Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696758
Zhan Yuzhuo
Since the 21st century, the vigorous development of big data technology has led to its application in the electrical power systems, while some initial progress has been made in the research on the application of big data technology in the electric power system. This paper analyzes the application of big data technology in electric power system from the development status of big data technology as well as electric power system. The key technologies of the application of big data technology in the electric power system are divided into integration and management technology, data processing technology, data analysis technology and data visualization technology of electric power big data, and analyzed one by one. Meanwhile, this paper also lists the application examples of electric power mega data technology in smart grid, so as to confirm the development trend of electric power system under big data technology.
{"title":"A Study on Power System Development Trend through Comptuer Visualization and Big Data Technology","authors":"Zhan Yuzhuo","doi":"10.1109/ICESIT53460.2021.9696758","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696758","url":null,"abstract":"Since the 21st century, the vigorous development of big data technology has led to its application in the electrical power systems, while some initial progress has been made in the research on the application of big data technology in the electric power system. This paper analyzes the application of big data technology in electric power system from the development status of big data technology as well as electric power system. The key technologies of the application of big data technology in the electric power system are divided into integration and management technology, data processing technology, data analysis technology and data visualization technology of electric power big data, and analyzed one by one. Meanwhile, this paper also lists the application examples of electric power mega data technology in smart grid, so as to confirm the development trend of electric power system under big data technology.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050172","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696759
Biao Li, Jing Liu
With the increasing contradiction between less people and more stations and the development of front-end state sensing technology, data transmission technology and intelligent diagnosis technology, in order to effectively lighten the work strength of operation and maintenance staff and improve the operation and maintenance efficiency, we put forward the construction scheme of remote intelligent management platform, which makes use of modern information technology and advanced communication technology like the existing mobile Internet and artificial intelligence to realizes “Internet of Everything” and human-computer interaction at all stations under the jurisdiction, so as to make it an intelligent service system featuring comprehensive state perception, efficient information processing and convenient and flexible application.
{"title":"Research on Remote Intelligent Platform and Automatic Monitoring System of Transformer Substations","authors":"Biao Li, Jing Liu","doi":"10.1109/ICESIT53460.2021.9696759","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696759","url":null,"abstract":"With the increasing contradiction between less people and more stations and the development of front-end state sensing technology, data transmission technology and intelligent diagnosis technology, in order to effectively lighten the work strength of operation and maintenance staff and improve the operation and maintenance efficiency, we put forward the construction scheme of remote intelligent management platform, which makes use of modern information technology and advanced communication technology like the existing mobile Internet and artificial intelligence to realizes “Internet of Everything” and human-computer interaction at all stations under the jurisdiction, so as to make it an intelligent service system featuring comprehensive state perception, efficient information processing and convenient and flexible application.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127829074","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}
The social model is a huge virtual platform where to freely express themselves and give their views and feelings, influencing any aspect of life, with implications for marketing and communication alike. Aspect words sentiment analysis can more accurately understand user needs and improve enterprise marketing strategies. In current researches on aspect words sentiment analysis, researchers use the integration of attention mechanism and LSTM to obtain key information. However, there are few studies on the fusion of aspect words, context, and multi-layer deep memory networks. Therefore, we proposed a multi-layer deep memory network model based on the splicing of aspect terms and context vectors. The model can further strengthen the fusion between aspect words and context vectors, and make up for the shortcomings of LSTM in transmitting information loss. Experimental results on Restaurant and Laptop datasets show that the proposed method has a better performance.
