Table of contents

The 2021 International Conference on Computers, Remote Sensing and Aerospace (CRSA 2021) was taken place on July 23-25, 2021 via online platform due to the pandemic of COVID-19. In light of the prevention and control of COVID-19 and travel restrictions worldwide, the organizer could not hold the conference physically, that's why we have to adjust the format to online meeting to avoid personnel gatherings, so that the conference could go ahead as scheduled.

This conference mainly focused on the latest research on "information science" and "artificial intelligence", and aims to gather experts, scholars, researchers and related practitioners in this field from all over the world to share research results, explore hot issues, and exchange new ideas, experience and technology. We warmly welcome experts and scholars in related fields to submit their new research or technical contributions to CRSA 2021, and share valuable experiences with scientists and scholars from all over the world!

During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. During the conference, we were pleased to had Prof. Yuanzhu Chen, from School of Computing, Queen's University, Canada as our Conference Chair.

In the second part, we invited five professors as our keynote speakers. Prof. Yuanzhu Chen, our beloved Conference Chair, performed a speech: Fading Networks. Prof. Johan DEBAYLE, from Ecole Nationale Supérieure des Mines de Saint-Etienne, France. He presented an insightful speech: Morphological image processing for remote sensing applications. Our third keynote speaker, Prof. Devinder Yadav, from Aerospace / Faculty of Science and Engineering, University of Nottingham Ningbo China, China. His publications in scholarly international journals cover interdisciplinary areas, such as aviation regulations, flight safety, aircraft personnel licensing, airport engineering, higher education, airworthiness, aircraft maintenance, aviation management, and project management. Associate Professor. D. Jude Hemanth, from Karunya University/ Department of Electronics and Communication Engineering, India. He delivered a wonderful speech: Why Deep Learning techniques over conventional Machine Learning techniques? An introspection. Assoc. Prof. Anwar Ali, our finale keynote speaker, from Zhejiang Sci-Tech University, China. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.

The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Computational science, Remote Sensing, Aeroacoustics Science and more related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.

We would like to acknowledge all of those who supported CRSA 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers.

The Committee of CRSA 2021

Committee member are available in this pdf

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information:

• Type of peer review: Double-blind

• Conference submission management system: AI Scholar System

• Number of submissions received: 125

• Number of submissions sent for review: 121

• Number of submissions accepted: 73

• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 58.4%

• Average number of reviews per paper: 2

• Total number of reviewers involved: 40

• Any additional info on review process:

• Step 1. Each of selected paper will be reviewed by two/three professional experts in the related subject area.

• Step 2. Review Reports received from the experts will be judged by one of the editors either Review Reports are logical or not?

• Step 3. If not logical, then editor can assign new reviewer or can also judge at his/her own.

• Step 4. If logical, then Review Reports will be sent to authors to modify the manuscript accordingly.

• Step 5. Authors will be required to revise their papers according to the points raised.

• Step 6. Revised version will then be evaluated by the editor for the incorporation of the points raised by the reviewers.

• Step 7. Then the editor will send the revised manuscript to the reviewers again for re-evaluation.

• Step 8. If the reviewers approve the revise version of the manuscript, then will be accepted for publication.

• Contact person for queries:

Xuexia Ye

publication@keoaeic.org

AEIC Academic Exchange Information Centre

The influence of nearshore mutational terrain on wave was experimentally studied via local-global physical wave model. It was discovered that the influence of submarine topography on wave was mainly manifested by the change in wave height, which was especially apparent when the submarine slope gradient changed greatly and presented approximate mutation. Moreover, the differences of different wave directions and different wave elements in influencing the wave heights with different accumulative frequencies were presented. Therefore, the influence of nearshore mutational terrain on wave should arouse high attention in the engineering construction.

The modelling of chiller performance is critical for chiller optimal control, and fault detection and diagnosis (FDD). Different kinds of chiller models including sophisticated mechanistic models (white-box models), purely data-driven models (black-box models like artificial neural networks, ANN), and semi-physical models (grey-box models like empirical equations) have been proposed and tested. Due to the development of machine learning techniques, data-driven models have become more popular recently. The performance of the established data-driven model (accuracy, robustness, generalization) could significantly affect the model application. To enhance the model performance, a lot of studies have been carried out on investigating and modifying the model structure. However, the influence of the data quality on model training has not been sufficiently studied. When adopting historical data to train models, the data distribution is highly correlated to the control logic. So how does the control logic influence the establishment of data-driven chiller models by affecting the data distribution? In this study, experiments are conducted on an air-cooled chiller under model-free stochastic control to acquire rich and variable operational dataset; then the dataset is grouped into three corresponding to different chiller control logics. Finally, three models trained by three training datasets are evaluated, and the results suggest that when establishing data-driven chiller models, preliminary stochastic operation is cost-effective to acquire rich data for robust chiller modelling.

Remote sensing monitoring has become an important means to evaluate urban water status, which plays the key role in maintaining the balance of urban ecological environment. In this study, based on the Sentinel-2 MSI sensor data, an inversion model of water quality parameters was established to monitor the concentration of chlorophyll a and suspended matter, and turbidity in some sections of the Jialing River. The results show that the inverted water quality parameters in this study are all within the normal range, and remote sensing technology can be effectively applied to regional water quality monitoring.

Two cumulus convection parameterization schemes, i.e., Kain-Fritsch (K-F) and Grell-3 based on the V4.1.2 Weather Research and Forecasting (WRF) model, were used to simulate the summer precipitation in the Weihe River Basin using hourly precipitation data at the resolution of 5 km and 2 km. the precipitation experiment results at different resolution were compared and analyzed. The root square error and correlation coefficient were used to verify and evaluate the simulation results. The results show that in K-F and Grell-3, the two cumulus model simulations with a resolution of 5 km can explain the spatial distribution pattern of precipitation and the diurnal variation process, and the experimental simulation results are consistent with the actual observations. The summer precipitation simulation in June, July, and August is highly sensitive to the two cumulus parameter schemes of K-F and Grell-3. The simulated precipitation value is higher than the actual value, and the root mean square errors (RMSEs) of K-F and Grell-3 schemes are 5.49-13.29 and 5.69-10.88, respectively. In terms of precipitation simulation, Grell-3 scheme is better than K-F scheme, especially in areas with heavy rainfall. When the resolution is increased to 2 km, the regions with heavy precipitation can be displayed more finely. The influence of water vapor flux and vertical velocity changes on precipitation in the Weihe River is analyzed from two aspects, i.e., water conditions and atmospheric instability. From the simulation results by the K-F scheme, the vertical velocity fluctuations are unstable and strong in the convective area, and the convective rainfall is relatively high. From the simulation results by Grell-3, the vertical velocity is large and increased rapidly, which is conducive to the generation of large-scale heavy precipitation. Both schemes are very sensitive to the simulation of water vapor transport and vertical velocity. This study provides a basis for the research on the adaptability of regional precipitation simulation.

