bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research and Analysis on Low-temperature Charging Control Strategy of Electric Vehicle
Authors: Li Chan , Chen Lixue , Li Jin 
Published: October 2020

Abstract
In this paper, the pure electric vehicle is taken as the research object. Under the same conditions (such as the same limit temperature) under different conditions, the charging control strategy test of the collective prototype and the same vehicle is carried out under different conditions. The test data is used to analysis vehicle charging control strategy and key technical indicators that affect charging performance. The test results show that from the analysis of the collective prototype test, AC charging can be started normally in the low temperature environment. The charging power of most test vehicles has a certain attenuation compared with the normal temperature, and the charging time has a certain increase. From the same temperature comparison test of the same car, the difference of charging current trend, battery input current, battery temperature and maximum charging current between low temperature and normal temperature is analyzed.
Research on Zero-speed Detector Based on INS / Wheel speed and Vehicle gear
Authors: Liu Pan , Huang HongQuan 
Published: October 2020

Abstract
The zero-speed detector is required for the correction of the positioning error and the initial static alignment of the autonomous vehicle. The zero-speed detector that only depends on the INS (Inertial Navigation System) has zero-speed misjudgment. The performance of the detector after the fusion of wheel speed has been improved, but the wheel speed is still not integrated in scenarios such as long periods of stationary, emergency stop and reverse. Because the wheel speed signal only outputs the vehicle speed when the vehicle reaches a certain speed, there is a long initial alignment time, and the wheel speed is not fused in the complex driving process. This paper proposes a Vehicle gear information added to the INS / wheel speed fusion zero speed detector to achieve a 10s increase in the initial alignment time, the effect of wheel speed fusion has been improved, and thus the zero speed The accuracy of detection.
Back propagation neural network rainfall prediction model based on particle swarm optimization
Authors: Luan Zhangbao 
Published: October 2020

Abstract
It is a feasible method to use neural network construction to predict rainfall in the region. However, the error of this method is bigger, and its error is from the neural network structure itself. In view of the problem that the rainfall prediction precision based on BP neural network construction is low, this thesis proposes to optimize BP neural network rainfall prediction model with particle swarm optimization (PSO) algorithm, and then use the same training samples and testing samples to conduct a simulation experiment to the forecast model before and after optimization. The results show that the experimental testing result of the forecast model after optimization is more in line with the actual value.
Evaluation method of minimum area roundness error based on sector search
Authors: Liu Changying , Wang Libin 
Published: October 2020

Abstract
The evaluation of roundness error has an important reference in the quality evaluation of shaft parts. Aiming at the problems of principle error, model error and non-linear solution difficulty in the existing roundness error evaluation algorithm, the area search algorithm is combined with the roundness error minimum inclusive area evaluation method, and the area fan search method is used The error is evaluated. By constructing reasonable simulation data and using the designed evaluation method to calculate the roundness error of the simulation data, the results of the roundness error of different evaluation methods are compared and analyzed. And the actual roundness error evaluation and method verification are carried out through experiments. The results show that this method is simple to operate, stable and reliable, and has higher calculation efficiency, and the evaluation accuracy is significantly improved compared with the least square method, the largest inscribed circle method, and the smallest circumscribed circle method.
Research on Face Recognition Based on Gabor-LeNet Convolutional Neural Network Model
Authors: Dai Qinxuan , Luo Xiaoshu , Meng Zhiming 
Published: October 2020

Abstract
Aiming at the problems of slowing convergence speed, and low recognition accuracy in the face recognition training of the existing LeNet-5 convolutional neural network, an improved LeNet-5 convolutional neural network model is proposed and applied for face recognition. The main improvement is the use of Gabor filter to initialize the first convolutional layer and the activation function of Parametric Rectified Linear Unit (PReLU). The recognition accuracy of the improved model on ORL and GT face datasets has reached more than 98%. At the same time, the recognition accuracy rate on the face data set AR with occlusion has reached more than 90%, indicating that the improved model has strong robustness.
Research on Intelligent Control Algorithm for Automatic Drilling of Vehicle-mounted Rig
Authors: Zhang Yang , Chang Jianghua , Fan Dong , Tian Hongliang 
Published: October 2020

Abstract
The adaptability of traditional PID, fuzzy PID and feedback linearization sliding mode variable structure control intelligent algorithm to automatic drilling is analyzed. The results show that the feedback linearization sliding mode variable structure control algorithm has more ideal response speed, control accuracy and robustness, which solves the nonlinear problem of the electro-hydraulic control system of the truck-mounted drilling rig and improves the tracking control accuracy of the system during automatic drilling.
Adoption of Back Propagation Network Improved by Particle Swarm Optimization in Network Intrusion Detection
Authors: Guan Chengli , Yang Yue 
Published: October 2020

