bluuebunny/crossref_metadata_2025 huggingface dataset:
Could the accident of “Ever Given” have been avoided in the Suez Canal?
Authors: Lebedev A O , Lebedeva M P , Butsanets A A
Published: November 2021

The paper analyzes the causes of the accident that took place on March 23, 2021 at 101 kilometers of the Suez Canal with the supercontainer ship “Ever Given”. The vessel lost control at the narrowest point of the transoceanic corridor. According to various sources, the possible cause of the accident was weather conditions. At the moment of passing through the Suez Canal, the vessel got into the area of a sandstorm with the increased wind speed and poor visibility. Sandstorms are frequent in the area. To avoid similar accidents, a quantitative analysis was performed to determine its causes. The analysis was undertaken based on solving the system of equations for mathematical model of a container ship with similar characteristics. The calculation was carried out according to well-proven methods with the involvement of databases for determining the hydrodynamic and aerodynamic characteristics. It has been established that a vessel with wind exposed area typical for vessels of the “Ever Given” type cannot be steered at wind speeds exceeding 12 m/s. In addition, a significant effect of the density of the air-dust mixture on the controllability of the vessel has been found. In the presence of an admixture of sand in the air, the vessel can lose controllability even at 9 m/s.
Modeling and controlling a quarter-vehicle active suspension model
Authors: Quan Vu Hai , Huy Truong Nguyen , Trong Duc Nguyen
Published: November 2021

This paper presents an application of the LQR active suspension control algorithm for a vertical planar oscillation model developed for ¼ of a vehicle. The wheel smoothness and dynamics with the road surface are two parameters to provide control signals. A simulation model is developed here based on MATLAB software to compare and evaluate the LQR active suspension model with the passive suspension. The results obtained here shows an improvement for a number of parameters when utilizing the active suspension model including fluctuating amplitude; oscillation damping time; the displacement acceleration of the active suspension body.
The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism
Authors: Xu Wencai
Published: November 2021

With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.
Computer Aided Drug Design Based on Artificial Intelligence Algorithm
Authors: Yu Wenye , Chen Zhenyu
Published: November 2021

The problems such as high cost and long development time in drug design and development have an important impact on its development, which makes many scholars devote themselves to looking for the auxiliary model of drug design. With the rapid development of computer technology, computer-aided drug molecular research model is more and more mature. This paper aims to study the computer-aided drug system based on artificial intelligence algorithm, so that researchers can speed up the process and reduce the cost when searching for specific protein molecules. In this paper, the principle of complementary matching in the docking process of target molecules and ligands, which is commonly used in drug design, is described, and the functional expression mode and various docking methods of molecular docking are studied. Finally, the research hotspots of molecular docking technology are analyzed, including scoring function, search strategy and flexible protein docking. Ant colony algorithm is introduced into molecular docking platform as a variant of conformation search algorithm, and a new plants algorithm is developed. Finally, the implementation of plants algorithm is analyzed in detail, and the optimized plants system and gold system based on genetic algorithm are simulated, and the relevant experimental data are counted. The simulation results show that the new drug design method based on ant colony algorithm has advantages in docking success rate, docking speed and docking accuracy. The success rate of plants is higher than that of gold, and the docking time is only 1/6 of that of gold.
Application of Data Mining Technology in Software Engineering
Authors: Hong Xiaobin
Published: November 2021

With the rapid development of informatization, computer database software systems have entered various fields of society, which has brought about the explosive growth of industry data. Faced with massive amounts of data, computers with limited storage capacity have to abandon some outdated data, and the application of various data mining technologies related to it has gradually matured. The purpose of this article is to discuss the application research of data mining technology in software engineering. This article analyzes the correlation analysis of a large number of bug repair source code update data and bug defect reports in the version control system SVN and the defect tracking system Bugzilla in the software engineering project development process, and tries to classify the bug report by data mining technology: defect changes and potential defects change. Starting from large-scale software engineering projects, apply data mining technology to the huge software engineeri ng knowledge base. Especially the software development and maintenance are explained, as well as the more challenging problems in the future. This paper uses data mining technology to study the dependency of the source code files of each module of the software system, and helps software developers quickly understand the software architecture by understanding the interrelationships between the modules, and provides suggestions for modification paths. Experimental research shows that this paper compares with F-measure and concludes that FL-M-GSpan algorithm is better than TS-M-GSpan algorithm. At the same time, it is found that the FL-M-GSpan algorithm always has a better accuracy rate close to 95%, while the TS-M-GSpan algorithm always has a better recall rate.
Based on the Software Complexity Measurement of Complex Networks under Big Data Technology
Authors: Hong Xiaobin
Published: November 2021