With the increase of human demand for natural climate research, climate monitoring technology has been greatly developed, and the occultation technology proposed in the field of surveying and mapping provides a more accurate and effective data source for meteorology research. With the destruction of the earth's ecosphere by pollution, the global climate change has a great impact on human life, so the research on meteorology has been paid more and more attention. The data obtained from GPS radio occultation observations have also become one of the key data sources to record the temperature changes of the atmosphere in tropical regions. The extraction and analysis of temperature data in the western Pacific are very essential. At the same time, it plays an crucial role in China's climate prediction and disaster warning. It is hoped that by reading the occultation data of the western Pacific in 12 months in 2014, and drawing the isoline map of the lunar season and seasonal temperature values, the change law of global and western Pacific temperature with longitude, latitude and altitude can be analysed.

The deterioration of the groundwater safety may lead to a series of ecological and social problems. In this study, we select relevant hydrogeological and anthropogenic parameters to construct the groundwater safety evaluation method for the lower plain of the Liaohe River based on the improved DRASTIC model. By spatially weighted overlay and Getis-Ord Gi* analysis of groundwater safety distribution maps, the main governance regions and main factors causing groundwater deterioration were identified. On this basis, the evolution trend of the groundwater key management area was quantitatively analyzed using the standard deviation ellipse (SDE) method. The results show that groundwater safety in the north and south of the lower plain of the Liaohe River are continuing to deteriorate. The correlation test between the groundwater safety index and the measured nitrogen concentration verified the scientific accuracy of the proposed groundwater safety evaluation method.

To manage the computing resources efficiency is crucial for the Cloud data centers (CDCs). By predicting the resource usages of certain virtual machine (VM), the workload could be balanced before the resource overusing occurs. In this paper, we propse an attention-based time series prediction model, which contains an encoder of input attention mechanism and a decoder of a temporal attention mechanism, to optimize the efficiency of could data center. Experiments on Alibaba CDC VM trace dataset demonstrates that our proposed methods can outperforms the classic LSTM method, especially when the resource usage data is lack of relationship.

At present, the ability of defect detection system to detect the position of semiconductor plastic packaging defects is poor, so a defect detection system of semiconductor plastic packaging based on machine vision is designed. In terms of hardware, based on machine vision technology, the system framework is determined, the camera, lens and light source models are selected, and the detection target image acquisition parameters are determined. In terms of software, according to the target image obtained by machine vision technology, the image is preprocessed, segmented and feature extracted to identify and detect the target image and detect the semiconductor plastic packaging defects. Experimental results: compared with the system selected in this experiment, the design system can accurately detect the defects in the semiconductor plastic packaging and the location of the defects.

This paper presents a multi-level digital watermarking algorithm suitable for raster geographic data. First, the new problems caused by the multi-level distribution of raster geographic data and its impact on watermark embedding/detection are analyzed; and then, a watermark information segmentation mechanism is designed, the multi-level watermark embedding strategy is established on this basis, thereby building the multi-level digital watermarking algorithm of raster geographic data based on the watermark segmentation mechanism with the traditional robust digital watermarking algorithm as the prototype; finally, the experimental comparison and analysis of the multi-level watermarking algorithm and the prototype algorithm are made. Results show that the proposed multi-level digital watermarking algorithm not only maintains the performance of the prototype algorithm, but also effectively solves new problems caused by the multi-level circulation of raster geographic data, such as multi-copyright protection and multi-user tracking, and has a high practical value.

At present, due to the variety of product models and the increasing production data, the existing single machine based tester can not meet the needs of production. This paper introduce how to design and develop a centralized measurement and control system for servo products through standardized design and craft configuration.

With the rapid growth of the global economy, environmental resources have also been severely consumed and destroyed. With the rapid growth of China's economy, domestic environmental pollution and environmental damage have become increasingly serious. In order to understand the changes in the urban greenness of Chinese cities, the changes in the urban greenness of coastal cities in China in the past five years have been explored by using Landsat satellite remote sensing image data from multiple periods since 1973. The reason for choosing coastal cities as the research objects is that the coastal cities have relatively developed economy and have certain demonstrative significance for both themselves and the development of inland cities. This article first popularizes the domestic urban environmental conditions and knowledge of remote sensing technology, and then analyzes the process of obtaining urban greenness sentinel data and Landsat data of coastal cities from remote sensing data, and finally analyzes the urban greenness of coastal cities in the past 5 years. The increase change and the average increase change are analyzed and compared, and corresponding conclusions are given based on the analysis results.

In order to meet the application requirements of deep learning detection and recognition algorithm for Field Programmable Gate Array (FPGA) computing structure, this paper proposes a machine learning optimization algorithm of convolutional neural network (CNN) based on pruning and Int8 quantization. By analyzing the machine learning intelligent recognition network, the Yolo v3 target detection network is selected to verify the optimization algorithm. Aiming at the Yolo v3 network with balanced speed and precision, the optimization algorithm is studied in detail. Based on Amazon Cloud FPGA EC2 instance platform and ZCU104 FPGA hardware platform, machine learning optimization algorithms are used to accelerate the application of Yolo v3 network detection and recognition. Satisfactory results are obtained on both FPGA computing platforms. The experimental results show that the machine learning optimization technology can improve the inference speed of neural network based on both FPGA platforms. The network structure optimized by pruning and Int8 quantization algorithms can achieve high acceleration speed, with very limited accuracy loss.

This paper studies the relationship between the fault modes of hydraulic system and their flying parameters according to the principles of hydraulic systems. Based on the study of the working principle and composition architecture of the hydraulic system, the AMEsim software is used to construct a simulation model of the hydraulic system, to analyse the fault modes and mechanisms of key components in the system and their failure effects, and to establish a link between the fault modes and the flying parameters according to which practical evaluation parameters are provided for the subsequent monitoring and handling of system failures.

To achieve the intelligent management of drugs in pharmacy, a remote data exchange system is designed. Based on Labview, the software of doctor side and pharmacy side is designed. According to the demand of smart pharmacy, the software are divided into various functional modules, and then the workflow of each software module is designed. By using TCP/IP protocol, the remote communication between the doctor and the pharmacy is realized. By using th Access database technology, it can realize the functions of data storage and data real-time display in the whole intelligent pharmacy system. Finally, experiments were performed and the results showed that the doctor can issue prescriptions through this system; Pharmacy can automatically receive prescriptions, and update it to uniform drug database.

In order to provide a reference for the safe buried depth of underwater pipeline in the navigable waters, this paper on the basis of the famous penetration formula, studied the calculation model of ship's anchor dropped to seabed. The model considers the main factors that affect the penetration depth, such as the type of anchor, the weight, the speed of bottoming and the substrate condition. The model makes clear the bottom coefficient values, shows the method of hall anchor's velocity of touching bottom and cross sectional area, and combines with the existing cast anchor experiment results to determine the shape factor of anchor. Using the calculation model, typical scenario was calculated and the calculation result of different methods were compared. The results show that the calculation model is better ease and compliance. It is very important to study the factors affecting the penetration of ship anchor and to determine and evaluate the buried depth of underwater pipeline in navigable waters.

