differences in tires and wheels. The controller has a quasi-linear closed-loop behaviour and that means that extra outer control loops can be appended later, for example path-following algorithms. An assistive agent was also created so that sperate wheelchairs will be able to communicate with each other in the future.

Currently, flight simulators are being developed to train pilots in the professional piloting of an aircraft in "special cases of flight". This is due to the need to teach the pilot the professional skills of piloting an aircraft in situations that are dangerous to reproduce in real flight. An aviation simulator is a combination of several imitators that simulate information about the behavior of all units of the aircraft and information about the interaction of the airframe of the aircraft with the atmosphere, depending on the control actions transmitted by the pilot through the simulators of the flight simulator controls. It is believed that the quality of information models synthesized by simulators is determined by the capabilities of modern software and hardware elements, or used mechanical units. It is impossible to create a complete model of any node; therefore, each imitator synthesizes, in addition to true information, additional false information. It is impossible to exclude false information. Therefore, when developing aviation simulators, a number of training situations are determined. The article discusses the features of assessing the degree of participation of information from two groups imitators in the formation of the components of a cognitive model that allows a pilot to fly an aircraft.

This paper presents the methodology for 3-D simulation and design of the magnetron with two energy outputs. The principal functionality of the modified magnetron, both theoretically and experimentally, has been determined. The central distinguishing feature of this magnetron (for example, unlike the conventional magnetron with one output) is the availability of second energy output in the anode block. The mathematical models of both the resonant anode block and the electron-wave interaction are described. The dispersion characteristics of the anode block for cavities various geometries are given. It is supposed that forming the total RF field in the interaction space results from the interference of RF fields excited severally in the resonant anode block consisting of the small and large cavities (resonators). For PIC-simulation of electron-wave interaction in the dual-output magnetron, the non-linear system of equations is stated as a self-consistent system containing the equation of motion (for electron stream), the equation of excitation (for RF field), and Poisson's equation for calculating the space-charge field. The fundamental feature of the self-consistent system of equations is a new algorithm for determining the Coulomb interaction forces. The implementation of the mathematical model made it possible not only to gain new knowledge about the physical processes in the magnetron but also to determine its output characteristics. On operating frequency of ∼ 13.34 GHz, at an anode voltage of 495 V, a magnetic field of ∼ 0.25 T, and with air cooling of the magnetron, there were obtained the following limiting values: the RF output power of ∼ 14.6 W and the power conversion efficiency of ∼ 40.8%. The use of the second energy output allowed extending the magnetron's functionality and implementing the modes of the frequency tuning (adjunction) and its stabilization. The simulation results are in good agreement with the experiment.

To the 100th anniversary of magnetron

In this paper, we address the problem of deep Q-Network (DQN) based joint power control and beamforming for enabling interference suppression in software defined UAV swarm (SD-UAS) network. We first present the optimization model of joint beamforming and power control (JBPC). Then, to solve this joint optimization problem, we make use of UCB exploration in the learning process of DQN. Simulation results validate that the convergence obtained with the proposed UCB-DQN strategy outperform the DQN learning algorithm, which can to raise the exploration efficiency and sequentially speed up the convergence for the JBPC problem. It is benefit to supress interference and enhance the SD -UAS communication performance.

Tencent's related products occupy the first place in the domestic social software market. This paper explores the Internet of things control method by using Tencent QQ (hereinafter referred to as QQ) application, with the purpose of realizing an IOT system controlled by social chat software. The research finds that: taking user QQ as the instruction sending end, cqhttp as the message transmission center, through the information processing program, instruction judgment program, DSL parsing engine, command transmission program and execution program, and finally through Arduino control electronic components to make relevant actions. A social software IOT control system with simple control mode, flexible design, fast expansion, fast response, good fault tolerance and high security is realized. The implementation of the system can be used as a reference for relevant fans to realize the IOT control system with various IM software.

It is difficult to achieve an excellent result to accomplish the transmission shaft fault diagnosis with single feature, so thadt a feature fusion fault diagnosis method is proposed. First, the preprocessed vibration signal is decomposed by variational mode decomposition (VMD), and the Intrinsic mode function (IMF) is obtained; Then, singular value, sample entropy and approximate entropy are obtained as features and fused; Finally, the fused feature is used as the classification object of SVM for fault diagnosis. In this paper, the feasibility of this method is verified by the transmission of experimental data. The experiment included five conditions, and the diagnostic accuracy was 99%.