With the development of the times, computer technology is booming, so the network is becoming more and more complex, software design is becoming more and more complex, because of the protection against a variety of internal or external risks. The internal risk is that the traffic carried by the system is too large to cause the system to crash or the system to crash caused by the code operation error, and the external threat is that hackers use computer technology to break into the system according to security vulnerabilities, so the purpose of this paper is based on big data technology, the software complexity of complex networks is measured and studied. With the consent of the school, we used the school’s internal network data, and after consulting the literature on the complex construction and analysis of complex networks and software, modeled and analyzed it using the improved particle group algorithm. The experimental results show that there is a certain correlation between complex network and software complexity. Because complex networks determine that software requires complex construction to withstand potential risks to keep the software running properly.
Algorithm Research and Implementation Based on Component Optical Detection System
Authors: She Yongming
Published: November 2021

With the development of the times, gradually there is a new thing called electronic components born. Electronic components are collectively called components of electronic components and small machine instruments, such as diodes used in light bulbs. Components can be used in optical inspection systems, but they require some help from computer technology. Therefore, the purpose of this paper is to use algorithms to study component-based optical detection systems. After consulting the literature on components and optical detection systems, we analyzed the suitability of components and optical inspection systems by constructing different systems using a variety of algorithms. The experimental results show that the traditional ant colony algorithm is better than the AdaBoost algorithm and the genetic algorithm, so we finally choose to use the ant colony algorithm to construct the optical detection system.
Pose Estimation of GIS Pipeline Based on Spatial Transformation Network
Authors: Zhang Gong , Li Boming , Liu Ti , Chen Wenhan , Wang Yichang
Published: November 2021

The manual flange structure alignment between GIS pipelines in the power system is inefficient and difficult to accurately align. To solve this problem, combined with the research results in the field of deep learning named spatial transformation network, a new pose estimation method based on single camera is proposed. In view of the high similarity between the moving flange and the static flange at the pixel level, the spatial transformation network is used to find the pixel mapping relationship of the two flange images. Thereby establishing the mapping relationship between the pixel coordinates of the two flange images and then using multiple points. In the perspective method, the pixel coordinates are mapped to the world coordinates to obtain the estimation of the position of the key point in the flange, and then the direction vector of the flange is calculated according to the position of the key point. Since there is a pixel coordinate transformation relationship between the static flange and the movable flange. Only the position of the key point in the static flange can be inversely solved by measuring the position of the key point in the static flange. Experiments show that, compared to the traditional method of measuring flange pose based on instrument measurement and linear regression, the method proposed in this paper can accurately measure the pose of the flange structure. And it can rely as little as possible on the measurement of the key points of the moving flange by the instrument.
Monitoring Algorithm of Stress Point of Concrete Penstock in Large Construction Engineering
Authors: Kou Xiaoxing
Published: November 2021

With the rapid development of the national construction industry, cracks and other problems often appear in the concrete structure during the initial and subsequent construction. When these problems develop further, the structural safety of the entire building may be compromised. Therefore, it is necessary to analyze the causes of cracks and other problems in concrete buildings, and be able to monitor and analyze these problems in time, and then propose reasonable solutions. This is already a problem that the entire construction technicians urgently need to solve. This paper studies the algorithm for monitoring stress points of concrete penstocks in large construction projects. Firstly, it uses literature research to explain the form of stress nodes in large-scale construction projects and the deficiencies in the research on the stress nodes of concrete penstocks in large-scale construction projects. In the experiment, the existing 3 algorithms are used to detect the force points, and compare their detection degree and false alarm rate. The experimental results show that the detection effect of the KNN algorithm is obviously inferior to the other two algorithms with the same neighbor parameters. Its detection rate is only 91%, and the false alarm rate reaches 30%. The other two algorithms are equivalent. The detection effect of the KNN algorithm is obviously inferior to the other two algorithms, the detection rate is poor, the outlier force points that are obviously deviating from the whole around the dense force points are not recognized, and the data of many normal force points located at the edge of the sparse area Instead, it was recognized as abnormal. Among the three algorithms, the detection rate of the NLOF algorithm is better, reaching 99%, which is significantly higher than the other two algorithms.
High Camouflage Intrusion Detection Method for Structured Database Based on Multi Pattern Matching
Authors: Song Dawei , Zhu Xun , Ma Fengjuan
Published: November 2021

