Pub Date : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482236
Lin Huo, Yan Zhang, Jianwen Zhang, Fei Liu, Jing Liang, Yao Wang
Distribution network work order is one of the important indicators to reflect the operation and management level of distribution network, which has important information value for providing auxiliary production decision-making of distribution network. In the face of massive distribution network work order data, need to solve the problem of how to transform the data into auxiliary decision-making information. In this paper, through machine learning, big data and other artificial intelligence technology and data mining technology, the distribution network operation index correlation analysis and intelligent prediction, found the inherent law between the operation index, realized accurate operation and maintenance and scientific decision-making. Firstly, the distribution network work order data was preprocessed to clean the error and abnormal data, and the fuzzy algorithm was used to match the corresponding station area according to the lack of station area. Secondly, the type and characteristics of each type of work order were analyzed, and the distribution network work order index mining was carried out through PrefixSpan algorithm. Finally, the effectiveness of the proposed algorithm was verified through the actual data, and the operation and maintenance of the distribution network were analyzed The paper put forward the preventive measures for the weak links in the service.
{"title":"Analysis of Multi Index Association of Power Grid Work Order based on Data Mining","authors":"Lin Huo, Yan Zhang, Jianwen Zhang, Fei Liu, Jing Liang, Yao Wang","doi":"10.1109/IMCEC51613.2021.9482236","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482236","url":null,"abstract":"Distribution network work order is one of the important indicators to reflect the operation and management level of distribution network, which has important information value for providing auxiliary production decision-making of distribution network. In the face of massive distribution network work order data, need to solve the problem of how to transform the data into auxiliary decision-making information. In this paper, through machine learning, big data and other artificial intelligence technology and data mining technology, the distribution network operation index correlation analysis and intelligent prediction, found the inherent law between the operation index, realized accurate operation and maintenance and scientific decision-making. Firstly, the distribution network work order data was preprocessed to clean the error and abnormal data, and the fuzzy algorithm was used to match the corresponding station area according to the lack of station area. Secondly, the type and characteristics of each type of work order were analyzed, and the distribution network work order index mining was carried out through PrefixSpan algorithm. Finally, the effectiveness of the proposed algorithm was verified through the actual data, and the operation and maintenance of the distribution network were analyzed The paper put forward the preventive measures for the weak links in the service.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126499809","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482152
Yiting Zheng, Wei Nai, Y.T. Ren
With the continuous development of automation and computer technology, information tools with high computational ability has been widely employed in stock transaction industry. Moreover, due to the subjective influence of people and the unstable return of traditional investment, more and more institutions gradually start to use the quantitative stock selection model in order to get higher return in pursuit of lower risk. As the most classic algorithm in quantitative stock selection, multi factor model is favored by many institutional investors. However, due to the difference of financial system between China and western countries, the existing quantitative model in western countries is not fully applicable in China. At the same time, the previous multi factor stock selection models have more subjective factors and less consideration of time series, so the earning rate is usually not so good. In order to pursue a higher earning rate, in this paper, by taking the impact of time series into consideration, a multi factor quantitative stock and transaction timing selection model has been proposed based on information coefficient (IC) mean value scoring. And by choosing constituent stocks in Shanghai and Shenzhen 300 (HS300) Stock Index have been chosen as the target, the effectiveness of proposed model has been analyzed and verified.