The research mainly focused on the effect of staff density and exit conditions on total evacuation time. The results showed that with the increase of staff density, evacuation time often increases non-linearly. The function equation of evacuation time and number of occupants in case A was obtained through computer simulation. The augment of exit number or total export width can reduce the total evacuation time to improve evacuation efficiency. While, if the number of evacuees reaches to a certain value, evacuation time may increase non-linearly after being affected by the internal structure and the random distribution of occupant. Export position also has impact on total evacuation time. The results of the simulation have guiding significance on the design of building structures and personnel evacuation in emergency.

Big data service is the key to the construction of smart grid, and the method of analyzing electricity consumption behavior based on improved AP clustering and the method of forecasting electricity load based on random forest is proposed. To address the problem of high complexity of AP clustering analysis, the entropy weight method is used to establish the index weights and improve the similarity calculation method to realize the fast and accurate analysis of customers' electricity consumption behavior. For the problem of power load prediction, the fuzzy C-means method is used to construct the index weights. The fuzzy C-means is used to construct the historical similarity daily sample set, and the random forest is used to predict the electric load. The SPACK multi-level data processing framework is proposed to meet the needs of parallel processing speed for large amounts of data. The simulation results verify the effectiveness of the method proposed in this article and the important guiding price for the Power grid.

With the continuous development and progress of society, people's requirements for living conditions have gradually changed. They begin to pursue the comfort and personalization of the living environment, while also pay more and more attention to the problems of energy and environment. In order to build a suitable living environment, the implementation of central heating is bound to consume all kinds of resources. In the heating process, the efficient use and effective saving of energy has become an important measure for the heating industry to achieve the coordinated development of energy and environment. Taking the central heating as the research object and the secondary side distributed variable frequency pump heating transmission and distribution system as the goal, this paper studies the energy saving of the distributed variable frequency pump heating transmission and distribution system. This paper provides a design method of distributed variable frequency pump heating transmission and distribution system, in order to provide some basis for the design of distributed variable frequency pump heating transmission and distribution system in the future, and solve the problem of high energy consumption in the traditional central heating transmission and distribution system.

The permeability coefficient of most cutoff walls with plastic materials is less than 10⁻⁶ cm/s, it exceeds the measurement range of the existed devices. For the permeability coefficient testing for cutoff walls with low permeability, there are complex in the automatic system of pressure control, difficult in the keeping stability of hydraulic pressure, high in the measurement precision, leaky in the contact surface between the fixing apparatus and the measured specimen. Firstly, based on the characteristic analysis of cutoff walls, the hydraulic pressure was accurately controlled when the valve of atmospheric pressure control with high precision worked in with the electromagnetic valve, the hydraulic pressure was controlled. Secondly, the shape of inner space for the fixing apparatus of the specimen is developed, the leakage of the contact surface between the fixing apparatus and the specimen was greatly decreased. The test showed that the semi-automatic apparatus could realize automatic control of the hydraulic pressure accurately with the combined action of an electromagnetic valve and a pressure reducing valve, the leakage of the contact surface could be basically avoided. The testing apparatus can be used in penetrability tests and detection of project quality for cutoff walls with low permeability.

Machine learning can make predictions on unseen data by extracting information and constructing algorithms and statistical models from the training data, which is highly desirable for speech recognition and computer vision. Quantum computing harnesses the phenomena of quantum mechanics realizing parallel computing, which could solve certain computational problems, such as RSA encryption significantly faster than classical computers. Quantum machine learning is reasonable to enable machine learning faster than that of classical computers by utilizing quantum operations. Binary classification is a typical quantum machine learning task applied to single qubits. Here we present a binary classifier for regions. Firstly, we sample two sets of quantum data points located in the X-Z plane of the Bloch sphere randomly. Then we utilize a single parameterized rotation gate and measure the qubits along the Z axis. We train the hybrid model combined by quantum mdoel and classical Neutral network and regard the corss entropy the predictions of the classical Netural network and labels as the loss function. Based on the above, we present the results of the trained hybrid model to classify new quantum qubits.

To solve the problem of low ratio success delivery, long latency caused by the rapid dynamic change of network topology, limited node buffer, large amount of data transmission and large user population density in delay-tolerant network (DTN). This paper propose a routing algorithm in DTN based on nodes' sociality. In this paper, contact history was used to determine the node link quality; Markov model was used to predict the node position based on the nodes' movement track; according to nodes' social similarity to divide community, then calculated node betweenness centrality. Finally, the best forwarding nodes for message transmission were determined. Besides, to increase the ratio of successful delivery, message delivery probability in the relay node's buffer was calculated. Experimental results show that compared with traditional routing algorithm, the proposed algorithm performs better on the delivery ratio and average end-to-end delay.

The short term air quality index can usually be predicted by statistical and numerical methods, but for the multi-point prediction of AQI, the traditional methods are often inaccurate. In this paper, a new hybrid multi-point prediction method was proposed by combining K-means clustering with the circulating neural network long and short time memory (RNN-LSTM) model. Based on this prediction method, the air quality index in Dezhou was predicted 1-5 days in advance by using 28 multi-point pollution monitoring sensor data from January 1, 2018 solstice to August 31. The prediction results show that the model not only improves the accuracy and effectiveness of the prediction, but also reveals the relationship between land use patterns and air quality index (AQI), which provides important information for land use planning, air pollution mitigation and urban intelligent governance.

In recent years, the problem of atmospheric pollution has received more and more attention. Combining the concentration data of various air pollutants monitored by the air quality monitoring stations in Nanning, Guilin, and Baise in Guangxi province in 2017 and the precipitable water vapor (PWV) data obtained by sounding stations in the three cities, analyzed the changes of PM_2.5 and PWV in major cities in Guangxi and build the multiple linear regression-differential autoregressive moving average (MLR-ARIMA) models respectively make short-term predictions for the changes in PM_2.5 concentration in the three cities. Among them, the mean absolute error (MAE) of the prediction results of Nanning, Guilin and Baise are 7.57μg/m³, 12.75μg/m³ and 7.67μg/m³, compared with the multivariate linear regression model and the neural network model, the prediction accuracy of this model in Nanning is 43.55% and 46.50% higher than that of the multiple linear regression model and neural network model, respectively, and in Baise is 21.41% and 26.32% higher accordingly, The model prediction effect in Guilin is optimal for the neural network model, which improves 24.46% and 11.84% compared with MLR and MLR-ARIMA models, respectively, where MLR-ARIMA model still has 14.31% accuracy improvement compared with MLR model. This study has some reference value for PM_2.5 prediction work in major cities in Guangxi, China.

According to UN FAO, global food production in 2019 reached 2.722 billion tons, showing high profitability and efficiency levels^[1]. However, while we enjoy fresh and nutritious food, people in Africa and Central Asia are suffering from starvation and nutritional deficiencies. Despite the high profitability and efficiency levels, the existing food systems are challenged by equity and sustainability problems. In order to assess the current food system and propose a rational optimization approach, we propose a linear and hierarchical food system based on the priorities of food flow and distribution. To quantify food security based on food systems, we develop the AAQN Index system to measure the affordability, availability, quality& safety, and natural resources& resilience of a food system based on the Global Food Security Index (GFSI). We select 68 indicators and calculate the indicator weights by combining the entropy weight method (EWM) with GFSI. By systematically clustering the calculated results, we classified the food systems of 113 countries/regions into: excellent, normal, and poor. Then, we adjust the priorities of the Indian food system, a poor food system, to reveal the differences before and after optimization. Finally, by predicting the future AAQN Index of Indian through Grey Relational Analysis.

