Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590726
Xinyue Sun
The simulation experiment of the double closed-loop control system of BLDCM Based on Fuzzy Neural Network Adaptive PID controller shows that the response speed of the control system can be improved, the overshoot can be reduced, and the load and the parameters of the motor can be improved.
{"title":"Research on Intelligent Algorithm of Brushless DC Motor Control System Based on Fuzzy Neural Network","authors":"Xinyue Sun","doi":"10.1109/ICISCAE52414.2021.9590726","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590726","url":null,"abstract":"The simulation experiment of the double closed-loop control system of BLDCM Based on Fuzzy Neural Network Adaptive PID controller shows that the response speed of the control system can be improved, the overshoot can be reduced, and the load and the parameters of the motor can be improved.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126854201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590818
Yi Fu, H. Zhang
Using the dimming control system, it is very convenient to control the lighting brightness of the lighting circuit according to the use requirements of different scenes at different times. It not only realizes the automatic control of lighting, but also creates and beautifies the light environment of the space inside and outside the building, and reduces the energy consumption and prolongs the service life of lamps. In view of this, the lighting condition model system of exhibition hall design based on computer aided design is put forward. Based on the optimization strategy of genetic algorithm, the visual comfort condition satisfied by the quantitative design of green lighting and the quantitative method for calculating the illumination intensity of green lighting are obtained. The system adopts ZigBee wireless network to realize data transmission, wireless monitoring, control and management among nodes of intelligent lighting system. As a bridge between the terminal and the server, the system gateway is responsible for monitoring information communication between ZigBee and Ethernet protocols. The server collects the monitoring information of the storage terminal and provides an interface for accessing and controlling the terminal.
{"title":"Illumination condition model system of exhibition hall design based on computer aided design","authors":"Yi Fu, H. Zhang","doi":"10.1109/ICISCAE52414.2021.9590818","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590818","url":null,"abstract":"Using the dimming control system, it is very convenient to control the lighting brightness of the lighting circuit according to the use requirements of different scenes at different times. It not only realizes the automatic control of lighting, but also creates and beautifies the light environment of the space inside and outside the building, and reduces the energy consumption and prolongs the service life of lamps. In view of this, the lighting condition model system of exhibition hall design based on computer aided design is put forward. Based on the optimization strategy of genetic algorithm, the visual comfort condition satisfied by the quantitative design of green lighting and the quantitative method for calculating the illumination intensity of green lighting are obtained. The system adopts ZigBee wireless network to realize data transmission, wireless monitoring, control and management among nodes of intelligent lighting system. As a bridge between the terminal and the server, the system gateway is responsible for monitoring information communication between ZigBee and Ethernet protocols. The server collects the monitoring information of the storage terminal and provides an interface for accessing and controlling the terminal.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121429866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590776
Zhidong Huang, Yuxiang Guo, Di Cao, Chenrui Hu, Chenjun Ding
Governance of zombie enterprises is an important means to ensure the healthy, sustained development of the economy. Traditional methods such as identifying zombie enterprises based on expert knowledge suffer from incomplete expert database and increasingly complex economic environment. Thus the proposed data-driven system is implemented to not only identify zombie enterprises, but also visually present the enterprise portraits. In this paper, the three-year data of 50000 enterprise is transformed into N×N×3 image-format-matrix (N×N are the number of features). Afterward, Convolutional Neural Network, namely CNN is applied and result is got in one stage instead of fitting the data of each year and voting. It is also proved that CNN can effectively mine the short-time series features of enterprises by reconstruct the data into image-format-matrix. Considering the imbalance of data, Focal-Loss is implemented as the loss function when applying CNN model to the data. Grad-CAM, a model interpretive method in the image domain, is used to explain the CNN network after the fitting is completed. It is found that the model pays too much attention to salient features. Thus Mutual Channel Loss is further implemented to make the model pay attention to those indistinguishable features. At the same time, CBAM attention module is added to pay selective attention to different characteristics of enterprises in different years. The three-year information of 15050 enterprises collected from the State Administration for Industry and Commerce of China is used as the source data. The results show that comparing with other models, our CNN model reached the state of art in the rate of misjudgment and missed judgment.