{"title":"Multi Factor Quantitative Stock and Transaction Timing Selection Model Based on Information Coefficient Mean Value Scoring","authors":"Yiting Zheng, Wei Nai, Y.T. Ren","doi":"10.1109/IMCEC51613.2021.9482152","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482152","url":null,"abstract":"With the continuous development of automation and computer technology, information tools with high computational ability has been widely employed in stock transaction industry. Moreover, due to the subjective influence of people and the unstable return of traditional investment, more and more institutions gradually start to use the quantitative stock selection model in order to get higher return in pursuit of lower risk. As the most classic algorithm in quantitative stock selection, multi factor model is favored by many institutional investors. However, due to the difference of financial system between China and western countries, the existing quantitative model in western countries is not fully applicable in China. At the same time, the previous multi factor stock selection models have more subjective factors and less consideration of time series, so the earning rate is usually not so good. In order to pursue a higher earning rate, in this paper, by taking the impact of time series into consideration, a multi factor quantitative stock and transaction timing selection model has been proposed based on information coefficient (IC) mean value scoring. And by choosing constituent stocks in Shanghai and Shenzhen 300 (HS300) Stock Index have been chosen as the target, the effectiveness of proposed model has been analyzed and verified.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125844109","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482276
Wenxiang Zhang, Youquan Lin, Long Zhuang, Jie Guo
In this paper, we propose a variational autoencoder (VAE) based generative model with particular regard to the aspect angle sensitivity of the radar HRRP data of maritime vessels, and conduct high-resolution range profile (HRRP) data augmentation experiments to improve the recognition performance. Specifically, we train the extended conditional Variational auto-encoder (ECVAE) model to reconstruction data, and consider the latent space distribution of the sample as a more general multidimensional posterior Gaussian distribution. Discrete or continuous labels can be input to the model. Design a periodic latent distribution to deal with periodic labels. Use Kullback-Leibler (KL) divergence to evaluate the similarity of the distribution and reconstruct data with the latent space distribution which making the dimension as low as possible. Experiments based on MNIST data and measured vessels HRRP data show that the ECVAE model can augment the data of samples to improve recognition Performance, in especial in the case of a small number of data samples.
{"title":"Radar HRRP Data Augmentation Using CVAE with Extended Latent Space Distribution","authors":"Wenxiang Zhang, Youquan Lin, Long Zhuang, Jie Guo","doi":"10.1109/IMCEC51613.2021.9482276","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482276","url":null,"abstract":"In this paper, we propose a variational autoencoder (VAE) based generative model with particular regard to the aspect angle sensitivity of the radar HRRP data of maritime vessels, and conduct high-resolution range profile (HRRP) data augmentation experiments to improve the recognition performance. Specifically, we train the extended conditional Variational auto-encoder (ECVAE) model to reconstruction data, and consider the latent space distribution of the sample as a more general multidimensional posterior Gaussian distribution. Discrete or continuous labels can be input to the model. Design a periodic latent distribution to deal with periodic labels. Use Kullback-Leibler (KL) divergence to evaluate the similarity of the distribution and reconstruct data with the latent space distribution which making the dimension as low as possible. Experiments based on MNIST data and measured vessels HRRP data show that the ECVAE model can augment the data of samples to improve recognition Performance, in especial in the case of a small number of data samples.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981671","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482243
Yuxiang Zou
The risk of online public opinion continues to exist after the epidemic. This paper proposes an information clustering method based on cloud computing to build a risk prevention and control model of online public opinion in colleges and universities. Firstly, a measurement model of network public opinion similarity is constructed, and a method to determine the optimal threshold of public opinion clustering is designed. Secondly, the public opinions on the same topic are clustered according to the similarity between opinions and the determined clustering threshold. Finally, using the parallel computing ability of cloud computing, public opinions on different topics can be gathered quickly and accurately to provide data support for the risk prevention and control of public opinions in universities. The experimental results show that this method can quickly obtain the public opinion on the network of colleges and universities, and has a high clustering accuracy. Therefore, this method can provide some support for the real-time monitoring of online public opinion in colleges and universities.