with the rise and rapid development of mobile communication, intelligent terminal and data system, we are entering the era of mobile Internet. In recent years, more and more data need to be processed and transmitted in daily life, and structured data is becoming more and more important. Among them, multi-mode matching technology can search data in a wider range. Matching for multiple patterns at a time avoids unnecessary matching, accelerates the matching process, and helps to find longer matching information and obtain higher accuracy. This paper mainly introduces the high camouflage intrusion detection method of structured database based on multi-mode matching. This paper uses the high disguised intrusion detection method of structured database based on multi-mode matching, collects sensitive information of wireless access points and stations through the communication of WLAN in multimodal matching, then intercepts and forges data packets to initiate replay attack. Replay attack is characterized by abnormal traffic in the network, which can be detected by statistical analysis. The experimental results show that the high camouflage intrusion detection method based on multi-mode matching makes the camouflage intrusion detection rate increase by 23%. The limitations of the design and research of camouflage intrusion detection are analyzed, discussed and summarized, so as to enrich the academic research results.
RETRACTED: The Application of Computer Technology in Electronic Information Engineering
Authors: Yang Yuhang
Published: November 2021

With the continuous progress and development of our country’s economy, computer network technology has also been continuously improved and upgraded, and the level of EI technology has matured day by day, and the requirements on enterprises have become more and more stringent. In order to more fully expand the scope of EI computing and ensure that the technology is in a normal state of development, it is necessary to conduct applied research and exploration of EI engineering based on computer technology in order to use it for people to provide better services and create More value. This article is based on the research on the actual application of computer technology applied in our country’s EI technology engineering. First, it uses the literature research method to summarize the characteristics of our country’s EI technology engineering and the actual application of computer technology in EI engineering. In this paper, the investigation and research method is used to investigate some of the problems and suggestions faced by the actual application of computer technology in our country’s EI technology engineering. Through the analysis of the results, the in-depth study of the development trend of our country’s EI engineering is predicted. Survey results: Among the problems faced by the application of computer technology in EI engineering, local area network instability problems occur more frequently, accounting for about 45%, and then data and information security problems, accounting for about 30%. In the proposal for the development of the application of computer technology in EI engineering, the introduction of advanced local area networks will account for about 40% of the equipment, and about 35% for the strengthening of data security technology. It can be seen from this that the main research direction of computer technology application in EI engineering is local area network stability and data security.
Implementation of Web Data Mining Technology Based on Python
Authors: Feng Guilian
Published: November 2021

With the arrival of the era of big data, people have gradually realized the importance of data. Data is not just a resource, it is an asset. This paper mainly studies the realization of Web data mining technology based on Python. This paper analyzes the overall architecture design of distributed web crawler system, and then analyzes in detail the principles of crawler’s URL function module, crawler’s web crawl function module, crawler’s web page parsing function module, crawler’s data storage function module and so on. Each function module of the crawler system was tested on the experimental computer, and the data information was summarized for comparative analysis. The main significance of this paper lies in the design and implementation of a distributed web crawler system, which, to a certain extent, solves the problems of slow speed, low efficiency and poor scalability of traditional single computer web crawler, and improves the speed and efficiency of web crawler in grasping information and web page data.
A Review of Key Line Identification Methods in Complex Networks
Authors: Chang Congyao , Zhou Xuhui , Zou Qingyu
Published: November 2021

This paper summarizes the key line identification method of the most representative power network in the complex networks. It is of great significance to identify the key line of the power transmission network to prevent the cascade failure of the power system. By analyzing and comparing the methods of identifying key line in recent years,this paper summarizes the power betweenness method, electrical betweenness method, comprehensive importance method,entropy theory and some other methods,and expounds their advantages and disadvantages. Based on this,the future research direction of critical line identification in complex power transmission networks is proposed.
Key Technologies of Seam Fusion for Multi-view Image Texture Mapping Based on 3D Point Cloud Data
Authors: Yang Xiaoxue
Published: November 2021