Abstract
In order to scientifically and reasonably solve some problems in the existing intrusion detection system and effectively maintain the security of network information, the improved particle swarm optimization (PSO) algorithm and back propagation (BP) neural network are used to form a new algorithm to optimize the intrusion detection technology. First, the traditional PSO algorithm is improved, and it is used to optimize the BP neural network. Then the fused new algorithm is adopted to establish an intrusion detection model. Finally, the detection effect of the new model is verified through the experiment. The results show that the detection rate of the intrusion detection model for network attacks can be as high as 93.31%, and it also greatly reduces the false positive rate of the system, which can be as low as 1.85% and has a high practical application value. This study has important reference value for the application of BP neural network improved by PSO in network security.
An Adaptive Multi-Scene Single-User Flexible Negative Control Effect Evaluation Algorithm
Authors: Zhang Gaoyang , Ke Huimin , Wang Gang , Wang Dong 
Published: October 2020

Abstract
Designing an evaluation algorithm that can adapt to the demand response of multiple scenarios can effectively reduce the cost of secondary development of the system and improve the willingness of users to participate in the regulation. In this paper, a self-oriented single user flexible negative control evaluation algorithm is proposed. In this algorithm, the determination method of target load curve is proposed first. On this basis, the generalized distance between curves is defined according to the characteristics of common evaluation indexes. Several parameters are reserved in the generalized distance. By adjusting the parameters, the evaluation scheme can be adapted to different scenarios. Then, this paper puts forward a method to set the initial value of parameters. By using this method and neural network, and combining with the existing evaluation samples, the parameters in the generalized distance can be determined automatically. Furthermore, an annual updating method of each parameter in the generalized distance is proposed by using the algorithm of neural network. Finally, the effectiveness of the algorithm is verified by the historical response data of Changzhou Lvjian district.
Node Deployment Optimization in Wireless Sensor Networks
Authors: Wang Zhenhuan 
Published: October 2020

Abstract
Recently, wireless sensor networks (WSNs) which consist of a group of cheap but energy-limited nodes with functionalities of sensing, computation, and communication, are widely applied to detect or monitor the events in interest. One of the fundamental design issues in WSNs is how to deploy or relocate the nodes to optimize the desired sensing performance. This survey focuses on a variety of sensing performance models and movement-assisted node deployment approaches that have been studied and proposed in the literature and highlights their strengths and limitations. On one hand, this tutorial proposes an exhaustive classification of existing movement-assisted self-deployment algorithms. On the other hand, some physical constraints and their impacts, such as limited-battery and obstacles are introduced in this tutorial. Then, the detailed comparisons between the existing algorithms with respect to different scenarios are well performed in this tutorial. Therefore, this paper provides not only a view of the state-of-the-art but also the insights for selecting the appropriate self-deployment algorithms for different sensing scenarios. Furthermore, the open problems and challenges in the area of node deployment are summarized at the end of this tutorial.
Research on University Energy Environmental Protection Monitoring System Based on Computer Network Technology
Authors: Shen Jie , Xia Qing 
Published: October 2020

Abstract
The issue of campus energy and environment has become one of our increasing concerns. Environmental issues have become an important assessment index restricting the development of schools and students’ self-improvement. Only the sustainable development of the environment can ensure the smooth implementation of school education. For this reason, the development of an efficient energy environment monitoring system based on computer networks is imperative. Based on this research background, the paper analyses the needs of an efficient micro-energy environment monitoring system, and discusses the functions of the monitoring system and related hardware and software. Finally, the system was tested to verify the feasibility of the system.
The Research on Data Acquisition and Analysis Platform for Lathe Machine based on Stream Computing
Authors: Lv Taizhi , Zhang Juan , Chen Yong 
Published: October 2020

AbstractManufacturing industry is the main body of national economy, the foundation of building a country, the tool of rejuvenating a country and the foundation of a strong country. Lathe machine is the basis of manufacturing industry, which determines the level of intelligent manufacturing industry. This paper uses the real-time processing characteristics of stream computing to build a data acquisition and analysis platform for lathe machine. This platform monitors the efficiency of the equipment in real time, improve the utilization rate of the equipment, and improve the processing efficiency. The platform collects data in real time, analyses the data, and tracks the efficiency and process of the process, which is of great significance to optimize the process and improve production management.
An Unsupervised Learning Short Text Clustering Method
Authors: Dai Zuhua , Li Kelong , Li Hongyi , Li Xiaoting 
Published: October 2020