{"title":"Risks prevention and control of university online public opinion based on data clustering algorithm","authors":"Yuxiang Zou","doi":"10.1109/IMCEC51613.2021.9482243","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482243","url":null,"abstract":"The risk of online public opinion continues to exist after the epidemic. This paper proposes an information clustering method based on cloud computing to build a risk prevention and control model of online public opinion in colleges and universities. Firstly, a measurement model of network public opinion similarity is constructed, and a method to determine the optimal threshold of public opinion clustering is designed. Secondly, the public opinions on the same topic are clustered according to the similarity between opinions and the determined clustering threshold. Finally, using the parallel computing ability of cloud computing, public opinions on different topics can be gathered quickly and accurately to provide data support for the risk prevention and control of public opinions in universities. The experimental results show that this method can quickly obtain the public opinion on the network of colleges and universities, and has a high clustering accuracy. Therefore, this method can provide some support for the real-time monitoring of online public opinion in colleges and universities.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450354","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482018
Yan Zhou, Mengdou Qin, Xu Wang, Cong Zhang
Multiple scene video surveillance systems can be combined through GIS technology to eliminate scene restriction and complete regional crowd state analysis. Regional surveillance video data is huge and complex, and its data screening, processing and analysis are difficult. Aiming at the problem of regional crowd state monitoring, this paper proposes a method of crowd state analysis based on GeoVideo and multimedia data cooperation. This method integrates social media data information, uses urban POI data and microblog sign-in data to extract hotspot areas to screen or collect the monitoring video of the hot area, then extracts the crowd state feature of the video and combines the VideoGIS to complete crowd status analysis in this area. Finally, the experiments which were designed for crowd flow monitoring confirm the feasibility and correctness of the method on the regional crowd state monitoring and analysis.
{"title":"Regional Crowd Status Analysis based on GeoVideo and Multimedia Data Collaboration","authors":"Yan Zhou, Mengdou Qin, Xu Wang, Cong Zhang","doi":"10.1109/IMCEC51613.2021.9482018","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482018","url":null,"abstract":"Multiple scene video surveillance systems can be combined through GIS technology to eliminate scene restriction and complete regional crowd state analysis. Regional surveillance video data is huge and complex, and its data screening, processing and analysis are difficult. Aiming at the problem of regional crowd state monitoring, this paper proposes a method of crowd state analysis based on GeoVideo and multimedia data cooperation. This method integrates social media data information, uses urban POI data and microblog sign-in data to extract hotspot areas to screen or collect the monitoring video of the hot area, then extracts the crowd state feature of the video and combines the VideoGIS to complete crowd status analysis in this area. Finally, the experiments which were designed for crowd flow monitoring confirm the feasibility and correctness of the method on the regional crowd state monitoring and analysis.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132195265","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482310
Yonghui Guo, Yuntao Li, Yu Zhang
The high resolution ISAR two dimensional image is very important for the detection and recognition of long-range non-cooperative targets such as satellites, missiles, aircraft, etc. However, traditional ISAR imaging requires a long imaging time to improve the imaging resolution and usually requires constant velocity motion of the target. To solve the above problems, many researchers have proposed an imaging radar which combines MIMO (Multiple Input Multiple Output) with ISAR (Inverse Synthetical Aperture Radar) technology, which theoretically overcomes the shortcomings of traditional ISAR. Therefore, this paper conclusion MIMO-ISAR high-resolution imaging from three aspects, first summarizes the sub-band synthesis technology to achieve one-dimensional distance image high-resolution. Second, systematically expounds the basic method of Doppler blurring of MIMO-ISAR echo data. Finally, the MIMO-ISAR imaging algorithm is summarized and the performance of the existing methods is evaluated. Based on the analysis of the above three aspects, the further research direction of MIMO-ISAR high resolution imaging technology is prospected.
{"title":"Research on MIMO-ISAR High Resolution Imaging Technology","authors":"Yonghui Guo, Yuntao Li, Yu Zhang","doi":"10.1109/IMCEC51613.2021.9482310","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482310","url":null,"abstract":"The high resolution ISAR two dimensional image is very important for the detection and recognition of long-range non-cooperative targets such as satellites, missiles, aircraft, etc. However, traditional ISAR imaging requires a long imaging time to improve the imaging resolution and usually requires constant velocity motion of the target. To solve the above problems, many researchers have proposed an imaging radar which combines MIMO (Multiple Input Multiple Output) with ISAR (Inverse Synthetical Aperture Radar) technology, which theoretically overcomes the shortcomings of traditional ISAR. Therefore, this paper conclusion MIMO-ISAR high-resolution imaging from three aspects, first summarizes the sub-band synthesis technology to achieve one-dimensional distance image high-resolution. Second, systematically expounds the basic method of Doppler blurring of MIMO-ISAR echo data. Finally, the MIMO-ISAR imaging algorithm is summarized and the performance of the existing methods is evaluated. Based on the analysis of the above three aspects, the further research direction of MIMO-ISAR high resolution imaging technology is prospected.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130045891","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482253
Baowei Tang, Lu Yang, Haixia Su, Junwei He
The increasing number, variety and complexity of spacecraft are placing greater demands on spacecraft testing. This paper analyses the future of 5G (The fifth generation mobile communication network) technology in the field of spacecraft testing. The application of 5G technology to spacecraft testing will lead to a change in the existing testing paradigm, increase the automation, digitalization and intelligence of testing, improve the sharing of test data and shorten the time required for testing. This paper explores the prospects and feasibility of applying 5G technology to satellite testing, where intelligent testing on satellites can optimise spacecraft layout and will also reduce the hazards associated with mishandling during manual testing. Data from the all test process can also be recorded, making it easy to apply this data to other areas of space technology and management. In the future, it will also be of great research value for the realization of multi-star parallel testing.