{"title":"Identification and Visualization of Zombie Enterprise Portraits - Mining Short-time Series Features from the Perspective of Image","authors":"Zhidong Huang, Yuxiang Guo, Di Cao, Chenrui Hu, Chenjun Ding","doi":"10.1109/ICISCAE52414.2021.9590776","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590776","url":null,"abstract":"Governance of zombie enterprises is an important means to ensure the healthy, sustained development of the economy. Traditional methods such as identifying zombie enterprises based on expert knowledge suffer from incomplete expert database and increasingly complex economic environment. Thus the proposed data-driven system is implemented to not only identify zombie enterprises, but also visually present the enterprise portraits. In this paper, the three-year data of 50000 enterprise is transformed into N×N×3 image-format-matrix (N×N are the number of features). Afterward, Convolutional Neural Network, namely CNN is applied and result is got in one stage instead of fitting the data of each year and voting. It is also proved that CNN can effectively mine the short-time series features of enterprises by reconstruct the data into image-format-matrix. Considering the imbalance of data, Focal-Loss is implemented as the loss function when applying CNN model to the data. Grad-CAM, a model interpretive method in the image domain, is used to explain the CNN network after the fitting is completed. It is found that the model pays too much attention to salient features. Thus Mutual Channel Loss is further implemented to make the model pay attention to those indistinguishable features. At the same time, CBAM attention module is added to pay selective attention to different characteristics of enterprises in different years. The three-year information of 15050 enterprises collected from the State Administration for Industry and Commerce of China is used as the source data. The results show that comparing with other models, our CNN model reached the state of art in the rate of misjudgment and missed judgment.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590747
Jingjing Mu
The electrical system of medium-sized transport aircraft mainly focuses on electricity. The safe use of electricity must be guaranteed. The reliability of aircraft power supply system is much stricter, because the transportation volume of aircraft is extremely large. At present, two major problems in the research of electrical system fault diagnosis are how to extract signal features and how to establish a diagnostic machine. With the emergence and development of wavelet theory and the increasing maturity of machine learning algorithm, it is an effective and worthwhile solution to preprocess the fault signal by wavelet and then use the machine learning algorithm for fault diagnosis, which provides a new and effective way for fault diagnosis of electrical system. In this paper, a support vector machine (SVM) classification model under the generalized framework is designed, and the parameters of the model are globally optimized by particle swarm optimization. The simulation results show that the fault types can be accurately and orderly identified, thus verifying the effectiveness of the diagnosis model.
{"title":"Research on Fault Diagnosis of Electrical System of Medium Transport Aircraft Based on Machine Learning Algorithm","authors":"Jingjing Mu","doi":"10.1109/ICISCAE52414.2021.9590747","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590747","url":null,"abstract":"The electrical system of medium-sized transport aircraft mainly focuses on electricity. The safe use of electricity must be guaranteed. The reliability of aircraft power supply system is much stricter, because the transportation volume of aircraft is extremely large. At present, two major problems in the research of electrical system fault diagnosis are how to extract signal features and how to establish a diagnostic machine. With the emergence and development of wavelet theory and the increasing maturity of machine learning algorithm, it is an effective and worthwhile solution to preprocess the fault signal by wavelet and then use the machine learning algorithm for fault diagnosis, which provides a new and effective way for fault diagnosis of electrical system. In this paper, a support vector machine (SVM) classification model under the generalized framework is designed, and the parameters of the model are globally optimized by particle swarm optimization. The simulation results show that the fault types can be accurately and orderly identified, thus verifying the effectiveness of the diagnosis model.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590737
Zeyang Zheng
Natural language processing is an important means to realize the communication between man and machine using natural language, and help computers quickly understand the meaning expressed by natural language. The most common application system using natural language processing technology is information retrieval system. On this basis, an information processing model based on BP neural network (BPNN) and statistical method is discussed, and the principle of BPNN is explained in detail. After analyzing these phenomena, researchers think that natural language processing is more suitable for tasks requiring accurate results, and the understanding level of natural language processing is divided into seven levels from low level to high level: pronunciation level → morphology level → vocabulary level → syntax level → semantics level → pragmatics level → context level. On this basis, the application of natural language processing in information retrieval system is discussed.