{"title":"Aspect-words Sentiment analysis of commodity comments based on deep memory network","authors":"Wenjun Cheng, Jike Ge, Chengzhi Wu, Sheng Yu, Haoyin Liu, Jichao Xu","doi":"10.1109/ICESIT53460.2021.9696708","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696708","url":null,"abstract":"The social model is a huge virtual platform where to freely express themselves and give their views and feelings, influencing any aspect of life, with implications for marketing and communication alike. Aspect words sentiment analysis can more accurately understand user needs and improve enterprise marketing strategies. In current researches on aspect words sentiment analysis, researchers use the integration of attention mechanism and LSTM to obtain key information. However, there are few studies on the fusion of aspect words, context, and multi-layer deep memory networks. Therefore, we proposed a multi-layer deep memory network model based on the splicing of aspect terms and context vectors. The model can further strengthen the fusion between aspect words and context vectors, and make up for the shortcomings of LSTM in transmitting information loss. Experimental results on Restaurant and Laptop datasets show that the proposed method has a better performance.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133055302","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696936
Jing Li, Bin Zhang, Haiqing Li
The cutter is one critical component in a milling tool, and its operational condition directly affects the part machining quality and production efficiency. In this paper, a new method for milling cutters health monitoring is proposed. The proposed method extracts nonlinear entropy features with adaptive decomposition of the original multi-sensor monitoring signals. Then the extracted features are selected and adaptively fused into a virtual health indicator (HI) by self-organizing mapping (SOM) network to characterize the operational health condition of the milling cutter. High speed milling data from 2010 prognostics and health management (PHM) challenge is studied to demonstrate performance of the presented method. Experimental results show that the approach can effectively integrate the online multi-sensor signals to reliably describe health degradation of the milling cutter.
{"title":"Health Monitoring of Milling Cutters with Nonlinear Entropy and Self-organizing Mapping","authors":"Jing Li, Bin Zhang, Haiqing Li","doi":"10.1109/ICESIT53460.2021.9696936","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696936","url":null,"abstract":"The cutter is one critical component in a milling tool, and its operational condition directly affects the part machining quality and production efficiency. In this paper, a new method for milling cutters health monitoring is proposed. The proposed method extracts nonlinear entropy features with adaptive decomposition of the original multi-sensor monitoring signals. Then the extracted features are selected and adaptively fused into a virtual health indicator (HI) by self-organizing mapping (SOM) network to characterize the operational health condition of the milling cutter. High speed milling data from 2010 prognostics and health management (PHM) challenge is studied to demonstrate performance of the presented method. Experimental results show that the approach can effectively integrate the online multi-sensor signals to reliably describe health degradation of the milling cutter.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133784666","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696720
Liu Jiahong, Bao Jie, Chen Yingshuang, Lv Chun
In this paper, a speaker recognition strategy in military radio communication is applied. In military operations, the most commonly used method of information transmission is radio communication. Speaker recognition technology can confirm the sender's identity, and effectively prevent the enemy from pretending to be our military commander to issue false orders. However, the datasets of the military commander from the radio are confidential, and there are no large open-source datasets. Consequently, speaker recognition accuracy is not ideal if we only train a small sample of speaker datasets. Therefore, we propose a transfer learning method for training. We pre-train a Deep Residual neural network (ResNet) with large sample datasets and re-train a novel adaptive model with a simple sample dataset in radio communication. Experiments are carried out using the aishell-2 dataset and the self-collected radio military command datasets. Experimental results demonstrate that the adaptive network with transfer learning method improves the performance by 23.55% relatively compared to the baseline system in radio communication.
{"title":"An Adaptive ResNet Based Speaker Recognition in Radio Communication","authors":"Liu Jiahong, Bao Jie, Chen Yingshuang, Lv Chun","doi":"10.1109/ICESIT53460.2021.9696720","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696720","url":null,"abstract":"In this paper, a speaker recognition strategy in military radio communication is applied. In military operations, the most commonly used method of information transmission is radio communication. Speaker recognition technology can confirm the sender's identity, and effectively prevent the enemy from pretending to be our military commander to issue false orders. However, the datasets of the military commander from the radio are confidential, and there are no large open-source datasets. Consequently, speaker recognition accuracy is not ideal if we only train a small sample of speaker datasets. Therefore, we propose a transfer learning method for training. We pre-train a Deep Residual neural network (ResNet) with large sample datasets and re-train a novel adaptive model with a simple sample dataset in radio communication. Experiments are carried out using the aishell-2 dataset and the self-collected radio military command datasets. Experimental results demonstrate that the adaptive network with transfer learning method improves the performance by 23.55% relatively compared to the baseline system in radio communication.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134078259","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696613
Baosheng Xie, Zhikai Lin, Jianbiao Chen, I. Maizura
The enterprise economy integrated with artificial intelligence computer information system is an important part of the modern enterprise system. We need to do a good job in the economy of computer information system integration to perfect the system of modern enterprises. Based on this research background, the paper designs an enterprise management project management information system. At the same time, we gave the overall structure of the design system and the functional modules of each subsystem, and finally completed the system development. The various functions of this system meet the design requirements, and can improve and standardize the management strategy of the enterprise.