{"title":"Analysis of the Application of 5G Technology in Spacecraft Testing","authors":"Baowei Tang, Lu Yang, Haixia Su, Junwei He","doi":"10.1109/IMCEC51613.2021.9482253","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482253","url":null,"abstract":"The increasing number, variety and complexity of spacecraft are placing greater demands on spacecraft testing. This paper analyses the future of 5G (The fifth generation mobile communication network) technology in the field of spacecraft testing. The application of 5G technology to spacecraft testing will lead to a change in the existing testing paradigm, increase the automation, digitalization and intelligence of testing, improve the sharing of test data and shorten the time required for testing. This paper explores the prospects and feasibility of applying 5G technology to satellite testing, where intelligent testing on satellites can optimise spacecraft layout and will also reduce the hazards associated with mishandling during manual testing. Data from the all test process can also be recorded, making it easy to apply this data to other areas of space technology and management. In the future, it will also be of great research value for the realization of multi-star parallel testing.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134518245","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 state of the pins on the transmission tower has an important impact on the safety of the transmission line. However, the size of the pin itself is small, the characteristics of different angles vary greatly, and the surrounding environment may be complicated. The traditional manual inspection method is no longer suitable for the increasingly large transmission network. In response to the above problems, this paper proposes a detection method for bolt bolts of transmission towers based on the attention mechanism. First, preprocess the input pin image, and then scan the image with a perturbation neural network that includes an attention mechanism to get the most likely area, and cut and save the area for further processing. This can reduce The useless information on the image increases the proportion of the pins on the image. After multiple rounds of focusing, a pin image with obvious characteristics can be obtained. Secondly, the deconvolutional perturbation neural network is used to establish the feature mapping relationship from the whole to the partial pin image. Thirdly, according to the closed-loop control principle, the distinguishability evaluation index is used to obtain the difference of the extracted features in the feature space. According to the evaluation results, the feature space is layered, the images with large feature deviations are separated and recombined into a new data set, which is then used as the training set of the next level model to retrain the new model, and then based on the evaluation The result of the decision determines whether to continue to layer the training set, realize the multi-level feature space separation and self-optimization adjustment mechanism of the recognition structure, and establish a multi-level model recognition system with stronger generalization ability.
{"title":"Research on Intelligent Cognition Method of Missing status of Pins Based on attention mechanism","authors":"Liu Weitao, Guan Huimin, Zhang Qian, Wu Gang, Tang Jian, Ding Meishuang","doi":"10.1109/IMCEC51613.2021.9482019","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482019","url":null,"abstract":"The state of the pins on the transmission tower has an important impact on the safety of the transmission line. However, the size of the pin itself is small, the characteristics of different angles vary greatly, and the surrounding environment may be complicated. The traditional manual inspection method is no longer suitable for the increasingly large transmission network. In response to the above problems, this paper proposes a detection method for bolt bolts of transmission towers based on the attention mechanism. First, preprocess the input pin image, and then scan the image with a perturbation neural network that includes an attention mechanism to get the most likely area, and cut and save the area for further processing. This can reduce The useless information on the image increases the proportion of the pins on the image. After multiple rounds of focusing, a pin image with obvious characteristics can be obtained. Secondly, the deconvolutional perturbation neural network is used to establish the feature mapping relationship from the whole to the partial pin image. Thirdly, according to the closed-loop control principle, the distinguishability evaluation index is used to obtain the difference of the extracted features in the feature space. According to the evaluation results, the feature space is layered, the images with large feature deviations are separated and recombined into a new data set, which is then used as the training set of the next level model to retrain the new model, and then based on the evaluation The result of the decision determines whether to continue to layer the training set, realize the multi-level feature space separation and self-optimization adjustment mechanism of the recognition structure, and establish a multi-level model recognition system with stronger generalization ability.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132599700","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482062
Xiaomeng Wu, Rongze Yuan
As distribution generation (DG) are more and more widely used in power system, the importance of their siting and sizing issues has been also become prominent. If the location and capacity of the DG are not properly selected, it may have a negative impact on the protection configuration, nodal voltage distribution and energy loss of the power distribution network. This paper is based on the background of distribution generation and a large number of plug-in electric vehicles (PEV) are connected to the grid, established a probability distribution model for DG and PEV, then determined the objective function considering cost and power quality, summarized the corresponding siting and sizing methods.