With the rapid development of computer technology and measurement technology, three-dimensional point cloud data, as an important form of data in computer graphics, is used by light reactions in reverse engineering, surveying, robotics, virtual reality, stereo 3D imaging, Indoor scene reconstruction and many other fields. This paper aims to study the key technology of 3D point cloud data multi-view image texture mapping seam fusion, and propose a joint coding and compression scheme of multi-view image texture to replace the previous independent coding scheme of applying MVC standard compression to multi-view image texture. Experimental studies have shown that multi-view texture depth joint coding has different degrees of performance improvement compared with the other two current 3D MVD data coding schemes. Especially for Ballet and Dancer sequences with better depth video quality, the performance of JMVDC is very obvious. Compared with the KS_ IBP structure, the gain can reach as high as 1.34dB at the same bit rate.
AI Applied in Computer Network Technology Analysis and Research
Authors: Chen Guocai
Published: November 2021

In the big data era, the level of information technology in China has been greatly improved, and AI technology has emerged as the times require. It has now been widely used in computer networks, effectively improving the overall work efficiency and quality of staff. The AI technology application has greatly changed the working methods of the people. The production and application of AI has prompted a clear development direction for science and technology in the big data era. In the development of modern society, the application of AI in computer network technology it is of great significance. The thesis mainly discusses the analysis of AI in computer network technology.
Vehicle Location Algorithm Based on Federated Learning and Smart Phone in GNSS Low Sampling Rate Scene
Authors: Guo Wenbiao , Li Hang , Yin Feng , Ai Bo
Published: November 2021

In the existing vehicle positioning system based on Global Navigation Satellite System/Inertial Navigation System (GNSS/INS), when the GNSS signal is lost, the error accumulated by using only the INS will damage the positioning accuracy. In order to improve the accuracy, this paper proposed a positioning method based on data-driven and learning models, which utilized distributed data sets to collaboratively construct accurate positioning models through federated fusion algorithms without sacrificing user privacy. In the field scenarios of IID and Non-IID types, this paper compared the performance of INS and the existing two typical methods, DeepSense and PVAUA, it is verified that the two federated learning algorithm models constructed had higher positioning accuracy in different scenarios and different GNSS signal loss durations. The results were analyzed.
Design and Research of Optical Fiber Signal Analyzer in Embedded System Based on Big Data
Authors: Tao Xiao
Published: November 2021

With the advent of the era of big data, substation automation technology has reached a certain level after more than ten years of application development. In recent years, in the transformation and construction of the transmission and distribution network, a large number of substations have adopted modern technology, which has greatly improved the technological modernization of transmission and distribution and transformer construction, enhanced the reliability of transmission and distribution and transformer scheduling, and reduced the substation. The total cost of construction and the application of electronic transformers have effectively promoted the research on digital substations. In order to complete the real-time monitoring and testing of the substation system, so as to more quickly grasp the working status of the substation equipment, this paper proposes a research and design of an optical fiber signal analyzer for substation system testing. By analyzing the IEC 61850 protocol, the characteristics of the embedded system and the embedded operating system, the software modular design of the sampling value module of the signal analyzer is carried out, and the analysis of the substation configuration file SCD is designed and realized. The research results show that in practical applications, it is necessary to parse the CID files one by one, which will cause cumbersome work and easy omissions. Based on the above considerations, the SCD configuration file containing the entire site information was finally selected.
Application of Artificial Intelligence Technology in Mechanical Electrical Control System
Authors: Li Nan
Published: November 2021

Artificial intelligence technology (A I T) has also been widely used in society. Combining A I T with mechanical and electrical control systems will bring huge profits to the corporate sector and greatly improve work efficiency. It can save a lot of money in the electrical control operations of all walks of life in the country, and fill the gap in this technology in the country. The purpose of this article is to study the application of A I T in mechanical electrical control systems (M E C S). This article first introduces the basic theories and concepts of A I T, extends the core technology of A I T, and combines the current status of the electrical control system of modern enterprises in our country to discuss its existing problems, and finally studies and analyzes A I T and machinery. Combination of electrical control systems, and discuss the application of A I T in mechanical electrical orifice subsystems. Experiments show that, compared with the existing M E C S, the M E C S using A I T can better complete the work and improve work efficiency.
Cooperative Sensing and Allocation Algorithm of Cognitive Radio Spectrum Based on Artificial Intelligence
Authors: Zhang Yu , He Wei , Zhao Jianchuan
Published: November 2021