Active obstacle avoidance control strategy is the main question of intelligent vehicles, and active four-wheel steering is gradually used in the intelligent control system. Considering both tracking performance and driving characters, an active controller with four-wheel steering (4WS) and active rear steering (ARS) based on adaptive model predictive theory (AMPC) is designed. The control architecture is composed a supervisor and an AMPC controller. The supervisor is used to select the appropriate control mode and the AMPC is used to calculate the expected steering angle when the stability indexes over the safety threshold. Finally, the proposed control strategy is simulated via Carsim-Simulink co-simulation. The results show that the integrated controller can track the obstacle avoidance path and has good driving performance.

In the field of image classification, deep learning has become the focus of research. But when the number of training samples is small, especially when there are a large number of intra-class variations in the small samples, the performance of deep learning is often not satisfactory. To deal with the problem, a new dictionary learning method based on sparse representation and an improved coefficient's constraint is proposed. A general dictionary is learned to eliminate noise signal, and then based on the general dictionary, a class specific dictionary is learned by an improved coefficient's constraint which maintaining the independence of the dictionary atoms between-classes, while allowing the dependence of the dictionary atoms intra- class. The class specific dictionary combined with the general dictionary is used for the image recognition. Experimental results show that, compared with the state-of-the-art dictionary learning methods, the proposed method usually shows better performance on image classification with small data sets.

The recent development of unmanned aerial vehicle flight control creates a strong demand for higher control accuracy and control quality. In this paper, the precise control and stable hovering problem are studied for unmanned helicopters. To solve the problem, we propose a hovering controller for dual-loop of unmanned helicopters. Firstly, the nonlinear mathematical model of unmanned helicopters is established based on Newton-Euler's laws. Then, we respectively design the position model and attitude model. In the position model, we use the cascade PD controller to realize the precise control; In the attitude model, we use the cascade PD controller to realize the stable hovering. Simulation results show the effectiveness of the conclusions made in this paper.

In this paper, two mathematical models of the primary inverted pendulum are established based on the Simulink and the Simscape toolboxes of the MATLAB respectively. The inverted pendulum changes from the unstable state to the stable state after performing the state feedback control and the PID control method. Under these two control methods, the overshoot of the Simulink model is 57% and 80% smaller, and the adjustment time is 25% and 20% larger than that of the Simscape model. The control effect of the PID method in the two models is better than that of the state feedback method. The adjustment time of the pendulum rod is 40% and 37.5% smaller, and the overshoot is 82% and 61% smaller than that of state feedback control, respectively.

In this paper, a backstepping adaptive control method based on Kalman data fusion (KDF-BAC) is proposed to solve parametric and non-parametric as well as state variables in manipulator dynamics model. Firstly, the non-parametric uncertainties are regarded as internal disturbances to simplify the dynamics model of the manipulator. Secondly the backstepping control law is derived using the idea of recursive design, and the adaptive method is used to identify the uncertain parameters to provide the estimated value of unknown parameters for the backstepping control law. Finally, the Kalman data fusion method is used to reduce the uncertainty of the velocity information, and the most reliable estimate value is obtained, which is fed back to the controller. The simulation results show that the proposed KDF-BAC can reduce the influence of manipulator uncertainties on trajectory tracking performance, and has better control effect than adaptive control and backstepping control.

In this paper, the adaptive cruise control method for the virtually coupled train set (VCTS) in rail transit is proposed. First of all, a leader-following VCTS dynamics model is constructed by analyzing the dynamic evolution of VCTS in a real-world environment, taking into account uncertain parameters, unknown disturbances and controller input saturation. Next, to cope with the position and speed constraints, the position artificial potential field function and the speed barrier function are embedded into the designed sliding manifold. And a novel adaptive cruise control protocol is designed, which can simultaneously deal with uncertain resistance parameters and unknown disturbances while tracking the desired cruise speed and maintaining the desired distance. Based on Lyapunov stability theory, the proposed control protocol guarantees the ultimate boundedness of all subsystems. Finally, the simulation results verify the effectiveness of the theoretical analysis.