Abstract
Due to the continuous development of Natural Language Processing (NLP), the task of short text categorization has been paid more and more attention. In short text clustering, the high-dimensional sparseness of text representation matrix becomes a challenging problem. This paper proposes a deep embedded method for feature extraction and clustering allocation using auto encoder of sentence distributed embedding. This method maps from data space to low-dimensional feature space and iteratively optimizes clustering targets. Experimental results on three short Chinese text data sets verify the effectiveness of the method. Moreover, it is superior to the existing correlation clustering methods.
The Clock Model of Tracking and Measuring the GNSS Orbit About Motion Observation Station
Authors: Liu Yang , He Jing , Wang Xiao , Wu Guangzhi 
Published: October 2020

Abstract
The clock model is analysed much in this paper which is key to GNSS orbit determination. First it is introduced how to calculate sampling distance. That is necessary and mostly to rebuild the clock model of incept equipment from both GNSS single data of especial PRN codes point positioning and passive ranging system. Then the formula is gived about both clock error model of GNSS and the change from GPST to time of absolute positioning And the express about telegraphese navigation to GPST is taken apart particularly. Last both the process of GNSS amending clock model with telegraphese navigation code and of calculating sampling distance and experience of shorting positioning distance first are given.
Computer Aided Research on the Physical Security of Ubiquitous Power Internet of Things Equipment
Authors: Tan Long , Wei Chunming , Zhang Kexin , Chen Shiyu 
Published: October 2020

Abstract
Through the analysis of the microgrid architecture under the access of the ubiquitous power Internet of Things and the current construction of the peak shaving auxiliary service market, the feasibility, necessity and economy of the microgrid participating in the peak shaving auxiliary service market are demonstrated. The thesis makes full use of information technology such as Internet of Things technology, computers, big data analysis, distributed applications, and mobile technology. It focuses on the safety management and control of the work site of power grid enterprises, and uses intelligent monitoring terminal equipment to access, such as access control (barrier), Authorize locks, intelligent safety monitoring robots and electronic fences to supervise and control production sites and operating equipment, standardize operating procedures, and improve on-site safety management and control capabilities and terminal operating equipment real-time supervision capabilities.
Research on the Application of Deep Learning in Computer Network Information Security
Authors: Zeng Han , Liu Zixi , Cai Honghao 
Published: October 2020

Abstract
With the development of current information technology, computer networks are playing an increasingly important role in people’s daily work and life. While the network brings great convenience to people, it also puts people at risk. A large number of information is stored on the network, which may be illegally invaded at any time, and there are serious security risks. Therefore, research on computer network information security is of great significance. Based on this, this article analyzes the application of deep learning in computer network information security. First, it gives an overview of network information security, introduces its concepts and current development status in China, then explains the concepts and principles of deep learning, and then analyzes the application of deep learning in network information security, including the current application status and its application in 7 aspects in details. This article has certain significance for deepening the research and application of deep learning in the field of information security.
Analysis of Autonomous Vehicle Safety Constraints Based on Systems-Theoretic Process Analysis
Authors: Hu Zongmei 
Published: October 2020

Abstract
This article starts with the safety challenges faced by autonomous vehicles, sorts out the development stages of autonomous vehicles and the cases of safety threats. It combines the safety standards of connected vehicles through case analysis, and uses STPA to analyze unsafe controls. Based on the four dimensions of laws, regulations, hardware, software, and network, this paper analyzes the safety constraints of autonomous driving technology, and proposes corresponding safety constraint strategies. The analysis results show that the improvement of the automobile life cycle safety regulation system, the establishment of a new type of anti-deep learning algorithms, and the construction of big data control links by OEMs can effectively reduce the safety risks of autonomous vehicles, which is worth further experimental research.
Modal Analysis of Thrust Coupling Based on ANSYS Workbench
Authors: Han Changkun 
Published: October 2020

Abstract
Through nx12.0, a solid model of a thrust coupling is established, which is transformed into a finite element model by using workbench 19.0, and modal analysis is carried out. The sixth order natural frequency and critical speed of the thrust coupling are obtained, and the first six order vibration figures are obtained, which clearly shows the natural vibration characteristics of the structure, and at the same time, it is the vibration analysis and structural optimization of the thrust coupling. The design provides a theoretical basis.
A Mathematical Morphology Algorithm for Detection and Counting of Nuclear Tracks
Authors: Pan Yi , Qiu Lei 
Published: October 2020