In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.
Research on Networked Product Packaging Design Based on Internet of Things Technology
Authors: Li Si
Published: November 2021

In recent years, with the improvement of Internet of Things technology and the continuous improvement of economic level, people’s consumption consciousness has also changed, especially for the packaging of networked products put forward higher requirements. This paper mainly studies the visual expression of networked product based on Internet of Things technology. Starting from the increasing influence of Internet electronic consumption in China’s economic consumption system, this paper deeply discusses the systematic product logistics packaging design under the Internet consumption form. This paper analyzes and discusses the differences between Internet consumer commodity packaging and physical consumer commodity packaging in functional orientation, added value of packaging, systematic visual design and other aspects.
Smart Relocation System
Authors: Ye Junyou , Dong Xiangxiang , Yang Ju , Xu Xiaomeng , Zhao Yuntian
Published: November 2021

With the increase of social mobility, the relocation of work and living places has become the norm in people’s daily life. However, the current level of informatization in the handling industry is generally low. If users want to know the progress of the porter age, they can only go to the site in person; after the porter age is completed, if they want to find the items, they need to search through the piles of boxes. It is even impossible to know the detailed location of the items. So there is an urgent need for an intelligent system that can comprehensively control the entire handling process from inventory, porter age to item positioning. The intelligent handling system designed in this paper uses the Internet of Things, network communication, artificial intelligence, cloud computing and other technologies, which can sensitively capture the packed items to improve the packing efficiency, quickly and accurately identify the name of the item to facilitate searching, and establish multi-level tags to store item information and achieve fuzzy matching, and encryption at each stage to ensure user privacy.
Big Data Technology Application in Mechanical Intelligent Fault Diagnosis
Authors: Cao Huiteng
Published: November 2021

With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.
Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow
Authors: Xu Wencai
Published: November 2021

Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.
Experimental Discussion on Fire Image Recognition Based on Deep Learning
Authors: Cui Yongyi , Qu Fang
Published: November 2021

Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.
Influence of Supercritical Carbon Dioxide Brayton Cycle Parameters on Intelligent Circulation System and Its Optimization Strategy
Authors: Li Kai , Sun Kai
Published: November 2021

The supercritical carbon dioxide (SCO2) Brayton cycle takes advantage of the special physical properties of carbon dioxide near the critical point (31.1 °C, 7.39MPa), and has higher energy conversion efficiency than the current large-scale steam power cycle. This cycle can be widely used in the field of power generation, but a lot of research work is still needed in terms of component parameters and layout under different working conditions. In this regard, the purpose of this paper is to study the influence of supercritical carbon dioxide Brayton cycle parameters on cycle efficiency and its optimization strategy. Based on the first law of thermodynamics, this paper uses Aspen Plus software to establish S-CO2 Brayton cycle system models with different circulation arrangements. In this paper, the existing algorithm of the simulation system and the newly-built algorithm are used to build the S-CO2 shunt and recompression Brayton cycle system model, and the accuracy of the model is verified with experimental data from literature. Then this paper conducts disturbance experiments on the model to study the influence of heater heating, valve opening and precooler cooling on the system, and analyze the dynamic characteristics of the system. Experimental results show that the thermal efficiency of the simple Brayton cycle is much lower than that of the recompression Brayton cycle and the split recompression Brayton cycle under higher parameters. The compressor outlet pressure and the turbine inlet temperature have an effect on the efficiency of the recompression Brayton cycle. The impact is significant, and the optimal value of the compressor shunt coefficient is between 0.5-0.7, which provides a reference for the layout optimization method of the SCO2 Brayton cycle and the optimization of the same type of power generation cycle.
Modeling and Experimental Verification of Heat Dissipation Model for Test System of Li-ion Battery Test-bench
Authors: Qu Jie , Huang Meihua , Wang Chao
Published: November 2021