The ability of the ocean to provide a good thermal environment and not take up land space resources has led to subsea data centres due to their energy efficiency, low failure rates and green sustainability. As a result, there is a growing need for subsea data centres to be structurally sound. This paper investigates the optimal design of data centre capacity and cooling structures to improve their thermal safety performance to a certain extent.

Firstly, the temperature quantification of the heat dissipation and heat generation processes is carried out using Newton's cooling theorem and Fourier's law of thermal conductivity to provide maximum and minimum constraints for optimization. Then, the " five-block method" is used to optimize the arrangement of internal servers with temperature constraints, considering the problem of packing same-sized items in closed spaces. Based on the quantification of the solid-liquid and solid-solid heat transfer processes and the local temperature analysis as well as the optimization of the boxing, the model yields a maximum of 384 servers for the data center.

Secondly, the fin structure is then further optimized for heat dissipation. An approximate model of the fin structure and thermal performance is developed and solved by a radial basis function (RBF) neural network algorithm. Then, based on 100 sets of parameters for the serrated fin structure, the RBF approximation model is used to optimize the range of values for height, pitch and thickness. Based on the three ranges of parameters, Monte Carlo simulations are used to generate different combinations of parameters for different shapes, and to obtain the exact parameter that will increase the surface area by 13.8%. In the case of the first task, a solution is obtained to store 396 servers.

The immune function of the middle-aged and elderly groups is decreased, and they are easy to be infected with the virus, and once infected, they are easy to be turned into acute and critical patients with a high mortality rate. In addition, China's aging population is becoming more and more serious, and the middle-aged and elderly population is a high incidence of all kinds of respiratory or cardiovascular system diseases. Most middle-aged and elderly people have more than one chronic disease at a time and need medication to treat it, and the type and amount of medication taken daily is often variable. However, due to the common problems of memory loss and blurred vision among middle-aged and elderly people, the various number and types of medication may lead to the possibility of the elderly taking it by mistake, missing or wrong, which may easily lead to the hidden danger of medication safety. This design will be health inspection management, medical interconnection, pharmaceutical affairs management, three main feature set in one, its realization storage service remind, interactivity, and index detection, do medicine detection and the sharing of data, realize the visualization of medical Internet, providing personalized family health management services, and the spread of interactive public medical basic knowledge.

Due to the existence of many interference factors, the recognition rate of the existing power decommissioning material management system is low when reading material labels, so a power decommissioning material management system based on RFID technology is designed. In the hardware part, RF transmitting circuit and receiving circuit are designed to collect and process the specific information of decommissioning material tags in real time. In the software part, the main performance requirements of the system are determined according to the business process of power decommissioning materials management. Radio frequency identification technology is used to identify the communication tag of decommissioning electrical materials, and the system function module is designed to realize the data transmission between the system function module and the non-contact communication tag. The experimental results show that the label recognition rate of the system designed in this paper is 10.08% and 6.17% higher than that of the management system based on one-dimensional bar code recognition technology and twodimensional bar code recognition technology, respectively. The system has good ability of reading and identifying the labels of decommissioning materials, which is conducive to improving the information management level of power enterprise.

As one of the main technologies of 5G, device-to-device (D2D) technology directly transmits data between mobile terminals in the adjacent area, and there is no need for base station relay equipment to transfer, so as to realize high data rate transmission, improve communication capacity, reduce transmission delay, and make full use of wireless spectrum resources. With the support of D2D technology, cellular wireless communication can be avoided between devices, so that wireless communication can be realized without occupying the bandwidth resources. In this paper, with the support of D2D technology, firstly, the transmission path optimization of D2D technology is realized by artificial swarm algorithm. Secondly, the simulation is carried out by MATLAB software. Finally, the path optimization of D2D communication is realized by simulation, and the test results are compared and analyzed.

This paper presents an enhanced second order prediction (ESOP) algorithm using the statistics of image difference signal. The algorithm can selectively control the residual of the first inter prediction for the second intra prediction. Unlike other second order prediction (SOP) algorithms, our proposed algorithm employs a check-strategy to adaptively select the residual of the first inter prediction for the second intra prediction so that the correlation between residual pixels can be kept close to the allocated accurate prediction as much as possible. Experimental results show that under the same condition, on average, the PSNR is increased by 0.40dB and the bit rate is reduced by 6.15% as compared with traditional second order prediction for QCIF and CIF sequences. This selective algorithm is particularly suitable for dealing with video contents containing complex motions in video compression.

Aiming at addressing the contradiction between the information timeliness and on-board computing capacity and data transmission capacity in on-orbit image processing system of small commercial remote sensing satellites, a convolutional neural network based target detection algorithm is proposed. The calculation amount of the model is optimized to 25MFLOPS by using transfer learning, sparse training and weight quantification, while the weight file data is reduced from tens of Mb to 0.5Mb, which makes it possible for small commercial remote sensing satellite to detect target on-orbit quickly, and meets the requirements of the timeliness of remote sensing information.

Super-resolution reconstruction is an imaging method to improve image resolution. It refers to reconstruct a clear high-resolution image from a low-resolution image. High-resolution remote sensing images can provide more detailed information and higher density, but in the field of remote sensing, because of the limitation of the hardware and vast distances, the remote sensing images are fuzzy sometimes. To facilitate subsequent tasks, this paper proposes a multi-channel feature extraction generative adversarial remote sensing image reconstruction method. According to the characteristics of remote sensing image, a generator is designed, which adds Laplace operator to enhance the edge information of the image, and uses multi-channel feature extraction, which not only enhances the ability of feature extraction but also reduces the number of parameters. In this paper, the super-resolution reconstruction task is carried out based on the 2X magnification factor, and the experimental results are evaluated on SET5/14 and NWPU-RESISC45 dataset. The experimental results show that the images generated by this method have a higher detailed texture and better super-resolution reconstruction effect of remote sensing images.

We introduce a new ship target association method called SRM-AT, which stands for a Ship Radar Matching Method Based on Target Attributes and Point Pair Topological Characteristics. Using the radar properties of electromagnetic signals, as well as the longitude and latitude coordinates of electromagnetic signals and remote sensing images, this method calculates the point pair properties probability and PPTC (Point Pair Topological Characteristic) probability of electromagnetic signals and remote sensing images, as well as the syntheses of BPAF (Basic Probability Assignment Function) of the two through D-S evidence theory, to update the probability matrix of target association between electromagnetic signals and remote sensing images by using probability relaxation labelling method iteratively. The main contributions of this paper are: firstly, using decision trees to classify electromagnetic signals; finally, at the same time applicable to electromagnetic signals and remote sensing image ship formation and non-ship formation form. Through comparative experiments, this paper verifies that the method in this paper is superior to the comparative method in terms of association accuracy.