{"title":"Natural language processing and information retrieval system based on BP neural network","authors":"Zeyang Zheng","doi":"10.1109/ICISCAE52414.2021.9590737","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590737","url":null,"abstract":"Natural language processing is an important means to realize the communication between man and machine using natural language, and help computers quickly understand the meaning expressed by natural language. The most common application system using natural language processing technology is information retrieval system. On this basis, an information processing model based on BP neural network (BPNN) and statistical method is discussed, and the principle of BPNN is explained in detail. After analyzing these phenomena, researchers think that natural language processing is more suitable for tasks requiring accurate results, and the understanding level of natural language processing is divided into seven levels from low level to high level: pronunciation level → morphology level → vocabulary level → syntax level → semantics level → pragmatics level → context level. On this basis, the application of natural language processing in information retrieval system is discussed.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131492294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590795
Zhixuan Xiao, Shihua Pan
In order to optimize problem of information security risks faced by the current social development, scientific research scholar assessed using machine learning algorithm is put forward the corresponding countermeasures, so can not only solve the previous evaluation subjectivity is strong, classification accuracy is too low, can reasonable use qualitative quantitative way to step by step a grading factors of information security risk assessment. Therefore, on the basis of understanding the application of machine learning algorithm, this paper comprehensively discusses how to do a good job of intelligent information security risk assessment according to the basic nature of decision tree.
{"title":"Analysis of Intelligent Information Security Risk Assessment Based on Decision Tree","authors":"Zhixuan Xiao, Shihua Pan","doi":"10.1109/ICISCAE52414.2021.9590795","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590795","url":null,"abstract":"In order to optimize problem of information security risks faced by the current social development, scientific research scholar assessed using machine learning algorithm is put forward the corresponding countermeasures, so can not only solve the previous evaluation subjectivity is strong, classification accuracy is too low, can reasonable use qualitative quantitative way to step by step a grading factors of information security risk assessment. Therefore, on the basis of understanding the application of machine learning algorithm, this paper comprehensively discusses how to do a good job of intelligent information security risk assessment according to the basic nature of decision tree.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127583527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590640
Y. Xie, Jianhua Huang, Xiyan Sun, W. Yin, Zhenghan Qiao, Yao Zhang
To solve the problems such as large workload, easy omission, low timeliness and low degree of automation in the method of visual identification of buildings in surveillance images, this paper studies the building extraction method based on surveillance images. In this paper, first of all, datasets of relevant scenes are collected and annotated. Then, we fine-tuned the Deeplabv3plus model to improve the accuracy of building extraction. Specifically, replace the backbone network with the resnet, the dilation rate is reduced to improve the detection accuracy of small objects, the output of the res net is combined with the output of the ASPP module through the way of skip connection, and the spatial details of the lower level and the semantic information of the higher level are fused. Besides, the multiple loss strategy is adopted. we also compared the fine-tuned model combined with different deep-level feature extraction networks with other classical semantic segmentation models on the open source CAMVID dataset, and the experiment showed that the combination of fine-tuned deeplabv3plus model and resnet50 reached the optimal IoU, F1 score and precision. In addition, we conducted an experimental comparison between the two training methods of only using the collected data training and the joint training of CAMVID dataset. The experiment shows that the model segmentation effect obtained by the joint training of data set is better. It significantly improves the details of the edge of the building, which can achieve robust extraction of the building.
{"title":"Research on building extraction method based on surveillance images","authors":"Y. Xie, Jianhua Huang, Xiyan Sun, W. Yin, Zhenghan Qiao, Yao Zhang","doi":"10.1109/ICISCAE52414.2021.9590640","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590640","url":null,"abstract":"To solve the problems such as large workload, easy omission, low timeliness and low degree of automation in the method of visual identification of buildings in surveillance images, this paper studies the building extraction method based on surveillance images. In this paper, first of all, datasets of relevant scenes are collected and annotated. Then, we fine-tuned the Deeplabv3plus model to improve the accuracy of building extraction. Specifically, replace the backbone network with the resnet, the dilation rate is reduced to improve the detection accuracy of small objects, the output of the res net is combined with the output of the ASPP module through the way of skip connection, and the spatial details of the lower level and the semantic information of the higher level are fused. Besides, the multiple loss strategy is adopted. we also compared the fine-tuned model combined with different deep-level feature extraction networks with other classical semantic segmentation models on the open source CAMVID dataset, and the experiment showed that the combination of fine-tuned deeplabv3plus model and resnet50 reached the optimal IoU, F1 score and precision. In addition, we conducted an experimental comparison between the two training methods of only using the collected data training and the joint training of CAMVID dataset. The experiment shows that the model segmentation effect obtained by the joint training of data set is better. It significantly improves the details of the edge of the building, which can achieve robust extraction of the building.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"9 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133294975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590714
Yuxuan Yao
The computer database that handles big data efficiently has created convenience for modern society, but it has gradually exposed more potential safety hazards in the application process. Computer databases storing massive data are prone to information loss, content tampering and other problems in the face of various attacks. With the development of the Internet and the continuous breakthrough of network technology, there are more and more hidden dangers that undermine network security, and the level and scope of damage are also increasing. This paper mainly describes the related concepts of intrusion detection technology, and puts forward the effective implementation of computer database intrusion detection technology based on virtualization technology, as well as the related strategies of fully optimizing computer database intrusion detection technology, so that the computer database can be effectively protected. Diversified intrusion detection system technology is widely used, which can not only ensure the safe and normal operation of database system, but also prevent the loss of important information and the destruction of structural integrity.
