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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 1,080 - 1,083 (4page)
이용수
초록· 키워드
오류제보하기
As artificial intelligence technique is generalized widely used in industry area. so, there are attempts to anomaly detect by using deep learning in small manufacturing industries. However, it is difficult for small manufacturing industries to have an artificial intelligence infrastructure. The nation support data set of open small manufacturing industries for solve these problems and help. This paper proposes an anomaly detection model for time series data using this data set. The propose LSTM-SVDD anomaly detection model is that combines the LSTM model widely used in time series data with the SVDD model widely used in anomaly detection. The propose model is that learns the range of normal data and detects data out of this range as abnormal data. It is confirmed that the data distribution of the test data not used for learning predicted similarly with prediction results. A performance indicator ROC is also high at 96.31. the proposed automatic anomaly classification model is expected that can be used in small manufacturing industries field that are limited in the construction of artificial intelligence infrastructure.
목차
Abstract
1. INTRODUCTION
2. RELATED RESEARCH
3. DATA SET
4. EXPERIMENT & RESULT
5. CONCLUTION
REFERENCES
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