The optimization of microgrid is an important part of smart grid. The global energy consumption is seriously greater than the energy it has, and the environmental pollution brought by it should not be underestimated. If we want to reduce their impact, introducing the optimization of microgrid is a good solution. Short-term load forecasting is a very important prerequisite for microgrid optimization, which lays a solid foundation for the realization of the development goal of environmental protection and the improvement of the economic benefits of microgrid. In this paper, a Multi-PSO-SVM forecasting model is proposed to forecast the actual load. By simulating four prediction models with three different samples, we can see that the average predicted value and actual load value of Multi-PSO-SVM algorithm in the three different samples are almost less than 10 MV. Compared with the other three algorithms, Multi-PSO-SVM is superior in accurately predicting the load value at each time point, which provides important conditions for the success of microgrid optimization.
{"title":"Multiobjective Optimal Dispatching of Smart Grid Based on PSO and SVM","authors":"Man Bao, Hongqi Zhang, Hao Wu, Chao Zhang, Zixu Wang, Xiaohui Zhang","doi":"10.1155/2022/2051773","DOIUrl":"https://doi.org/10.1155/2022/2051773","url":null,"abstract":"The optimization of microgrid is an important part of smart grid. The global energy consumption is seriously greater than the energy it has, and the environmental pollution brought by it should not be underestimated. If we want to reduce their impact, introducing the optimization of microgrid is a good solution. Short-term load forecasting is a very important prerequisite for microgrid optimization, which lays a solid foundation for the realization of the development goal of environmental protection and the improvement of the economic benefits of microgrid. In this paper, a Multi-PSO-SVM forecasting model is proposed to forecast the actual load. By simulating four prediction models with three different samples, we can see that the average predicted value and actual load value of Multi-PSO-SVM algorithm in the three different samples are almost less than 10 MV. Compared with the other three algorithms, Multi-PSO-SVM is superior in accurately predicting the load value at each time point, which provides important conditions for the success of microgrid optimization.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"152 1","pages":"2051773:1-2051773:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75984383","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}
Digital technologies are shown to perform a vital role in developing a resource-efficient industrial base. The effective adoption of digital technologies can help reduce costs and improve the sustainability and flexibility of manufacturing industries. However, how digital technologies favor the transition towards manufacturing systems has not been analyzed in detail yet, so more conceptual and empirical investigation is required in this field. This study investigates the digital transformation trend in the manufacturing industry by combining the bibliometric method with life cycle mode and literature mining. An integrated data analysis system (IDAS) is developed, which includes data collection, data analysis, and visualization processes. As a result, dual-map overlay, keywords clusters, Timeline, and Time zone views are visualized to identify the research trend with the three main aspects: design, larger integrated circuit, and sustainable manufacturing concept evolution. Besides, to recognize potentials for future development of the domain, life cycle model-based technological trajectory is identified, constructing an information society, the combination of tools, and the performance improvement of the whole digital transformation. Moreover, in response to sustainable development, a system considering business strategies and cultural delivery, technological integration, and partner participation and evaluation is developed, which could cope with complex manufacturing processes due to different industrial tasks in different regions.
{"title":"The Sustainable Digitalization in the Manufacturing Industry: A Bibliometric Analysis and Research Trend","authors":"Zhiming Shang, Liming Zhang","doi":"10.1155/2022/1451705","DOIUrl":"https://doi.org/10.1155/2022/1451705","url":null,"abstract":"Digital technologies are shown to perform a vital role in developing a resource-efficient industrial base. The effective adoption of digital technologies can help reduce costs and improve the sustainability and flexibility of manufacturing industries. However, how digital technologies favor the transition towards manufacturing systems has not been analyzed in detail yet, so more conceptual and empirical investigation is required in this field. This study investigates the digital transformation trend in the manufacturing industry by combining the bibliometric method with life cycle mode and literature mining. An integrated data analysis system (IDAS) is developed, which includes data collection, data analysis, and visualization processes. As a result, dual-map overlay, keywords clusters, Timeline, and Time zone views are visualized to identify the research trend with the three main aspects: design, larger integrated circuit, and sustainable manufacturing concept evolution. Besides, to recognize potentials for future development of the domain, life cycle model-based technological trajectory is identified, constructing an information society, the combination of tools, and the performance improvement of the whole digital transformation. Moreover, in response to sustainable development, a system considering business strategies and cultural delivery, technological integration, and partner participation and evaluation is developed, which could cope with complex manufacturing processes due to different industrial tasks in different regions.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"35 1","pages":"1451705:1-1451705:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84340779","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}
Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.