{"title":"Research on Computer Database Intrusion Detection Technology Based on Virtualization Technology","authors":"Yuxuan Yao","doi":"10.1109/ICISCAE52414.2021.9590714","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590714","url":null,"abstract":"The computer database that handles big data efficiently has created convenience for modern society, but it has gradually exposed more potential safety hazards in the application process. Computer databases storing massive data are prone to information loss, content tampering and other problems in the face of various attacks. With the development of the Internet and the continuous breakthrough of network technology, there are more and more hidden dangers that undermine network security, and the level and scope of damage are also increasing. This paper mainly describes the related concepts of intrusion detection technology, and puts forward the effective implementation of computer database intrusion detection technology based on virtualization technology, as well as the related strategies of fully optimizing computer database intrusion detection technology, so that the computer database can be effectively protected. Diversified intrusion detection system technology is widely used, which can not only ensure the safe and normal operation of database system, but also prevent the loss of important information and the destruction of structural integrity.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115394462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590770
Xiaohui Wu
According to the development of information technology and the current situation of university teaching, scientific research and social management, this paper designs a data model for predicting students' achievement according to the characteristics of university data, and implements a complete college English assistant teaching system, which is based on B/S framework, and finally uses web as the front end to display decision data. Combining with the development trend of information technology, such as data mining, this paper constructs a college English auxiliary teaching system, optimizes Apriori algorithm in the teaching operation module, and analyzes students' English scores in college entrance examination. The research and implementation of this system in this paper is of great practical significance for improving the application level and depth of the college English auxiliary teaching system.
{"title":"Research on College English Assisted Instruction System Based on Data Mining Algorithm","authors":"Xiaohui Wu","doi":"10.1109/ICISCAE52414.2021.9590770","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590770","url":null,"abstract":"According to the development of information technology and the current situation of university teaching, scientific research and social management, this paper designs a data model for predicting students' achievement according to the characteristics of university data, and implements a complete college English assistant teaching system, which is based on B/S framework, and finally uses web as the front end to display decision data. Combining with the development trend of information technology, such as data mining, this paper constructs a college English auxiliary teaching system, optimizes Apriori algorithm in the teaching operation module, and analyzes students' English scores in college entrance examination. The research and implementation of this system in this paper is of great practical significance for improving the application level and depth of the college English auxiliary teaching system.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123418220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-24DOI: 10.1109/ICISCAE52414.2021.9590649
Mingcheng Zhang
The stock market is a highly complex nonlinear dynamic system. The stock price involves many uncertain factors. The influencing factors of the stock market are very complex and changeable. Reasonable and effective prediction of stock price has always been an important and difficult problem in the whole financial field. Time series analysis is one of the important tools in the field of economic forecasting. In the stock market, the time series forecasting method is often used to forecast the stock price trend, providing decision-making basis for investors and stock market managers. Therefore, the design of Trading System for simulating stock forecasting based on time series is proposed. Through the analysis and design of the simulated stock forecasting Trading System, the virtual Trading System is finally realized, which provides an important tool for investors to learn stock trading knowledge and increase stock operation experience, and contributes to the sound development of the stock trading market.
{"title":"Design of simulated stock forecasting trading system based on time series","authors":"Mingcheng Zhang","doi":"10.1109/ICISCAE52414.2021.9590649","DOIUrl":"https://doi.org/10.1109/ICISCAE52414.2021.9590649","url":null,"abstract":"The stock market is a highly complex nonlinear dynamic system. The stock price involves many uncertain factors. The influencing factors of the stock market are very complex and changeable. Reasonable and effective prediction of stock price has always been an important and difficult problem in the whole financial field. Time series analysis is one of the important tools in the field of economic forecasting. In the stock market, the time series forecasting method is often used to forecast the stock price trend, providing decision-making basis for investors and stock market managers. Therefore, the design of Trading System for simulating stock forecasting based on time series is proposed. Through the analysis and design of the simulated stock forecasting Trading System, the virtual Trading System is finally realized, which provides an important tool for investors to learn stock trading knowledge and increase stock operation experience, and contributes to the sound development of the stock trading market.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125055288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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