In recent years, the convolutional neural network (CNN) has had a wide application in hyperspectral image (HSI) classification. HSI has many spectral and spatial features, which is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. Therefore, this paper proposes a classification method with CNN, which uses attention-enhanced spectral and spatial features (CNN-ASS). First, we use spectral and spatial subnetworks to extract spectral and spatial features. At the same time, spectral attention and spatial attention are added to the two subnetworks, respectively. Then, we sum the weights of the classification results of the two subnetworks to get the final classification result. This paper conducts experiments on three typical hyperspectral image data sets, and the experiment results show the CNN-ASS has a competitive advantage compared with some advanced methods.

Absrtact: There are a large number of power system equipment. The realization of smart grid requires effective collection and comprehensive judgment of the information collected and transmitted by many scattered equipment. Wireless communication technology plays an important role in it. The information interaction of each intelligent device needs to adapt to different platforms and protocols. This paper analyzes the problems existing in the current power peer platform, and provides corresponding strategies and solutions for the construction of intelligent power Internet of things communication platform, so as to efficiently realize the adaptation of platforms, systems and protocols, and realize the unified access and effective management of devices.

With the rapid development of intelligent transportation systems (ITS) and location-based services (LBS), it is more important to match the original trajectory sequence generated by users/vehicles to the actual road network. Most of the existing online map matching algorithms are based on the idea of local processing, or require richer data input and more mathematical models to ensure matching accuracy. This paper presents a simple and effective map matching method, called self-adjusting online map matching (AOMM). The algorithm is developed based on hidden Markov model (HMM). Considering the topological and geometric properties of the road network, the emission probability and transition probability calculation formulas of HMM are defined. And three adjustment strategies are provided to deal with trajectory noise points, dense trajectory points, and offset trajectory points. The algorithm only needs latitude and longitude information of trajectory points, and can match point by point. Experimental results on open trajectory data show that the algorithm has high matching accuracy and low output delay, and can meet the requirements of general online map matching tasks.

Small object detection is a challenging research direction in the field of computer vision, due to the low resolution and restricted information of small objects. At present, the general detectors only use appearance features to classify and locate objects, but they are prone to failure under the interference of background noise. On the other hand, the detector based on deep neural network has excellent performance on large scale, but it is difficult to extract enough information of small objects. This paper proposes a feature enhancement network (FENet), which contains two modules. The Residual feature enhancement (RFE) module combines residual learning and sub-pixel convolution to improve the resolution of input small objects and remove image noise. The Attention Feature Pyramid (AFP) module integrates the feature pyramid and attention mechanism, which can extract context information and filter redundant context information. At the same time, considering the imbalance of the contribution of large and small objects to the loss function during the training process, a feedback-driven function is introduced to solve the problem of uneven loss under multiple scales. Experimental results show that compared with the existing small object detection methods, our method has better performance.

With the development and maturity of science and technology and the arrival of the Internet era, instant messaging software has become an indispensable communication method in people's life due to its comprehensive functions, high performance and high stability. However, due to the open, interactive and global nature of the Internet, there are a series of security problems that cannot be ignored, such as information leakage and virus transmission. Information security is particularly important to us and directly affects whether our personal interests can be effectively protected. Therefore, strengthening information protection and avoiding information leakage are important issues that need to be solved urgently. This paper designs a system based on hybrid encryption by using symmetric encryption original AES and improved symmetric encryption AES to ensure the efficiency of people in the process of information transmission and communication.

NetCDF network general data format is a common data format used to store meteorological data such as ocean current field. However, the ocean current data in NetCDF format can not be read directly on the web for dynamic visualization. To solve this problem, this paper takes the Pearl River Estuary area as the research area, adopts the sparse algorithm based on local Moran's I index and improved point center buffer to dilute the ocean current data, converts the ocean current point data into particle data by using the vector synthesis calculation method of ocean current data, and designs specific JSON data structure for data storage. After completing the pre-processing of the visualization data of ocean current based on particles, a dynamic visualization display platform of ocean current particles is built on the Web side, and the JSON data of ocean current is read. The space-time dynamic visualization effect of ocean current data in the form of particles in the format of NetCDF is realized, and the static and intuition problem of ocean current visualization is solved.

The regional disaster scenario can be described by the disaster consequence and its evolution trend: the state of the disaster bearing body directly reflects the disaster consequence, and the relationship between the disaster bearing bodies leads to the complex and diverse evolution trend of the disaster consequence. Therefore, taking the disaster bearing body and its association as the core, a network model reflecting the characteristics of the disaster affected area is constructed to describe the regional disaster scenario. Based on the spatial distribution of potential hazards, the characteristics analysis method of regional geological hazards is proposed. This paper analyzes the regional elements of scenario architecture from a systematic point of view. Based on the evolution attribute of disaster bearing bodies, the influence range model of disaster bearing bodies and the influence topological association between them are defined, and the generation method of association network model of regional disaster bearing bodies is proposed. Using this method to construct disaster scenarios reflecting the characteristics of the affected area in advance is conducive to the assessment of the risk of regional disaster loss in advance.

Aiming at the problems of less bands of high resolution remote sensing image data and limited learning richness of model features, this paper proposes a high resolution remote sensing image classification algorithm based on improved full convolution neural network. Firstly, a standardization layer is added to batch process the image, and then a pooling index is added to the image to realize the up-sampling. Finally, the pooling index, the transposed convolution and the convolution eigenvalue are combined into a feature group to restore the class pixels of the image to a great extent. It can improve the prediction ability of the model. A simulation experiment is carried out to verify the effectiveness and feasibility of the proposed algorithm.

The Antarctic ice sheet change is one of the key factors affecting global climate change. The influence of climate factors obscures the understanding to elevation change of the ice sheet. Due to the lack of understanding of the quantitative relationship between climate events and elevation change of Antarctic ice sheet, we here combined climate factor data to quantify the impact of climate events on the elevation change of Antarctic ice sheet. A strong correlation between climate factors and elevation change of ice sheet was found. And the influence between them exists time delay in some extent. Meanwhile, the influence of climate factors in the Antarctic ice sheet change was separated from the ice sheet change based on the Independent Component Analysis (ICA) and multivariate linear regression model. The study also indicates that the influence of climate factors may be an important contribution for the accelerated decline of ice sheet elevation in the Amundsen Sea.

In view of the slow rendering speed and poor effect of 3D visualization of marine environment field, this paper studies the 3D visualization of vector field and scalar field of marine environment based on particle system, second-order Euler integral algorithm, linear interpolation method and 3D GIS visualization technology. Firstly, the particle motion model is constructed based on the second-order Euler integration algorithm, and the multi-scale transformation model is constructed based on the perspective perception, and the multi-scale visualization model of the vector field of marine environment is established. Secondly, the mesh is generated by bilinear interpolation, and the color mapping model and the hierarchical color setting scalar field visualization model are established. Finally, based on the technology of 3D GIS, the 3D visualization of marine environment field is realized, and the rendering speed and rendering effect are improved, which provides the basis for the efficient utilization of marine environment information.