{"title":"Application of artificial intelligence computer control technology in management information system","authors":"Baosheng Xie, Zhikai Lin, Jianbiao Chen, I. Maizura","doi":"10.1109/ICESIT53460.2021.9696613","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696613","url":null,"abstract":"The enterprise economy integrated with artificial intelligence computer information system is an important part of the modern enterprise system. We need to do a good job in the economy of computer information system integration to perfect the system of modern enterprises. Based on this research background, the paper designs an enterprise management project management information system. At the same time, we gave the overall structure of the design system and the functional modules of each subsystem, and finally completed the system development. The various functions of this system meet the design requirements, and can improve and standardize the management strategy of the enterprise.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134426190","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696940
Yi Wang, Zongfeng Li
In order to meet the measurement requirements of the end force of the robot finger, this paper designs a miniature six-axis force/torque sensor based on the Y-shaped beam. Mechanical modeling, static analysis, bridge selection and least square method decoupling are carried out on the proposed six-axis force/torque sensor, which ensures the feasibility of the scheme and measurement accuracy.
{"title":"Structure design of elastomer of miniature six-axis force/torque sensor based on Y-type beam","authors":"Yi Wang, Zongfeng Li","doi":"10.1109/ICESIT53460.2021.9696940","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696940","url":null,"abstract":"In order to meet the measurement requirements of the end force of the robot finger, this paper designs a miniature six-axis force/torque sensor based on the Y-shaped beam. Mechanical modeling, static analysis, bridge selection and least square method decoupling are carried out on the proposed six-axis force/torque sensor, which ensures the feasibility of the scheme and measurement accuracy.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216639","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696872
Bin Zhao, Boyu Zhao, Pengfei Li
With the development of society, the application of abnormal behavior detection in the field of public safety has become more and more extensive. We propose a frame prediction video behavior anomaly detection model based on Generative Adversarial Network (GAN). We use the U-net network with the feature storage module and variance attention mechanism as the generator, which not only increases the network's sensitivity to the movement part of the sample, but also reduces the network's learning ability and limits the network's ability to predict abnormal samples. For the discriminant model, we have added a channel and spatial attention mechanism to the Markov discriminator to improve the discrimination ability, which is conducive to improving the quality of future frame generation. Compared with the existing abnormal behavior detection methods, our proposed model achieves excellent detection performance.
{"title":"Video Anomaly Detection Based on Frame Prediction of Generative Adversarial Network","authors":"Bin Zhao, Boyu Zhao, Pengfei Li","doi":"10.1109/ICESIT53460.2021.9696872","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696872","url":null,"abstract":"With the development of society, the application of abnormal behavior detection in the field of public safety has become more and more extensive. We propose a frame prediction video behavior anomaly detection model based on Generative Adversarial Network (GAN). We use the U-net network with the feature storage module and variance attention mechanism as the generator, which not only increases the network's sensitivity to the movement part of the sample, but also reduces the network's learning ability and limits the network's ability to predict abnormal samples. For the discriminant model, we have added a channel and spatial attention mechanism to the Markov discriminator to improve the discrimination ability, which is conducive to improving the quality of future frame generation. Compared with the existing abnormal behavior detection methods, our proposed model achieves excellent detection performance.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122336965","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696972
Xiaoyue Jia, Fengchun Liu
With the rapid development of China's mobile Internet and the advent of 5g era, employees from all walks of life will basically use websites to buy all kinds of goods needed in life. As we all know, big data has become a key direction in the work of various Internet companies and the recommendation system can be said to be one of the best landing applications of big data. The benefits it brings to Internet companies are real and visible. Especially for e-commerce, intelligent recommendation system can directly affect the sales performance of an e-commerce enterprise[1]. How to store these massive data and efficiently mine valuable user information is the real challenge of big data technology[2]. In this paper, based on the modified Chinese Amazon e-commerce data set well-known in the field of recommendation system construction, and based on the real business data architecture of an e-commerce website, the project constructs an integrated e-commerce recommendation system, offline recommendation service and real-time recommendation service provide a variety of methods to achieve mixed recommendation effect. It provides a variety of off-line analysis methods and clever and accurate real-time recommendation model to realize data mining.
