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2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)最新文献

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Evaluation Model of Asphalt Mixture Road Performance Based on Data Mining 基于数据挖掘的沥青混合料路用性能评价模型
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332819
Sun Jian-guo, Sun Gui-e
{"title":"Evaluation Model of Asphalt Mixture Road Performance Based on Data Mining","authors":"Sun Jian-guo, Sun Gui-e","doi":"10.1109/IAAI51705.2020.9332819","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332819","url":null,"abstract":"In order to solve the problem of evaluation error in traditional road performance evaluation model, the optimization design of asphalt mixture pavement performance evaluation model is realized by using data mining technology. Design and set up evaluation standards for the use of asphalt pavement, and use data mining technology in the network environment to obtain relevant data on asphalt mixture road performance, and use this as the input of the model. The asphalt mixture is classified, and the corresponding influencing factors are analyzed for different types, combined with the asphalt mixture type and influencing factors, the road performance evaluation index is selected, and the corresponding weight value is calculated. Combined with the input value of the model and the test results of asphalt mixture pavement performance, the comprehensive road performance evaluation data is obtained, and the final evaluation result of asphalt mixture pavement performance is obtained by comparing with the evaluation standard. Compared with the traditional evaluation model, the error is reduced by 27.4 points.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127933442","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}
引用次数: 1
Research on Distributed Storage Data Consistency Control Method Based on Pattern Recognition 基于模式识别的分布式存储数据一致性控制方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332822
Ran Jun-Yi, Liu Zhao
{"title":"Research on Distributed Storage Data Consistency Control Method Based on Pattern Recognition","authors":"Ran Jun-Yi, Liu Zhao","doi":"10.1109/IAAI51705.2020.9332822","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332822","url":null,"abstract":"The traditional consistency control method of distributed storage data, the recognition ability of the selected algorithm is poor, which leads to the low consistency control index. According to the relay node, the implementation control network is deployed; the consistency algorithm of distributed storage data is designed according to the consistency constraint rules; and the consistency of distributed storage data is controlled by constructing a control model based on pattern recognition. Experimental results: compared with traditional methods, this research method has stronger recognition ability and higher consistency control index. It can be seen that pattern recognition can effectively classify data and meet the requirements of this research.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313948","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}
引用次数: 0
Design of Risk Assessment System for College Students' Mainstream Cultural Identity in the Era of Micro Communication 微传播时代大学生主流文化认同风险评估系统设计
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332825
Zhu Fang-fei, Wang Ling-bing
{"title":"Design of Risk Assessment System for College Students' Mainstream Cultural Identity in the Era of Micro Communication","authors":"Zhu Fang-fei, Wang Ling-bing","doi":"10.1109/iaai51705.2020.9332825","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332825","url":null,"abstract":"In view of the influence of the differences between Chinese and western mainstream culture on the risk assessment of College Students' mainstream culture identity, in order to improve the feasibility of risk assessment of College Students' mainstream cultural identity, this paper puts forward the design of risk assessment system of College Students' mainstream cultural identity in the era of micro communication. By extracting the risk characteristics of College Students' mainstream cultural identity, this paper constructs the risk assessment model of College Students' mainstream cultural identity, and adopts the method of minimum mean square error compensation to carry out adaptive quantitative evaluation, so as to realize the quantitative assessment of College Students' mainstream cultural identity risk. The test results show that the risk assessment system of College Students' mainstream culture identity in the era of micro communication is feasible and accurate.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347249","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}
引用次数: 0
Online Evaluation System of Students' Daily Emotional Intervention Effect Based on Human Computer Interaction Platform 基于人机交互平台的学生日常情绪干预效果在线评价系统
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332848
Peng Jie, Gao Hui