In view of the traditional manual and semi-automatic methods can not quickly and effectively extract control points, this paper uses five-layer fifteen-level tiles (FLFLT) as reference images, and proposes an efficient and automatic method for automatic extraction control points of GF-7 image. Firstly, the remote sensing image and reference image are sampled down, and the remote sensing image is partitioned to improve the image processing efficiency. The Harris algorithm is used to extract the feature points of the remote sensing image and reference image, the normalized cross-correlation (NCC) algorithm is used for feature matching, and the Random Sampling Consistent (RANSAC) algorithm is used for gross error elimination. Finally, the least-square algorithm was used to fit the geometric transformation parameters, and the geometric transformation model was used to carry out geometric correction of GF-7 image, and the better correction results were achieved. Experimental results show that the proposed algorithm can extract control points quickly and effectively, and can be used for automatic extraction and geometric correction of high resolution satellite data.

At present, the management of high-standard farmland is only managed at the county level, and data reporting is done by the county in the form of reports, which cannot form a unified data sharing model, resulting in low efficiency in daily management. The comprehensive management system of high-standard farmland designed in the thesis can completely solve the problem of non-uniform management and provide a comprehensive management, query and statistical summary platform with unified data and intuitive image.

In the past few decades, the tidal flats along the Jiangsu coast have been extensively modified, which has affected the function of a tidal flat ecosystem. In this paper, 4556 Landsat remote sensing images were processed based on the Google Earth Engine to classify features in the region from 1989 to 2019, and the overall and local geomorphology of the Jiangsu coastal area in the past three decades were analyzed and discussed. The trend of tidal flats in the study area has decreased 345.61 km², with an average loss of 11.52 km² per year. The expansion of human activities has led to the migration of the Jiangsu coast to the sea, the reduction of natural coastline fragmentation, and the expansion of artificial coastline to the sea.

Deep learning has been greatly improved recently, and natural image processing based on deep learning has also been greatly improved. However, there are still great differences between natural images and remote sensing images, among which the biggest is that the size of the target in remote sensing images is greatly different, which requires the model to have a strong multi-scale processing ability. In order to meet this goal, we use HRNet with full multi-scale fusion capability to replace ResNet to process remote sensing images. HRNet fully integrates low-level detail features, middle-level structure features and high-level semantic features, which is very suitable for remote sensing images. The experimental results show that our method has been greatly improved.

The train in tunnel paradox has always been a topic of debate in the scientific community, in order to allow readers to better understand its basic principles, and it needed to combine the basic knowledge of the theory of relativity to outline the train tunnel paradox. Taking Einstein's special theory of relativity as the starting point of the argument, this paper elaborates on Einstein's theory of relativity, focuses on the simultaneous relativity and the theory of physical motion shape change in the special theory of relativity, and clarifies the famous train in tunnel paradox. Based on the results of different reference systems, the author analyzes the paradox principle of trains and tunnels. Through the analysis, it can be seen that the special theory of relativity is meaningful in real life, and the learners should pay attention to the cultivation of their own logical reasoning ability when learning, and emphasize the understanding of the spirit of scientific criticism, so as to improve the learning effect of the special theory of relativity.

Shoreline is closely related to the management and development of the ocean, people's life, and national economic construction, Therefore, it is particularly important to accurately obtain the location of the shoreline. The existing Shoreline Extraction methods are based on image data, lidar data, or the integration of the two kinds of data. However, due to the characteristics of image data, the extracted shoreline is often fragmentary or there are areas where shoreline can not be extracted, and the type of shoreline is more, there is no unified, uniform algorithm. Therefore, this paper attempts to extract shoreline of man-made construction areas from airborne lidar data, and the algorithm is implemented with C + + and PCL. The experimental results show that the method can extract shorelines completely with high precision. Compared with the contour tracing method, the shoreline extracted by this method is smoother, and there is no local regional jump.

Detecting densely arranged and arbitrarily oriented targets on optical remote sensing images is a challenge, and there is much room for improvement in existing algorithms. In this paper, an end-to-end two-stage rotating target detection model based on rotating frames is proposed. This model adds FPN + PAN feature fusion structure after the backbone network to obtain enhanced features that fuse the feature information of each layer, and secondly, fine-grained rotation detection is accomplished by introducing Oriented Region Proposal Network and Oriented Region of Interest Pooling layer. By comparing the performance of this paper's algorithm with the current mainstream rotation detection algorithm on DOTA remote sensing dataset, this paper's algorithm can solve the difficult problem of detecting remote sensing targets with tight arrangement and arbitrary orientation to a certain extent.

Absrtact: transmission line is an important part of smart grid construction, which has many factors, such as wide distribution, long distance, complex working environment, difficult maintenance and easy to be damaged by natural external forces, etc. Among them, the construction of transmission line online monitoring network plays an important role in ensuring the stability and safe operation of online monitoring technology. In this paper, through the analysis of the transmission line and on-line monitoring transmission data demand and the existing problems of the current communication network, the communication network and data transmission system for transmission line monitoring is designed, which provides some reference for the construction of stable operation of the transmission line on-line monitoring network.

The effect of soil-pile-structure interaction (SPSI) is becoming one of the key issues in the seismic design of the nuclear island structures built on non-bedrock sites. The 3D lumped-mass stick (LMS) model of the AP1000 nuclear island structure considering the SPSI effect is developed to represent the steel containment vessel, the containment internal structures, and the coupled shield and auxiliary buildings supported by the pile-raft foundation. To improve the calculation efficiency, the 3D model is simplified into the equivalent 2D models in the X-Z and the Y-Z planes through vector decomposition of the 3D components and their interactions. The soil nonlinear behavior is termed the soil primary nonlinearity effect and the soil secondary nonlinearity effect (SSNE). The influence of SSNE caused by the effect of SPSI on the seismic responses of the AP1000 nuclear island structure is studied using various bedrock motions. The results show that: (1) Compared with the vertical seismic responses of the nuclear island structure, the SSNE of the horizontal responses is more obvious, and is stronger for bedrock motions with intense high-frequency components; (2) The influence of SSNE on the responses of the nuclear island structure has a positive correlation with its heights and bedrock motion levels; (3) Because of the SSNE, the peak accelerations and the peak relative displacements of the nuclear island structure decreases and increases respectively; while the 5% damped spectral accelerations of the nuclear island structure decrease in the shorter periods and increase in the mid-long periods. (4) For the peak relative displacements, the shorter the predominant periods of bedrock motions are, the higher the proportion of SSNE in the nonlinear effect of SPSI is. In general, the existence of SSNE will increase the flexibility and the energy consumption of the SPSI system of the nuclear island structure, and thus it is inappropriate to overlook SSNE arbitrarily.

In order to improve the accuracy of lightning strike fault location and identification based on the distributed traveling wave monitoring system and lightning location system, the location algorithm and recognition algorithm are improved and integrated. The distributed traveling wave monitoring device is regarded as the lightning detection sub-station, and the calculation results of the lightning location system are corrected to get the accurate lightning flashover tower position. The lightning flashover voltage of the positioning tower is calculated based on the lightning current amplitude and the procedure method. After the lightning fault type is predetermined, the recognition calculation based on waveform feature extraction can be carried out. The actual lightning tripping fault and waveform were used to verify, the results show that the proposed method can effectively improve the accuracy of lightning strike fault location and identification.