Abstract
Solid state nuclear track detection plays an extremely important role in the field of nuclear technology, but artificial cognition is extremely time-consuming and labor-consuming. In this paper, an automatic measurement system of solid state nuclear track is proposed, in which the microscopic image of the nuclear track is obtained by the camera and processed digitally, and then use software to identify the image content. Based on the image restoration theory, this paper preprocesses such as noise elimination and luminance correction, and the mathematical morphology algorithm is used for track counting and parameter estimation. The comparison results between automatic recognition and manual recognition show that the algorithm can effectively identify track points from images under different conditions and improve work efficiency.
Topology optimization of aircraft flip spreader for lightweight
Authors: Diao Jiandong , Zhang Chenfeng , Huang Qiushuang , Wang Feng 
Published: October 2020

Abstract
In recent years, structural optimization design and computer-aided design methods have achieved long-term development. In order to achieve design innovation, improve product performance, and reduce production costs, enterprise pay more and more attention to the new design methods. Optimization design has become the key process of product development. Especially in the fields of aerospace, automobile that focus on lightweight and fuel saving, the scope and depth of topology optimization design is rapidly developing. In this paper, topology optimization has been applied on design of aircraft flip spreader, using ANSYS Workbench software to reduce the weight of the spreader. First, using static structural tool for static analysis, set loads and supports, solve the stress distribution of spreader under various working conditions. Then, using topology optimization tool for optimization design, set the optimization variables, constraints and objective, solve the optimal material distribution. At last, compared with traditional experience-based design methods, topology optimization design methods have obvious advantages, the weight of spreader has been reduced 25.5%, without affecting the component strength.
Computer aided design and simulation of the amplification structure of 3D electronic packaging printer
Authors: Gao Haitao , Liu Zhiwei , Meng Yu , Li Qi , Yue Xiong , Liu Fengmei , Li Likun , Yong Yiyao 
Published: October 2020

Abstract
3D electronic packaging printer can realize the transformation of electronic packaging from 2D to 3D, and it is a very important new device in the electronic industry. The amplification structure is the key part of it. In this paper, by means of geometric calculation, computer aided design and simulation, an improved rhombic 3D design drawing and key design parameters of the amplification structure are obtained. The rhombic amplification structure is simulated by finite element simulation method, and the simulation amplification coefficient is 9.05, which is consistent with the design amplification coefficient of 9.36. The displacement output diagram and stress cloud diagram show that the displacement and strength of the rhombic amplified structure conform to the service requirements of 3D electronic packaging printer.
Application Scenarios Of Knowledge Graph In IT Operation
Authors: Li Ziqian , Liu Xusheng , An Yeteng , Sun Lintan , Zhang Yintie , Wan Jing 
Published: October 2020

Abstract
Since Google optimized its search service by using knowledge graph, the application of knowledge graph has developed rapidly, especially in the aspect of information system operation and maintenance. CMDB (configuration management) built based on knowledge graph plays an excellent role in fault location of information system.However, the operation and maintenance data generated during the operation of the information system are diverse, not limited to configuration, correlation, and alarm information, and include all data generated during the operation and maintenance of the system. The knowledge map technology is used to effectively correlate various types of operation and maintenance data, which is more conducive to identifying core application scenarios of the information system, thereby further improving the global control ability in the operation and maintenance process. Therefore, this paper explores the rich application scenarios and prospects of knowledge graph application in the field of information operation and maintenance.
Optimization Algorithm of Bearings Only Tracking based on Cubature Kalman Filter
Authors: Xiao Fu , Lei Liu 
Published: October 2020

Abstract
In view of the shortcomings of low precision and poor performance in the process of bearings only tracking (BOT) in wireless sensor networks(WSNs), an optimization algorithm of BOT based on the cubature Kalman filter(CKF) in WSNs is proposed. The sensors with spatial distribution are divided into several clusters, the number of sensors in each cluster is the same, and each sensor detects data to form a local estimation. Then uploads it to the cluster head (CH) node data fusion, so as to get the accurate information of the target. In order to improve the positioning accuracy, the CKF is used to process the data of each sensor, and then it is uploaded to the CH node to ensure the tracking ability of the filter to the changes of structural parameters. The CH node uses weighted data fusion algorithm to get the final fusion result. The simulation results verify the effectiveness of the method.