{"title":"Online Evaluation System of Students' Daily Emotional Intervention Effect Based on Human Computer Interaction Platform","authors":"Peng Jie, Gao Hui","doi":"10.1109/IAAI51705.2020.9332848","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332848","url":null,"abstract":"At present, students' psychological problems have become the focus of social concern. In the daily emotional intervention, the online evaluation system of students' daily emotional intervention effect is poor. The main reason is that the system has poor command analysis ability for users. To solve the above problems, an online evaluation system of students' daily emotional intervention effect based on human-computer interaction platform is designed. ADuC834 MCU is used as data processing chip, and FPGA core board is used as human-computer interaction chip. The two chips are set as the core processor of the system, and the power circuit is set according to the processor. The weight of influencing factors of artificial intervention effect was calculated, and the evaluation model of artificial intervention effect of students' daily emotion was constructed to complete the intervention effect evaluation. At the same time, the human-computer interaction program is set to realize the communication between the user and the system. So far, the online evaluation system of students' daily emotional intervention effect based on human-computer interaction platform has been completed. Through the comparison with other two traditional systems, the response time and user parallelism of this system are obviously better than the traditional system. In conclusion, the performance of the online evaluation system for students' daily emotional intervention based on human-computer interaction platform has been significantly improved.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129637701","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}
引用次数: 1
Performance Prediction Method of Bridge Asphalt Pavement Material Based on Grey Theory 基于灰色理论的桥梁沥青路面材料性能预测方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332886
Xulan Gui, Xinghang Long, Weiwei Zhan
{"title":"Performance Prediction Method of Bridge Asphalt Pavement Material Based on Grey Theory","authors":"Xulan Gui, Xinghang Long, Weiwei Zhan","doi":"10.1109/IAAI51705.2020.9332886","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332886","url":null,"abstract":"Under the combined action of natural environment and traffic load, the performance of asphalt pavement materials for asphalt concrete bridges is declining, which directly affects the speed of vehicles and the safety, economy and comfort of transportation. Therefore, according to the series grey prediction theory of grey system theory, the index prediction model is established for the basic indexes (PCI, SSI, RQI, SFC) in the service performance of bridge asphalt pavement, and the accuracy of the model is verified by residual error and correlation degree. Through the case analysis, it is concluded that the prediction results of the prediction model are similar to the actual situation, and the corresponding maintenance measures are put forward according to the prediction results. The research conclusions can provide a strong reference for the operation management and maintenance of highway bridges, and have certain guiding significance for improving the durability of bridges.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115447637","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}
引用次数: 0
STM32 Single Chip Microcomputer Signal Recognition Method Based on Sparse Filtering Neural Network 基于稀疏滤波神经网络的STM32单片机信号识别方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332889
Ya-Hui Zhou, Men Tong
{"title":"STM32 Single Chip Microcomputer Signal Recognition Method Based on Sparse Filtering Neural Network","authors":"Ya-Hui Zhou, Men Tong","doi":"10.1109/IAAI51705.2020.9332889","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332889","url":null,"abstract":"Due to the high dimension of STM32 signal characteristic graph, the signal recognition process will produce signal overlap, resulting in the decline of recognition rate. In order to solve this problem, a signal recognition method based on sparse filter neural network is designed. The signal type of STM32 single-chip microcomputer is defined, the characteristic parameters of single-chip microcomputer signal are determined, and the interference caused by overlapping signal is eliminated by sparse filtering of overlapping signal. The identification process is controlled by neural network of full connection layer, and finally the recognition of STM32 single-chip microcomputer signal is realized. The experimental results show that the proposed method has higher recognition rate and reliability.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115725052","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}
引用次数: 0
Research on drive control method of induction motor based on random forest 基于随机森林的感应电机驱动控制方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332861
Wen-Mian Shao, Xian-zhong Liu
{"title":"Research on drive control method of induction motor based on random forest","authors":"Wen-Mian Shao, Xian-zhong Liu","doi":"10.1109/IAAI51705.2020.9332861","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332861","url":null,"abstract":"The induction motor is one of the key hardware for the normal operation of mechanical equipment. However, due to the poor performance of the controller designed by the traditional method, it is easy to cause the drive control instability. Therefore, the induction motor drive control method based on random forest is studied. This method obtains in advance the working characteristics of induction motors in high and low temperature environments; considering the working characteristics, a new induction motor drive controller is designed based on the random forest algorithm; and the induction motor drive control is realized by setting fuzzy rules. The experimental results show that in the high temperature and low temperature test environment, the drive control method studied in this study has regularity in the control wave amplitude and the control effect is more stable. It can be seen that the random forest theory meets the requirements of this research.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869365","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}