In the present study, the wind data during two synoptic processes observed by the surface wind tower in the Tibetan Plateau and the high spatial resolution model data simulated by CALMET were be evaluated, the horizontal and vertical distribution characteristic of the model wind data around the region of the wind tower point and the reference point were be analysed. Results showed that the wind observation data during the two processes in the nearsurface boundary were a well consistency. The horizontal distribution of the wind fields were consistent at the moment of the maximum wind speed during the two processes. The trend of the ten minute mean wind speed were upward with time variations. The air was flowed from the mountain ridge to the mountain valley over the complex region. The vertical distribution of the wind speed had more relevant that the correlation coefficient were 0.6811(α = 0.05) in the observation tower point and 0.8304 (α = 0.05) in the reference point between 16:00 pm Jan 30^th and 16:00 pm Jan 31^st. The advantages of using the advanced numerical models will be reflected over the extremely complex underlying terrain where the lack of observation stations.

The timely and effective evaluation of spatially-explicit urban PM2.5 concentrations is highly important for an improved understanding of urban environmental health and sustainability in China. However, recent studies examining the spatiotemporal patterns of PM2.5 concentrations has not been comprehensively clarified due to the absence of spatial-detailed urban landscape linkage. In this study, a general non-linear model was developed to depict the non-linear relationship between spatially explicit impervious surface area (ISA) fractions and associated PM2.5 concentrations gradient changes for individual cities from multi-source satellite-based dataset. The comparative results of environmental quality across 32 major cities in China showed that the spatial pattern of urban PM2.5 concentrations is correlated with geographical orientation and socio-economic clusters—high baseline and more balanced states of PM2.5 concentrations are prevalent in North China and the Yangtze delta agglomerations. Temporally, during the period of 2000-2018, most of cities have the path dependency, whereas high concentration of PM2.5 diffused from the original east of 'Hu-Huanyong Line' toward some cities in north-eastern, central and western regions. In addition, our study highlights the optimizing regional economic structure and promoting urban greening construction will be of great significance in sustainable urban environmental health management.

By using the spatial analysis function of ArcGIS and the statistical function of EXCEL, the cumulative confirmed cases, cumulative cured cases and cumulative death cases of new-type corona pneumonia from January 2020 to July 2020 were correlated with the vector map of China's administrative region to obtain the spatial distribution of new-type corona pneumonia nationwide. The cumulative diagnosis, cure and death tolls of the new type of coronary pneumonia were represented by color classification, and the situation of the new type of coronary pneumonia nationwide was visualized. The analysis showed that the distribution of the new type of coronary pneumonia at this stage was clustered, mainly centered in Hubei Province, spreading to the surrounding Hunan Province, Henan Province, Jiangxi Province and Anhui Province, and the sudden occurrence of the new type of coronary pneumonia occurred in very few regions.

The structure of the coal mine goaf is complex and its stability is easily affected by many factors. The use of reasonable detection methods to obtain basic data on the development characteristics of the overburden cavities, cracks and separations in the goaf is very important for the development and construction of the goaf. Meaning. The article summarizes and analyzes the currently commonly used goaf detection methods, and compares their applicability and advantages and disadvantages. Finally, the combination of engineering examples verifies the use of drilling core, drilling TV camera technology, and geophysical logging. The method provides a certain reference for the design and construction of the goaf detection project for the reliability of the goaf detection.

The development of smart grid needs the blessing of power Internet of things technology. With the continuous promotion and construction of domestic power Internet of things technology, a large number of Internet of things terminals will be connected to the network communication through different wireless communication technologies, which will form a huge heterogeneous network. Among them, wireless communication technology is the key to realize the power Internet of things. In order to give full play to the absolute advantage of the power Internet of things, this paper analyzes the characteristics and composition of the current heterogeneous network, analyzes the key points of the heterogeneous network technology, and analyzes the corresponding strategies and feelings.

In order to meet the rapid development and strategic needs of the national economy, a large number of airports are newly built, rebuilt and expanded every year in China. With the accelerating process of urbanization, the airport construction environment is getting worse and worse: the terrain is complex, the construction process is often accompanied by high excavation and deep filling of earthwork, and the high filling airport is often accompanied by the problems of foundation settlement and slope safety and stability. Therefore, it is very important to identify and monitor the existing or potential deformation of the airport and the deformation under the external environment such as earthquake.

In order to study the linear instability and turbulence of the boundary plasma, this paper uses the BOUT++ numerical simulation tool. Mainly analyze the driving mechanism of the instability of the ideal balloon model, and the instability suppression mechanism of the ion diamagnetic effect, and compare with the analysis results of the dispersion relationship. Subsequently, the integral dispersion relationship is used to analyze the suppression mechanism of the shear flow. In addition, the dispersion relationship is not suitable for analyzing the global effect of the shear flow. Due to the locality of the dispersion relationship, the integral dispersion relationship uses the numerical integration of the mode structure to resolve the dispersion relationship. Used to analyze shear flow. Then, use the numerical integration of kinetic energy in the whole space to study the contribution of these effects to free energy. Finally, using the above linear analysis method, the physical mechanism of these effects under the EAST divertor configuration is studied.

For the launch of Chinese Mars-1 exploration in a coastal spaceport at noon, summer, there are issues such as big fairing size, complex fairing layout and uncertain flow field of fairing. The author conducted analysis of fairing air conditioning security by thermodynamic modeling calculation and CFD simulation and proposed specific improvement measures to secure on-time launch according to the calculation analysis conclusions. Special test verification was conducted after the carrier rocket transfer. The improvement measures were proven to be effective. During mission launch, the fairing environment security is good. After fairing air conditioning decoupling, the fairing inside temperature rise is within the required scope, ensuring on-time launch of Mars exploration.

To improve the utilization rate of DRO, reduce the cost of human lunar and human mars exploration mission, and exploit space between earth and moon, this paper studied human lunar and mars exploration flight modes based on DRO space station. Firstly, the mission profile of human lunar and mars exploration flight mode based on DRO space station is analyzed by comparing with the direct round-trip lunar flight mode. The human lunar and mars exploration mission can be decoupled into the human space transportation mission and the lunar or mars landing mission by the DRO space station. Then, a flight mode evaluation model is established to analyze and evaluate quantitatively the advantages and disadvantages of the flight modes based on the DRO space station. The evaluation results show that the flight mode based on the DRO space station is a optimal flight mode.

Aiming at the aerodynamic optimization problem of guided ammunition airfoil, genetic algorithm is used to optimize the lift-drag ratio of the airfoil based CST geometric description method with given inflow velocity and fixed angle of attack. Firstly, the parameter space generated by CST parameterized airfoil description method is performed with binary code and the basic operations of genetic algorithm such as selection, crossover and mutation. Then, the external CFD solver is called to automatically divide the grid and calculate the lift-drag ratio through the control script. Finally, the target solution is obtained by searching. The calculation results show that this method can obtain a optimal target solution in relative less generation for a group of airfoils generated randomly in a certain search space.