{"title":"Identification System Based on Resolution Adjusted 2D Spectrogram of Driver's ECG for Intelligent Vehicle","authors":"Gyu-Ho Choi, Ki-Taek Lim, S. Pan","doi":"10.1155/2022/5404343","DOIUrl":"https://doi.org/10.1155/2022/5404343","url":null,"abstract":"Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"5 1","pages":"5404343:1-5404343:13"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75978157","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}
Currently, many people enjoy videos and music content through their smart devices while using public transportation. However, because passengers focus so much on content on their smart devices, they sometimes forget to disembark and miss their destination stations. Therefore, in this paper, we propose an application that can notify users via smart devices when they approach the drop-off point in public transportation using an inaudible high frequency. Inaudible frequency signals are generated with announcements from speakers installed on subways and city buses. Smart devices receive and analyze the signals through their built-in microphones and notify users when they reach the drop-off point. We tested destination notifications with the proposed system and 10 smart devices to evaluate its performance. According to the test results, the proposed system showed 99.4% accuracy on subways and 99.2% accuracy on city buses. Moreover, we compared these results to those using only subway app in subways, and our proposed system achieved far better outcomes. Thus, the proposed system could be a useful technology for notifying smart device users when to get off public transport, and it will become an innovative technology for global public transportation by informing users of their desired stations using speakers.
{"title":"A Disembarking Notification Application for Public Transportation Using Inaudible Frequency","authors":"Myoungbeom Chung","doi":"10.1155/2022/4775794","DOIUrl":"https://doi.org/10.1155/2022/4775794","url":null,"abstract":"Currently, many people enjoy videos and music content through their smart devices while using public transportation. However, because passengers focus so much on content on their smart devices, they sometimes forget to disembark and miss their destination stations. Therefore, in this paper, we propose an application that can notify users via smart devices when they approach the drop-off point in public transportation using an inaudible high frequency. Inaudible frequency signals are generated with announcements from speakers installed on subways and city buses. Smart devices receive and analyze the signals through their built-in microphones and notify users when they reach the drop-off point. We tested destination notifications with the proposed system and 10 smart devices to evaluate its performance. According to the test results, the proposed system showed 99.4% accuracy on subways and 99.2% accuracy on city buses. Moreover, we compared these results to those using only subway app in subways, and our proposed system achieved far better outcomes. Thus, the proposed system could be a useful technology for notifying smart device users when to get off public transport, and it will become an innovative technology for global public transportation by informing users of their desired stations using speakers.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"85 1","pages":"4775794:1-4775794:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76582979","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}
As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.
{"title":"Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing","authors":"Long Hao, Li-Min Zhou","doi":"10.1155/2022/9925930","DOIUrl":"https://doi.org/10.1155/2022/9925930","url":null,"abstract":"As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"44 1","pages":"9925930:1-9925930:9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75451153","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}
In order to explore the spillover effect of urbanization on rural land transfer, this paper uses the panel data of various regions and cities in Xinjiang from 2008 to 2018. Moran's I method is used to test and analyze the spatial correlation between urbanization and farmland transfer. Intelligent computing SDM is used to analyze the spillover effect of urbanization on farmland transfer. The results show that there is spatial correlation between farmland transfers in Xinjiang. There is spatial heterogeneity in the spatial agglomeration of urbanization and farmland transfer in northern and southern Xinjiang. The content of this paper can provide some reference and ideas for follow-up research.