This paper proposes a Q-learning-based state feedback suboptimal controller to solve the current control problem of three-phase grid-connected LCL coupled inverters with unknown circuit parameters. In practice, the circuit parameters of the inverters will change obviously for reasons such as calculation errors, external environment and operation aging, which makes the dynamics of the inverter system become unknown. With the circuit's model of the LCL inverters and the reference current dynamic, an augmented system is constructed and a discounted performance function is formulated as the optimal objective of current control which transform it into a H∞ tracking control. Using Q-learning algorithm with model-free characteristics, a current controller is proposed in which an iterative reinforcement learning (RL) algorithm is embedded. Simulations are presented to verify the valid of the proposed control scheme, where especially the results shows that it can keep excellent control performance under the condition of inverter parameter mutation and grid voltage distortion.

This paper proposes a neural sliding mode control method for the tracking problem of the longitudinal dynamics of air-breathing hypersonic vehicles (ABHV). Considering the input/output feedback linearization, a high-order sliding mode law of the elevator deflection and the fuel equivalence ratio is designed. Moreover, the effect of uncertain model and control input disturbances is approximated with a Radial Basis Function Neural Network (RBFNN). The stability of the closed-loop system is analysed based on Lyapunov theorem. Simulation results shows the good tracking performance of the proposed controller and robustness with parameter uncertainties. All the signals are globally bounded and converged in short time.

In view of the traditional PID controller used in the UAV visual servo control, there are problems that the parameter setting is extremely troublesome, and the speed and stability of the system cannot be considered at the same time. An improved PID-based UAV pod visual servo control method is designed. By introducing the concept of integral separation, according to the size of the deviation between the target's pixel coordinates in the image and the set expected pixel coordinates, it is decided whether the integral control is used or not, which can avoid large overshoot and improve control accuracy. The simulation experiment shows that the visual servo control algorithm based on improved PID has smaller tracking error and higher critical tracking speed than the visual servo control algorithm based on traditional PID, and it has better performance.

To improve the parameter robustness of the open-winding permanent magnet synchronous motor (OW-PMSM), this paper proposes an improved model-free predictive current control (MFPCC) method. First, the first-order ultra-local model of OW-PMSM is established, which does not contain any motor parameter. Secondly, a sliding mode observer (SMO) is introduced into the model to estimate the current in d-q-0 axis. Then, to suppress the torque ripple generated by the zero-sequence current (ZSC), the central hexagonal modulation strategy and the q-axis current injection method are combined, and the compensation current that needs to be injected into the q-axis is calculated according to the estimated value of ZSC. Finally, after considering the one-step delay, the voltage reference at the next instant is calculated based on the estimated values of d-q axis current and compensation current. Simulation and experimental results indicate that the proposed method can improve the parameter robustness of OW-PMSM, and the performance of current and torque has been improved under various parameter mismatch conditions.

This paper re-examines the discrete-time Linear Quadratic Gaussian (LQG) regulator problem. The normal approach to this problem is to use a Kalman filter state estimator and Kalman control state feedback. Though quite successful, an alternative approach in the frequency domain was employed later. That method used z-transfer functions or polynomials in the z-domain. The transfer function approach is similar to the method used in Wiener filtering and requires the use of Diophantine equations (sometimes bilateral) to find the optimal controller. The contribution here uses a similar approach but uses lower triangular Toeplitz matrices instead of polynomials to gain advantage of eliminating the use of Diophantine equations. This is because the single Diophantine equation approach fails when the system has non-relative prime polynomials and the need for bilateral Diophantine equations is computationally far more complex.

This paper describes an organization-based approach to modelling an autonomous distributed system. This approach simplifies design of such systems and allows for a better understanding of the system overall. Such an approach is applicable for modelling a variety of distributed systems. We demonstrated it for a swarm system of autonomous drones used for disaster relief aid delivery.