{"title":"Research on Location and Capacity of Distribution Generation Considering Plug-in Electric Vehicle","authors":"Xiaomeng Wu, Rongze Yuan","doi":"10.1109/IMCEC51613.2021.9482062","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482062","url":null,"abstract":"As distribution generation (DG) are more and more widely used in power system, the importance of their siting and sizing issues has been also become prominent. If the location and capacity of the DG are not properly selected, it may have a negative impact on the protection configuration, nodal voltage distribution and energy loss of the power distribution network. This paper is based on the background of distribution generation and a large number of plug-in electric vehicles (PEV) are connected to the grid, established a probability distribution model for DG and PEV, then determined the objective function considering cost and power quality, summarized the corresponding siting and sizing methods.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129385545","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 : 2021-06-18DOI: 10.1109/IMCEC51613.2021.9482160
Y. Dou, Fu-Zeng Wang, Xiuchun Wu, Wentao Meng
Public hospital managers and information departments need to comprehensively and timely analyze the content of performance indicators, and use the information evaluation system to realize the medical quality supervision, so that hospital managers and department heads can effectively trace the problems of performance evaluation results and process indicators, so as to realize the dynamic feedback of medical quality. In this paper, we use model thinking to explore and build a set of index evaluation system based on seven dimensions of function orientation, quality and safety, rational drug use, service efficiency, revenue and expenditure structure, cost control and personnel training. We use expert scoring method to evaluate the membership degree of different indicators, and use Matlab R2019b statistical processing and analysis, combined with the fuzzy comprehensive evaluation model to evaluate the overall performance evaluation level of our hospital, the final score is 84.5515, the quantitative results also intuitively show the shortcomings and advantages of our hospital, which provides a reference for our hospital to realize its own stable development, provide professional medical services for patients, and realize the good experience of patients.
{"title":"Research on Performance Evaluation of Three Level Public Hospitals Based on Fuzzy Comprehensive Evaluation Model","authors":"Y. Dou, Fu-Zeng Wang, Xiuchun Wu, Wentao Meng","doi":"10.1109/IMCEC51613.2021.9482160","DOIUrl":"https://doi.org/10.1109/IMCEC51613.2021.9482160","url":null,"abstract":"Public hospital managers and information departments need to comprehensively and timely analyze the content of performance indicators, and use the information evaluation system to realize the medical quality supervision, so that hospital managers and department heads can effectively trace the problems of performance evaluation results and process indicators, so as to realize the dynamic feedback of medical quality. In this paper, we use model thinking to explore and build a set of index evaluation system based on seven dimensions of function orientation, quality and safety, rational drug use, service efficiency, revenue and expenditure structure, cost control and personnel training. We use expert scoring method to evaluate the membership degree of different indicators, and use Matlab R2019b statistical processing and analysis, combined with the fuzzy comprehensive evaluation model to evaluate the overall performance evaluation level of our hospital, the final score is 84.5515, the quantitative results also intuitively show the shortcomings and advantages of our hospital, which provides a reference for our hospital to realize its own stable development, provide professional medical services for patients, and realize the good experience of patients.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131190219","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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