충전데이터를 이용한 이상감지 제어시스템
In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an ana...
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| Published in | 한국정보통신학회논문지 Vol. 26; no. 2; pp. 313 - 316 |
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| Main Author | |
| Format | Journal Article |
| Language | Korean |
| Published |
한국정보통신학회
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2234-4772 2288-4165 |
| DOI | 10.6109/jkiice.2022.26.2.313 |
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| Abstract | In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data. |
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| AbstractList | In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data. KCI Citation Count: 0 In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data. |
| Author | Sang-Ho Moon(문상호) |
| Author_xml | – sequence: 1 fullname: Moon, Sang-Ho |
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| DocumentTitleAlternate | Abnormality Detection Control System using Charging Data |
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| Keywords | Electronic vehicle charging Classification model Charger Clustering Anomaly detection |
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| Title | 충전데이터를 이용한 이상감지 제어시스템 |
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