{"title":"Research on Spillover Effect of Urbanization on Rural Land Transfer Based on the SDM Model of Intelligent Computing","authors":"Fucheng Yang, Guoyong Liu","doi":"10.1155/2022/9921309","DOIUrl":"https://doi.org/10.1155/2022/9921309","url":null,"abstract":"In order to explore the spillover effect of urbanization on rural land transfer, this paper uses the panel data of various regions and cities in Xinjiang from 2008 to 2018. Moran's I method is used to test and analyze the spatial correlation between urbanization and farmland transfer. Intelligent computing SDM is used to analyze the spillover effect of urbanization on farmland transfer. The results show that there is spatial correlation between farmland transfers in Xinjiang. There is spatial heterogeneity in the spatial agglomeration of urbanization and farmland transfer in northern and southern Xinjiang. The content of this paper can provide some reference and ideas for follow-up research.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"603 1","pages":"9921309:1-9921309:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77406919","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}
Safety is an essential topic for electric power plants. In recent years, accidents caused by unsafe behaviors of electric power plant employees are frequent. To promote the sustainable development and safety of electric power plants, studies on the assessment of unsafe behavior are becoming increasingly important and urgent. In this study, accident statistical analysis, literature review, and expert survey are adopted to select more comprehensive and accurate assessment indicators of unsafe behavior of the workers in electric power plants. Data about indicator and unsafe behavior were obtained through a questionnaire survey, and 27 indicators were used as inputs, and the unsafe behavior was taken as the output of a backpropagation (BP) neural network based unsafe behavior assessment model. An assessment indicator system about power plant workers’ unsafe behavior composed of 4 first-level indicators and 27 second-level indicators was established and the weights of the assessment indicators were determined. A three-layer feedforward BP neural network assessment model of “27-13-1” layers was found to be a suitable model. The proposed model can demonstrate the nonlinear complex relationship between the assessment indicator and the unsafe behavior of power plant workers. The model can be helpful to evaluate, predict, and monitor the safety performance of electric power plants.
{"title":"Assessment of Power Plant Based on Unsafe Behavior of Workers through Backpropagation Neural Network Model","authors":"Juan Shi, Ding-Tsair Chang","doi":"10.1155/2022/3169285","DOIUrl":"https://doi.org/10.1155/2022/3169285","url":null,"abstract":"Safety is an essential topic for electric power plants. In recent years, accidents caused by unsafe behaviors of electric power plant employees are frequent. To promote the sustainable development and safety of electric power plants, studies on the assessment of unsafe behavior are becoming increasingly important and urgent. In this study, accident statistical analysis, literature review, and expert survey are adopted to select more comprehensive and accurate assessment indicators of unsafe behavior of the workers in electric power plants. Data about indicator and unsafe behavior were obtained through a questionnaire survey, and 27 indicators were used as inputs, and the unsafe behavior was taken as the output of a backpropagation (BP) neural network based unsafe behavior assessment model. An assessment indicator system about power plant workers’ unsafe behavior composed of 4 first-level indicators and 27 second-level indicators was established and the weights of the assessment indicators were determined. A three-layer feedforward BP neural network assessment model of “27-13-1” layers was found to be a suitable model. The proposed model can demonstrate the nonlinear complex relationship between the assessment indicator and the unsafe behavior of power plant workers. The model can be helpful to evaluate, predict, and monitor the safety performance of electric power plants.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"38 1","pages":"3169285:1-3169285:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85572828","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}
In the new situation of Internet plus, information technology has been widely applied in education, and hence online education has attracted wide attention from all walks of life. Today’s society is a risk society, and risk is everywhere. Online education reform is also risky, which is determined by many reasons. Some risks will cause certain losses to the online education reform, so based on risky decision-making, it is necessary to carry out online education reform under the new situation of Internet plus. At first, the risky decision-making in online education reform is analyzed, which is the risk of online education reform in risk society and the allocation logic of online education reform. Then, taking interval type-2 fuzzy logic (IT2FL) as the information environment, this study proposes the optimal risky decision-making method based on IT2FL utility functions, IT2FL entropy, and risk preference factor of online education reform to solve the multipath risky decision-making problem of online education reform. Finally, the experimental results show that, in the risky decision-making model, the decision-maker’s risk preference has an impact on the path weight and the ranking of the scheme, and the idea has a certain reference role for risky decision-making. Compared with the three benchmarks, the proposed method has the fewest ranking time with the same ranking results.