引用次数: 0
Prediction Model of Financial Income of Listed Companies Based on Grey Model 基于灰色模型的上市公司财务收益预测模型
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332821
Li Tao
{"title":"Prediction Model of Financial Income of Listed Companies Based on Grey Model","authors":"Li Tao","doi":"10.1109/IAAI51705.2020.9332821","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332821","url":null,"abstract":"The traditional financial income prediction model of listed companies is not accurate enough for the medium and long-term prediction results, so a grey model based on the financial income prediction model of listed companies is designed. First of all, the risk prediction ability of this model is improved by accumulating the risk statistical model and learning the risk model. In the case application analysis, we use the designed prediction model and the traditional model to predict the financial income of a company. The experimental results show that the results of the designed prediction model in the long-term forecast are closer to the actual situation.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521072","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}
引用次数: 0
Process Prediction Method of Networked Ammunition Cooperative Operation based on Effectiveness Evaluation 基于效能评估的网络化弹药协同作战过程预测方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332832
Yao-Hua Huang, Xin Ning
{"title":"Process Prediction Method of Networked Ammunition Cooperative Operation based on Effectiveness Evaluation","authors":"Yao-Hua Huang, Xin Ning","doi":"10.1109/IAAI51705.2020.9332832","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332832","url":null,"abstract":"Networked munitions coordinated operations is an effective modern warfare method. In order to better apply the networked munitions coordinated operations effectiveness evaluation to the integrated analysis of actual operations, a networked munitions coordinated operations process based on effectiveness evaluation is proposed method of prediction. Analyze the characteristics of the combat plan reflecting the battlefield situation during actual combat or simulation deduction, and construct corresponding effectiveness evaluation indicators for networked ammunition operations. By collecting the target recognition rate in the process of networked ammunition coordinated operations, the target kill rate and false target attack rate are analyzed to obtain the cost-effectiveness ratio. The effectiveness evaluation index is used to accurately predict the cooperative operation process of networked ammunition to achieve the prediction requirements. The simulation results show that the prediction accuracy of the network ammunition cooperative operation process prediction method based on effectiveness evaluation is higher, and it can better realize the efficient and reliable network ammunition cooperative operation effectiveness.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919772","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}
引用次数: 0
Research on Displacement Monitoring Method of Heavy Load Bridge Structure Based on Stereo Vision 基于立体视觉的重载桥梁结构位移监测方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332870
Xinghang Long, Weiwei Zhan, Xulan Gui
{"title":"Research on Displacement Monitoring Method of Heavy Load Bridge Structure Based on Stereo Vision","authors":"Xinghang Long, Weiwei Zhan, Xulan Gui","doi":"10.1109/IAAI51705.2020.9332870","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332870","url":null,"abstract":"The traditional sensor monitoring method has many problems in actual bridge health monitoring, such as low measurement accuracy and low sampling frequency. Based on this, a method for monitoring the displacement of a heavy-duty bridge structure based on stereo vision is proposed. The various physical parameters of the bridge structure are continuously acquired by installing different types of sensors, and the data-driven structure state assessment method is used to diagnose the performance and safety status of the bridge. Combined with stereo vision technology and traditional civil bridge structure monitoring methods, a set of bridge health monitoring and state evaluation system based on stereo vision was constructed to complete the structural displacement monitoring. Vibration monitoring and dynamic characteristics identification of the simply supported beams of the building simulated bridge deck and the vertical rods of the simulated bridge tower were compared with the traditional acceleration sensor method to verify that the method proposed in this paper has the advantages of structural vibration monitoring and dynamic characteristics identification. Feasibility and accuracy.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847846","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}
引用次数: 2
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