bluuebunny/crossref_metadata_2025_split huggingface dataset:
Algorithm Research and Implementation Based on Component Optical Detection System
Authors: She Yongming 
Published: November 2021

Abstract
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

AbstractThe 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.