In order to develop a test-bench which can accurately test the mechanical signal of lithium-ion battery under various working conditions, the finite element model of heat dissipation simulation is established for different test systems designed in the mechanical system of the test-bench. At the same time, pulse excitation experiments are used to verify the simulation results, and the bulk force experiments are carried out to verify the optimal test system structure chosen accord to the simulation results. The mechanical structure of a test system is composed of a lithium-ion battery and upper/lower spacer. In its finite element model, the finite element model of the lithium-ion battery is established by the actual measurement after cutting lithium-ion battery by a diamond cutter, and spacers are established according to their actual design. The heat dissipation simulation finite element model can simulate the heat dissipation of an actual test system, which is conducive to the design and selection of an optimal test system, so as to improve the accuracy of test data measured through the test-bench and provide a reliable data basis for the development of the battery management system coupling temperature-current-voltage-swell-force.
Chaotic Analysis of Fractional-Order Permanent Magnet Synchronous Generator for Wind Turbine
Authors: Yang Li , Yang Fuzhao , Huang Sudan , Liang Tao , Huang Tianmin
Published: November 2021

Fractional-order wind turbine is a strongly coupled non-linear dynamic system. It mainly studies the significant chaos characteristics such as the complex chaotic motion with fractional order varying. According to the mathematical model of the system, the fractional order Lorenz chaotic equation is established by linear affine transformation and time scale transformation. The theory of Lyapunov stability analysis is adopted to deeply study the development process of the system from stable operation to chaotic motion. The correctness of the chaos characteristics of the system is verified.
Automatic Kinking Device System Design Based on Microcontroller
Authors: Gao Peng , Li Wei , Fang Jian , Wu Yue
Published: November 2021

To solve the problem that traditional Kinking equipment can not meet the needs of handcrafters today. The system to automation, intelligence asa breakthrough point, the use of STC12C5A60S2 micro-controller as the control core of the entire system, through the Hall encoder, film keyboard, LCD display, so that producers can use only keys to control the wire-tanger, and can be real-time from the display to observe the state of work, so as to achieve low-cost, high-efficiency wire production. Experiments show that in the case of 14 hours of continuous work, the tanger can still complete the normal tanger work and the accuracy rate is as high as 99.8percent. Low cost, good stability, high efficiency, small size, wire-tanger can assist people’s production and life, to achieve the goal of efficient production.
Application of Optimization Technology for Overhaul Decision of Substation Equipment Based on Machine Learning
Authors: Liu Ziquan , Zhu Xueqiong , Ma Jingtan , Hu Chengbo , Fu Hui , Zhao Ke
Published: November 2021

With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.
RETRACTED: Driving Behavior Based on Big Data Analysis and EEG Data Analysis
Authors: Zhang Tao , Hong Wang
Published: November 2021

In recent years, as the number of automobiles in my country has increased year by year, the number of automobile accidents and casualties and the direct economic losses caused by them are very high. How to improve the level of road safety has become an important research content in the field of transportation. Driving a car is a complex activity involving perception, judgment, decision-making and manipulation, and requires the brain to coordinate and guide the driver’s driving functions. EEG signals can reflect the driver’s psychophysiological state, and then represent the driver’s perception activities. The application of EEG data analysis in driving behavior research explains the mechanism of driving behavior from a new perspective of cognitive neuroscience and brings new solutions to traffic safety problems. Driving behavior research is the main research content in the field of road safety. Recognizing and predicting the state of driving behavior is very important for the development of intelligent driving assistance systems and the improvement of road safety. This paper analyzes the EEG data while driving based on big data analysis. Firstly, the literature research method is used to summarize the EEG data analysis process and the research significance, and then the driver’s EEG data is analyzed and researched through simulated driving experiments. The relationship between its parameters and driving behavior. Experimental results show that the front area and buffer area have a strong correlation with all variables of driving behavior, especially the correlation with acceleration and forward time is about 35%. In addition, compared with other driving behavior variables, time zone has the strongest correlation with speed, about 56%. Approximately 46% of the samples are beta waves that are significantly related to driving behavior. In addition, alpha waves account for about 20% of the total number of samples, while the correlation between delta waves and driving behavior is the weakest, accounting for only about 10% of the total number of samples.
Research on New Rail Transit Power Supply System Scheme
Authors: Jin Song
Published: November 2021