{"title":"Research on intelligent recommendation system model supported by data mining and algorithm optimization","authors":"Xiaoyue Jia, Fengchun Liu","doi":"10.1109/ICESIT53460.2021.9696972","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696972","url":null,"abstract":"With the rapid development of China's mobile Internet and the advent of 5g era, employees from all walks of life will basically use websites to buy all kinds of goods needed in life. As we all know, big data has become a key direction in the work of various Internet companies and the recommendation system can be said to be one of the best landing applications of big data. The benefits it brings to Internet companies are real and visible. Especially for e-commerce, intelligent recommendation system can directly affect the sales performance of an e-commerce enterprise[1]. How to store these massive data and efficiently mine valuable user information is the real challenge of big data technology[2]. In this paper, based on the modified Chinese Amazon e-commerce data set well-known in the field of recommendation system construction, and based on the real business data architecture of an e-commerce website, the project constructs an integrated e-commerce recommendation system, offline recommendation service and real-time recommendation service provide a variety of methods to achieve mixed recommendation effect. It provides a variety of off-line analysis methods and clever and accurate real-time recommendation model to realize data mining.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121442004","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 : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696710
Baoxin Chen, Kan Chen, Xi Wang, Xu Wang
Based on the flood disaster loss data during1980-2019 in various prefectures of Tarim Basin. Disaster loss indicators are: disaster area, collapsed house, deaths toll, dead livestock, collapsed shed. By calculating the catastrophe index and using the gray correlation analysis method to compare the risk level of disaster loss in each prefecture. Disaster loss of Aksu has the greatest correlation with the damage area. In Bazhou prefecture, it has the greatest correlation with the dead livestock. In Hotan prefecture, it has the greatest correlation with the collapsed shed. In Kashgar prefecture, it has the greatest correlation with the disaster area. And in Kezhou prefecture, it has the greatest correlation with the dead livestock. It should be noted that the correlation degree of dead livestock caused by floods in Kezhou prefecture and the correlation degree of collapsed shed caused by floods in Hotan prefecture are higher (R>0.8). The risk analysis of flood disaster loss in Tarim Basin is significant for disaster management.
{"title":"Research on Data Collection and Catastrophe Index Using Grey Relational Analysis","authors":"Baoxin Chen, Kan Chen, Xi Wang, Xu Wang","doi":"10.1109/ICESIT53460.2021.9696710","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696710","url":null,"abstract":"Based on the flood disaster loss data during1980-2019 in various prefectures of Tarim Basin. Disaster loss indicators are: disaster area, collapsed house, deaths toll, dead livestock, collapsed shed. By calculating the catastrophe index and using the gray correlation analysis method to compare the risk level of disaster loss in each prefecture. Disaster loss of Aksu has the greatest correlation with the damage area. In Bazhou prefecture, it has the greatest correlation with the dead livestock. In Hotan prefecture, it has the greatest correlation with the collapsed shed. In Kashgar prefecture, it has the greatest correlation with the disaster area. And in Kezhou prefecture, it has the greatest correlation with the dead livestock. It should be noted that the correlation degree of dead livestock caused by floods in Kezhou prefecture and the correlation degree of collapsed shed caused by floods in Hotan prefecture are higher (R>0.8). The risk analysis of flood disaster loss in Tarim Basin is significant for disaster management.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116413307","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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