The article presents programs that implement original approaches to group expert rating estimation and fuzzy inference. They implement probabilistic models based on Bayes' Formula, previously proposed and published in the works of the authors. In these models, the estimated input data are interpreted as evidence in favor of one or another hypothesis from the set of possible ones, determined by the specifics of the model. All the evidence obtained is, in one way or another, transformed into a set of Bayesian conditional probabilities calculated under the assumption that the corresponding hypothesis is true, and the posterior probability distribution on the set of these hypotheses is used as the output. This distribution is used either directly as a result for decision making, or as a basis for calculating the final result. The features of the software implementation of models on the Java platform are discussed, the advantages of the models, confirmed or identified in the process of software implementation, are noted. The developed programs have a convenient graphical user interface and can be used as decision-making support tools to solve applied problems in the field of expert rating estimation and fuzzy inference.

This paper discusses the challenges and impacts of the luminance calculation method for roadway lighting applications and recommends a novel illuminance calculation method as the most suitable for Roadway Lighting Applications. This change would reduce the costs of roadway lighting design without increasing risks, which will benefit society and the profession. Luminance calculations are complicated and take approximately five times longer than the new illuminance calculation method, provide no additional insight, and result in the same design outcome. This paper makes a comparative study and analyzes both methods using regular comparative assessment and quantitative and qualitative assessments and provides a solution to the over 50-year-old challenge. The quantitative evaluation uses a sample case study and examines its corresponding benefit-cost ratio. The qualitative approach is to complete a survey among the peers and the lighting designers. This is the first paper to address all these parameters of roadway lighting holistically. This paper will be helpful for academics, researchers, scientists, engineers, consultants, architects, lighting designers, contractors, developers, financial institutions, and government agencies funding outdoor lighting.

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics.

The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It's verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there's a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren't falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.

The working mechanism of a half-bridge SiC MOSFETs two chips parallel module is studied. Its parasitic inductance of the power module is simulated by using the Ansoft Q3D Extractor software, and its heat transfer mechanism is simulated using the finite element software ANSYS software. Aiming at reducing the parasitic inductance, a new topology of power chips inside the module is designed, which places the chips that are in the same working circuit loop in close vicinity to reduce the whole circuit length and area. Meanwhile, the thermal characteristics are also analyzed. Considering the factors of parasitic inductance and heat dissipation, the optimal module design scheme is finally determined.

With the deepening of the aging process and the weakening of the family's ability to care for the aged, urban residents will have an increasing demand for elderly care service institutions in the future. Therefore, it is necessary to optimize the prediction of the demand for elderly service beds. In the paper, an optimization method of Leslie matrix is proposed to predict the future trend of the total population and aging by comprehensively considering factors such as birth rate and mortality rate, and analyze and predict the demand scale of elderly service beds according to the distribution of urban and rural areas and economic divisions. Based on the AHP model, the business opportunities for elderly service beds will be transformed into a quantitative evaluation model to analyzing the return rate of pension service investment in different regions. The simulation experiment shows that increasing the total number of elderly care institutions and improving them can effectively solve the insufficient number of elderly service beds.

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has become an up-and-coming technique to provide additional computing support for smart mobile devices with limited battery capacity in the wireless communication network systems. In this paper, a multi-UAV-aided MEC system is modelled where UAVs are equipped with MEC servers and users can execute local computing and computation offloading to UAVs simultaneously. The total amount of processing bits of all users is maximized by the joint optimization of the user-UAV association, local processing frequency, transmit power, bandwidth allocation and UAVs' trajectories, meanwhile, the energy consumption constraint and the UAVs' speed constraint are satisfied. Because the formulated problem is not convex, it is challenging to get the global optimal solution directly. To iron out the non-convex problem, it is divided into three subproblems and solved by an alternating optimization algorithm. Simulations show that the algorithm introduced in our paper performs better than the local computing, full offloading and fixed trajectory patterns.

Electric submersible screw pump often works at high speed, which has the characteristics of high speed, high temperature and large rubber deformation. In case of sudden shutdown, the rubber will not shrink at high temperature, which may lead to serious accidents such as pump locking and electrode burning. In the initial stage of pump start-up and restart after pump shutdown, due to the above reasons, it is often difficult to start the pump, which makes the pump unable to work normally. Therefore, this paper studies the clearance value of the electric submersible screw pump, calculates and tests the interference amount and initial interference amount, improves and optimizes the clearance value of the electric submersible screw pump, effectively improves the working efficiency and service life of the electric submersible screw pump, achieves the purpose of improving the quality and efficiency of oilfield production, and has a very important practical significance for the development of the rodless pump market.

Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.

In order to meet the needs of matching Advanced Driver Assistance System (ADAS) map elevation data to ordinary map shape points, we propose a hierarchical point-position matching algorithm and interpolation algorithm. The main work is as follows: (1) Under the premise of considering different road scenes, the characteristics of road shape points on the ADAS map and road shape points on an ordinary map are fully analyzed, and elevation data are matched to road shape points on an ordinary map through graded matching calculation to obtain more accurate elevation value. (2) For the road shape points on the ordinary precision map which does not match the appropriate elevation value, the more accurate results are calculated by interpolation method according to the elevation value of the front and back order points, so as to achieve the coverage of the road elevation value on the ordinary map. Take the road of Beijing map as the test sample, and the results show that the algorithm is reasonable and effective through statistics and sampling analysis. In terms of the verification of matching coverage, the ADAS map road network points are successfully obtained or calculated the effective elevation value of 976835 points, with a matching rate of over 99.99% after the hierarchical point-position matching algorithm and interpolation calculation algorithm. Furthermore, the results of the practical application of feedback also show that the algorithm can satisfy vehicles daily for ADAS basic function application.

Aiming at the difficulty of extracting heart rate due to the relative movement between human body and acquisition equipment, this paper proposes a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDA) based on Normalized Least Mean Square (NLMS) adaptive filtering combined with fully adaptive noise sets for heart rate estimation. Firstly, an adaptive filter was used to filter the motion artifacts in the original signal by taking the triaxial acceleration signal as the reference signal. Then, the PPG signal was decomposed by CEEMDAN to obtain a series of Intrinsic Modal Functions (IMF) from high to low frequency. The Permutation Entropy (PE) criterion was used to determine the threshold range of the signal, so as to filter out the high-frequency noise and baseline drift. The results show that the Pearson correlation coefficient between the computed heart rate of PPG signal after noise reduction and the standard heart rate based on ECG signal is 0.742, and the average absolute error percentage is 6.08%, which indicates that the proposed method can accurately calculate the heart rate in the exercise state, and is beneficial to human physiological monitoring under the exercise state.

For solar system boundary exploration mission, this paper designed an X-ray pulsar-based navigation (XNAV) scheme. The Fisher information matrix is used to choose optimal pulsar observation configuration. Based on the selected pulsar configuration, the accuracy of pulsar navigation and stellar angle navigation is compared. Besides, the navigation performance of XNAV is verified by the orbits of voyager 1, voyager 2 and pioneer 10 respectively. Simulation results show that the pulsar navigation could provide accurate positioning results for solar system boundary explorers.

The mechanical structure of MEMS gyroscope is a spring-mass-damper system, which is susceptible to interference near the resonant frequency. This work compared the interference effects of mechanical vibrations and high frequency sound waves on MEMS gyroscopes. Three MEMS gyroscopes ADXRS620 were interfered by vibrations and sound waves near the resonant frequency. The error outputs increased linearly with the increase of interference intensity. The maximum error output could reach 88.95 °/s, which seriously affect the normal operation of the gyroscope. The waveforms of gyroscope outputs under acoustic and vibration interference were almost coincident, which showed the similarity of acoustic and vibration interference. However, to produce the same effect on gyroscopes, the power required of vibration interference was much less than that of acoustic interference. Taking one of the gyroscopes for example, when the SPL of acoustic interference was up to 90 dB, the maximum error was only 3.37 °/s. But when the acceleration amplitude of vibration interference reached 0.050 g, the maximum error was 3.42 °/s. In addition, the effectiveness against vibration interference of the filtering algorithm based on orthogonal demodulation was verified by testing the self-developed gyroscope. Vibration interference could be reduced by 98.88% at most.