Considering design of flight control system in the early stage of aircraft design is the development trend of aircraft design, which improves the design quality and reduces the cost, and the analysis of aircraft dynamic characteristics is its foundation. The aircraft flight performance analysis and simulation software is designed and developed by C++ language according to the flight dynamics principle of fixed-wing aircraft with conventional layout, combined with control law and simulation algorithm, which can be used to analyze the stability of aircraft, estimate the flight performance, preliminarily design the flight control system and carry out real-time flight simulation. The numerical example shows that the software can accurately analyze and calculate the flight performance of the aircraft, and achieve good attitude maintenance and maneuver control in the simulation. The comparison of results verifies the accuracy of the software.

With the continuous increase of the deep space detection range, the establishment of the space-to-earth link and the stable transmission of the signal are the main problems facing. Increasing the antenna aperture of the ground-based measurement and control equipment can effectively improve the signal receiving sensitivity. However, the increase in antenna size brings about an increase in wind resistance. Strong wind will cause resistance to the driving of large-aperture antennas in deep space and cause certain deformations in the structure. This article takes a 35-meter-aperture antenna as an example, aiming at the antenna being orthogonal and parallel to the wind direction, the simulation analysis was performed under the influence of strong wind in the two cases, and the deformation cloud image of the antenna mouth surface was obtained. The antenna gain loss caused by the deformation error of the main surface and the translation of the secondary reflection surface was calculated, and the maximum gain loss reached 3.70dB.

Satellite remote sensing is one of the indispensable means of natural resources monitoring and management. With the great improvement of the spatial resolution, spectral resolution, temporal resolution and data coverage capability of Chinese satellites, domestic satellites have been deeply and extensively applied in the natural resource management and geological survey. In recent years, the impact of global change on the ecological environment and biodiversity in the alpine region of northern Tibet has aroused great concern, among which the response to land degradation, desertification and plateau wetland protection are the important contents. In this paper, Gaofen-1 (hereinafter referred to as "GF-1") satellite data are used to conduct the research and experiment of wetland and saline-alkali land feature extraction, which provides guiding suggestions and scientific references for the wider and deeper application of domestic satellites in feature extraction.

Supplier design collaboration based on digital mock up is an important feature of the globalization and digitization of modern aircraft development. Supplier design collaboration based on digital mock up can organize all design engineers in different locations and time around the world, make them working in a unified design environment. It can overcome the shortcomings of traditional aircraft development, and improve the efficiency of aircraft development and shorten the development time greatly. This article introduces the advantages of supplier design collaboration based on digital mock up, focuses on the classification, general requirements, process and data management of supplier design collaboration based on digital mock up. It provides relevant reference and help for supplier design collaboration of digital mock up.

As a highly intelligent robot, AGV has many applications in automatic production, AGV needs to ensure its own safety when it works in a complex environment. If there are irregularly moving obstacles within the working range of AGV, collision accidents are easy to occur. This paper establish a obstacle avoidance fuzzy controller based on fuzzy control algorithm, this controller can obtain the operation data of obstacle avoidance, AGV adaptive neuro-fuzzy network system is further established to train these operation data for a certain number of times, so that this system can provide correct behavior decision for AGV dynamic obstacle avoidance. This paper builds a simulation environment to test this system. It is proved that this system has good robustness and reliability.

In this paper, the extended state observer was applied to satisfy the high accuracy demand of the nonlinear dynamic inversion through compensating the uncertainty and disturbance of the nonlinear dynamic system as a total disturbance. The terminal sliding mode controller was adopted to assure the system converging fast and accurately. The effectivity and quickly converging of the proposal method was demonstrated by the simulation of the attitude control problem of the upper stage, with the comparison of other methods.

Spacecraft system, as a typical complex system, is also a high-risk field. Even small faults in local links may bring huge losses or disasters. Analyzing the system level associated anomaly detection method caused by the interaction of multiple telemetry parameters can provide technical means for the rapid and accurate detection of system level in orbit associated faults. In this paper, firstly, a system anomaly detection method based on high-dimensional space mapping is proposed. Secondly, based on the preprocessing of telemetry data, the division of telemetry subsystem is realized. Thirdly, Based on the dimension reduction of subsystem space, the system anomaly detection method is given, and the technical process of the method is given. Finally, through the verification of on orbit actual data, it shows that the system anomaly detection result is accurate.

Accurate positioning of drones is one of the key technologies for drone management and security. Aiming at the characteristics of the active emission of electronic radiation signals during the operation of UAVs, the passive positioning method based on multi-station time difference of arrival (TDOA) is studied, and the problem of solving the nonlinear equation of time difference of arrival passive location is transformed into an optimization problem of seeking the extreme value of the time difference likelihood function. The chicken swarm algorithm is used to search for the best positioning coordinates. The simulation results show that the chicken swarm algorithm can quickly and stably converge to the target position in time difference of arrival passive location system.

In the rapid development of intelligent network connected vehicle, many information security risks have been exposed. The application of CAN bus encryption and decryption technology can solve the problem of safety communication in vehicle intranet. The sender and the receiver use the same set of key. The sender encrypts the data before sending it, and the receiver decrypts the data before applying it. In order to study the influence of CAN bus encryption and decryption on transmission efficiency, taking tc299, s32k144, mpc5606b and spc560b54 chips as examples, different encryption and decryption algorithms are used to encrypt and decrypt data of different lengths. The research shows that the time of symmetric encryption is much less than that of asymmetric encryption; With the increase of the length of the data to be encrypted and decrypted, the time required for encryption and decryption also increases; There is no significant difference in encryption and decryption efficiency between different chips. The time consumed by encryption and decryption is us level, which will not affect the real-time performance of the bus.

Over-exploitation of open-pit mines has caused extensive development of geological hazards, posing serious threats to people and buildings in the area. Based on the UAV tilt-tilt photography technology, this paper generates a real-world 3D model of the study area, taking the molybdenum mine of Songshumao in Yangjiajiaozi, Xingcheng City, Liaoning Province as an example, and combines orthophotography, high-precision DEM data and field geological survey research to establish a geohazard interpretation marker for the study area and interpret various types of geohazards such as landslides, landslides, dangerous rocks, ground cracks and unstable slopes in the study area. Based on the hierarchical analysis method combined with the geological environment characteristics, the geological hazard risk evaluation model and index system were established, and five indexes were selected, including geological hazard range, stratigraphic lithology, slope, elevation, vegetation coverage, etc. The study area was divided into high, medium and low risk zones, and the geological hazard risk zoning map was obtained to complete the geological hazard risk evaluation of the study area.

A test platform for reused solar modules was established in Wuhan in order to study the appearance changes, aging problems and degradation rate in electrical properties of the reused modules. The photovoltaic modules were mono-Si modules produced by Siemens in 1996. It was found that there was moisture at the edge of the modules. The aging of tin-coated copper strip was obvious, and there was blackening phenomenon. There were many white spots on the solar cells around the tin-coated strip, and the solar cells turned blue locally. The average annual power attenuation rate of different modules varied greatly, but it was mainly related to the increase of series resistance of modules. The integrated average annual power attenuation of PV modules was about 3.59% during 4 years.