The tourism rail transit project has broad development prospects in China. The new type of rail transit represented by trams and monorails is suitable for the demand of the project of the rail transit, and the traction power supply system is great significance for the normal operation of the new type rail transit. This paper introduces the external power supply, medium-voltage loop network and traction power supply of new type of rail transit project. Through the comparison of technical schemes, the selection for several important engineering schemes in the new rail transit power supply system engineering design are obtained.
A Protection Method for DC Distribution Network with Single Pole Grounding Based on Integrative Two-terminal MMC
Authors: Jiang Hua , Shen Xinglai , Ma Bing
Published: November 2021

Based on the analysis model of unipolar grounding fault occurred in two-terminal MMC DC distribution network, the characteristics of unipolar grounding fault are studied. The similarity characteristics of transient currents between the fault line and the non-fault line are analyzed when the fault is located in the feeder, tie line and bus respectively. According to the characteristics of single-pole grounding fault and the similarity principle, a new single-pole grounding protection method for DC distribution network based on two-terminal MMC is proposed. The simulation results show that the proposed single-pole grounding protection method can accurately determine the single-pole grounding fault at any position of the DC distribution network. The research results can effectively solve the problem of single-pole grounding protection in DC distribution network, and effectively improve the security and reliability of DC distribution network.
Development of Li-ion Battery Bulk Force and Expansion Displacement Test-bench
Authors: Qu Jie , Huang Meihua , Wang Chao
Published: November 2021

In order to test the bulk force and expansion displacement of lithium-ion batteries, it is planned to develop a corresponding test-bench, which is mainly composed of a measurement-control system and a mechanical system. To improve the accuracy of the test data, the coupled thermal-structure simulation of the test system in the mechanical system of the test-bench is carried out to select an optimal mechanical structure of the test system. At the same time, for safe and convenient testing, a monitoring-testing system software was developed to ensure the reliability and safety of data collection. Finally, through the test-bench, the battery expansion displacement-SOC curve and the battery bulk force-SOC curve under different discharge rates were tested, providing a basis for the development of a battery management system coupling temperature-current-voltage-displacement-force.
On the Application of Impeller in Multi Axis CNC Machine Tools
Authors: Ye Weiwen
Published: November 2021

Multi axis CNC machine tool has good linkage processing effect. Through the application of integral impeller in CNC machine tools, to improve the adaptability of CNC machine tools to complex surface processing parts, to improve the accuracy of multi axis CNC machine tools. The first part of this paper introduces the integral impeller and its machining characteristics; the second part introduces the basic NC machining process of integral impeller; the third part discusses the application of impeller in multi axis CNC machine tools from the creation of guide track, the simulation of integral impeller, software processing and generation. The purpose is to provide some reference for the processing and production of integral impeller.
Optimization of laser cladding parameters of nickel-based metal powder for creation a heat-resistant coating
Authors: Gulov M A
Published: November 2021

Optimization of laser deposition of nickel aluminide was carried out to obtain a single track of high quality. The structure of single tracks was investigated. Based on the data obtained, a multilayer structure was deposited and a study of the distribution of elements was carried out.
Introduction of additive technologies in PJSC «UEC-Saturn»
Authors: Zavodov S A , Fedoseev D V , Polyakov S A
Published: November 2021

This article describes the process of assimilation of additive technologies at PJSC UEC-Saturn, introduced and advanced domestic materials for various areas of additive production, the results of manufacturing critical parts of the gas turbine engine by this technology.
Hierarchical ensemble learning method in diversified dataset analysis
Authors: Liu Ze yuan , Li Xin long
Published: November 2021

The remarkable advances in ensemble machine learning methods have led to a significant analysis in large data, such as random forest algorithms. However, the algorithms only use the current features during the process of learning, which caused the initial upper accuracy’s limit no matter how well the algorithms are. Moreover, the low classification accuracy happened especially when one type of observation’s proportion is much lower than the other types in training datasets. The aim of the present study is to design a hierarchical classifier which try to extract new features by ensemble machine learning regressors and statistical methods inside the whole machine learning process. In stage 1, all the categorical variables will be characterized by random forest algorithm to create a new variable through regression analysis while the numerical variables left will serve as the sample of factor analysis (FA) process to calculate the factors value of each observation. Then, all the features will be learned by random forest classifier in stage 2. Diversified datasets consist of categorical and numerical variables will be used in the method. The experiment results show that the classification accuracy increased by 8.61%. Meanwhile, it also improves the classification accuracy of observations with low proportion in the training dataset significantly.