In China, the high-speed rail network has developed rapidly in recent years. The wear of rail is a risk to its safe operation. Compared with traditional static detection methods, the methods applying computer vision have become one of the major detection methods due to its non-contact ability, high efficiency, and low cost. However, the accuracy of dynamic detection is affected by the flexible outdoor detection conditions, random vibration during the detection process and other undesirable factors. Thus, in order to improve the accuracy of rail wear detection under complex detection conditions, this paper develops a rail wear detection method based on particle swarm algorithm. The experimental results with line-structured light data show that the proposed method improves the accuracy of rail wear detection and provides technical references for the dynamic detection of rail wear.

Crowd sensing is a new type of data acquisition method that can efficiently and diversify the realization of sensing tasks. However, this method currently has some problems, such as data storage being overly dependent on third-party platforms, and there is a lack of reliable data credible evaluation methods. To solve this problem, our paper proposes a distributed incentive mechanism based on Hyperledger Fabric (HF-DIM) in the Crowd sensing scenario. In particular, the following questions are studied: How to achieve distributed incentive to solve the traditional incentive that relies on a centralized platform? How to evaluate the credibility of the sensing data provided by the users? To the former question, we implement a multi-attribute auction algorithm based on smart contracts, and distributed incentives are implemented using blockchain deployed contracts. To the latter question, We propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to evaluate the credibility of the data and establish a reputation index based on the credibility of the data. Through simulation experiments using real data set, the feasibility and effectiveness of the proposed framework and algorithm are verified.

Aiming at the problem of low link bandwidth utilization in the current FlexE client slot allocation mechanism, a FlexE calendar bandwidth allocation mechanism that is compatible with the client data transmission rate is proposed, and the LSTM algorithm is used to predict network traffic data, and according to the client priority and traffic prediction results, the client is allocated with slots. Experiments show that, compared with the traditional FlexE slot allocation mechanism, the method proposed in this paper can make the bandwidth resource utilization rate reach 82.8%.

In order to enhance the driving safety, various driving assistant systems are gradually developed and widely used. Based on the safe distance model related to the braking process, one warning/dangerous critical distance model for longitudinal control is proposed in this paper, considering the driver's style and environment factors. The variable driver response time is obtained by employing fuzzy control theory. The author then establishes the vehicle dynamics model and its inverse model. Through the relative distance error and relative speed error, the upper controller is designed to obtain the expected acceleration based on sliding mode control, and the lower controller based on PID control is designed to track the acceleration. In order to solve the chattering problem of sliding mode controller, the author proposes variable gain coefficient control method. The fixed gain parameter in the expected acceleration formula is related to the negative exponential correlation formula, which can effectively depress the chattering of the expected acceleration. Finally, the author uses CarSim and Simulink to establish a co-simulation model of the collision avoidance warning system, and designs the test simulation conditions for typical traffic test scenarios to verify the control algorithm effectiveness.

High spatial and temporal current resolution velocities were measured by use of an acoustic Doppler velocimeter (ADV) in the tidal bottom layer of North Passage Deep water Navigational Channel (DNC) of Yangtze Estuary in China. And the vertical profiles of mean velocity were measured by using a pulse-coherent acoustic Doppler profiler (PC-ADP). The measured data show the vertical profiles of mean velocity are almost logarithmic. For discussing, the turbulent kinetic energy (TKE) method based on the velocity for ADVs-based is chosen and a new logarithmic profile (LP) method ignoring the influence of complex variation of the parameter Z₀ is suggested to estimate the bed shear stresses respectively. The calculated results show that the bed shear stresses estimated by LP method are larger than the ones estimated by TKE method mostly in Yangtze estuary. At the moment of ebb peak, the maximum error can be about twice occurring and the error of ebb tide is almost greater than the one of flood tide. These phenomena are explained in this study by the deduction of formula for steady and unsteady flow. It is concluded that the vertical logarithmic profile of velocities will be influenced by unsteady flow and there is not the same bed shear stress even if there is the same logarithmic velocity profile in vertical. Generally, this has led to a necessary adjustment of the drag coefficient C_d of steady flow in flow model to avoid overestimating the bed stress for unsteady flow in estuary based on the observed data.