{"title":"A Novel Online Education Reform Model Based on Risky Decision-Making under the Situation of Internet Plus","authors":"Yao Lv","doi":"10.1155/2022/9668631","DOIUrl":"https://doi.org/10.1155/2022/9668631","url":null,"abstract":"In the new situation of Internet plus, information technology has been widely applied in education, and hence online education has attracted wide attention from all walks of life. Today’s society is a risk society, and risk is everywhere. Online education reform is also risky, which is determined by many reasons. Some risks will cause certain losses to the online education reform, so based on risky decision-making, it is necessary to carry out online education reform under the new situation of Internet plus. At first, the risky decision-making in online education reform is analyzed, which is the risk of online education reform in risk society and the allocation logic of online education reform. Then, taking interval type-2 fuzzy logic (IT2FL) as the information environment, this study proposes the optimal risky decision-making method based on IT2FL utility functions, IT2FL entropy, and risk preference factor of online education reform to solve the multipath risky decision-making problem of online education reform. Finally, the experimental results show that, in the risky decision-making model, the decision-maker’s risk preference has an impact on the path weight and the ranking of the scheme, and the idea has a certain reference role for risky decision-making. Compared with the three benchmarks, the proposed method has the fewest ranking time with the same ranking results.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"2648 1","pages":"9668631:1-9668631:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86591081","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}
In order to actively respond to the government’s call to scientifically create campus football culture, combine the characteristics of football sports, and improve people’s understanding of the mental and intellectual functions of football, this article focuses on the impact of football training on physical function and football technology. Based on the understanding of related theories, the experiment on the impact of football training on physical function and football technology was carried out. The experimental results showed that the weight, height, and BMI increased significantly during the period of football training ( P < 0.05 ). The independent sample T test showed that there were no significant differences in height, weight, and BMI between the two groups before and after training; the standing long jump performance of the control group after training showed an upward trend, but the significance level was not statistically significant. Three months later, the time for the experimental team to complete the eight-character dribble test in football training was reduced from 20.51 seconds to 15.57 seconds. The independent sample T test found that there was no significant difference in the physical fitness of the two groups before training and the changes in football skills of the subjects before and after training. Then, the clustering algorithm in the big data was used to analyze the data of the experimental group. The standing long jump has the highest performance; the second category belongs to the third level, and the third category belongs to the second level.
{"title":"The Influence of Football Training Based on Big Data on Physical Function and Football Skills","authors":"Chang-zhou Hu, Yanghai Sun","doi":"10.1155/2022/1735022","DOIUrl":"https://doi.org/10.1155/2022/1735022","url":null,"abstract":"In order to actively respond to the government’s call to scientifically create campus football culture, combine the characteristics of football sports, and improve people’s understanding of the mental and intellectual functions of football, this article focuses on the impact of football training on physical function and football technology. Based on the understanding of related theories, the experiment on the impact of football training on physical function and football technology was carried out. The experimental results showed that the weight, height, and BMI increased significantly during the period of football training (\u0000 \u0000 P\u0000 <\u0000 0.05\u0000 \u0000 ). The independent sample T test showed that there were no significant differences in height, weight, and BMI between the two groups before and after training; the standing long jump performance of the control group after training showed an upward trend, but the significance level was not statistically significant. Three months later, the time for the experimental team to complete the eight-character dribble test in football training was reduced from 20.51 seconds to 15.57 seconds. The independent sample T test found that there was no significant difference in the physical fitness of the two groups before training and the changes in football skills of the subjects before and after training. Then, the clustering algorithm in the big data was used to analyze the data of the experimental group. The standing long jump has the highest performance; the second category belongs to the third level, and the third category belongs to the second level.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"36 1","pages":"1735022:1-1735022:8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84183816","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}
Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.
{"title":"Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence","authors":"Wenning Wu, Zheng-hong Deng","doi":"10.1155/2022/7745028","DOIUrl":"https://doi.org/10.1155/2022/7745028","url":null,"abstract":"Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"55 1","pages":"7745028:1-7745028:10"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86484836","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：