Uncapacitated Facility Location Problem (UFLP) is a NP-hard Problem that to determine the optimal Location of facilities. In this paper, the general Genetic Algorithm (gGA) is first introduced and adopted to solve a small case of UFLP (case1). Then an improved genetic algorithm (iGA) based on real coding is proposed to solve the UFLP problem. This paper mainly makes appropriate adjustments in the selection of fitness function, crossover operator and mutation operator to be more suitable for UFLP. Facilities thus can be roughly allocated according to the cost of facilities and the demands of customers. Case2 was calculated by several CAP data (CAP101, CAP103, CAP104, CAP131, CAP133, CAPB and CAPC) in OR-LIBRORY. The results proved that the iGA is feasible and effective, and it is found that 80% of the results obtained by the iGA are within 0.05% of the optimal solutions. Compared with several other common algorithms, the advantages of iGA increase as the scale of calculation increases. Finally, applying iGA to an on-site oilfield pipeline network, it was found that it can find the optimal solution in a short time.

Large hydropower plants in China are usually required to transmit power to multiple provinces and meet their complex peak-shaving demands. How to model and solve this problem is challenging. Based on a Hydropower Project in southwest China, this study proposed a method for day-head operations of large hydropower plant considering peak-shaving demands of two provincial power grids. A short-term peak-shaving model for multiple power grids is constructed, which takes the minimum sum of the squares of the remaining loads of each power grid. This model considers complex constraints such as the operation of hydropower plants and the requirements of power transmission. The multi-objective optimization is transformed into a single-objective problem and solved by a group search algorithm based on the genetic algorithm. Two cases which respectively consider different loads and objective weights are presented to test the model. It is found that the large hydropower plant can simultaneously respond to different peak loads for multiple power grids. It should be also noted that different objective weights may have a significant effect on peak-shaving results.

The operations of cascaded hydropower plants have always been recognized as a complex system optimization problem. The difficulty of modelling and optimization solution is further aggravated when comprehensive water demands for power generation, navigation and ecology are considered. This study develops an optimization model for operations of cascaded hydropower plants with navigation demands, and proposes a two-stage successive approximation method of dynamic programming. In the model, the navigation demand is considered as a constraint. The first stage is to optimize the operation of cascaded hydropower plants with no consideration of navigation demand. The optimized result is taken as an initial solution of the last stage. Moreover, the navigation constraint is introduced in the solution procedure to obtain a reasonable operation scheme. The method is implemented on operations of a cascaded hydropower plants with navigation task in Southwest China. The results show that the navigation demand can be satisfied by coordination of cascaded hydropower plants. Moreover, this constraint has an effect on the monthly distribution of total energy production during one year. In addition, the computing efficiency and solution accuracy are analysed by using different discrete steps.

Compared with a single type of power supply, hydro-wind-solar-storage multi energy complementary system has obvious advantages in active power regulation performance. However, there are also many new problems in terms of coordinated control of multiple types of power supplies and giving full play to the performance advantages of various power supplies. Considering the regulation characteristics of different power supplies and the requirements of shallow charging and shallow discharging of energy storage battery, this study proposes a joint active power control strategy of multi-energy complementary power supply systematically. The basic regulation of hydropower is used to supplement the output of wind and solar power plants, while the compensation regulation of energy storage power supply is used to improve the stability of total output. Additionally, hydropower is used to charge and discharge the energy storage battery. The simulation results show that the proposed strategy can effectively give play to the regulation advantages of different power supplies, and meet the requirements of shallow charge and introduction.

At present, electric vehicles have become one of the main directions for the development of today's automotive industry by virtue of their high energy utilization efficiency and low environmental pollution. With the popularity of electric vehicles, the recycling of their batteries will become a challenge in the future. At present, the recycling system of vehicle batteries is not perfect. How to evaluate different quality of batteries to decide whether to reuse them or to remanufacture them directly is a question worth exploring. The purpose of this paper is to discuss the quality grading strategy of used batteries and make decisions based on the evaluation results by constructing a game theory model that includes four parties: new energy vehicle manufacturers, consumers, battery manufacturers, and recyclers, with the goal of maximizing the utility of the parts in the closed-loop supply chain of electric vehicles. This study is beneficial for the government and managers to better understand the electric vehicle battery closed-loop supply chain, and for the sustainable development of the supply chain as well.