지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.8
- 수록면
- 15 - 30 (16page)
- DOI
- 10.37272/JIECR.2022.08.22.4.15
이용수
초록· 키워드
오류제보하기
Data labeling is the task of supplementing data corresponding to the correct answer in training data for AI(Artificial Intelligence). Data labeling helps improve the performance of AI learning models, but is often done manually. Automating data labeling tasks so that high-quality learning data can be efficiently produced can serve as a foundation for the development of artificial intelligence.
In this paper, we proposed a method using topic modeling to automate data labeling of error data produced in the smart factory control system. Error text files were created by extracting major error-related items and error messages from the database accumulated in the smart factory operating environment. Before the topic modeling, frequently appearing words were extracted through basic analysis of error text, and main causes of errors were roughly identified by visualizing them with bar graphs and word clouds. After that, major topics related to errors were extracted by applying topic modeling to the error text. Based on the key words included in the topics, meanings were given to each topic, error types were derived, and error type codes were also assigned. Coherence and Perplexity were calculated to derive the optimal number of topics, and 4-5 topics were found to be optimal. This paper is meaningful in that it confirmed the possibility of automating data labeling in big data including text data.
In this paper, we proposed a method using topic modeling to automate data labeling of error data produced in the smart factory control system. Error text files were created by extracting major error-related items and error messages from the database accumulated in the smart factory operating environment. Before the topic modeling, frequently appearing words were extracted through basic analysis of error text, and main causes of errors were roughly identified by visualizing them with bar graphs and word clouds. After that, major topics related to errors were extracted by applying topic modeling to the error text. Based on the key words included in the topics, meanings were given to each topic, error types were derived, and error type codes were also assigned. Coherence and Perplexity were calculated to derive the optimal number of topics, and 4-5 topics were found to be optimal. This paper is meaningful in that it confirmed the possibility of automating data labeling in big data including text data.
목차
Abstract
Ⅰ. 서론
Ⅱ. 이론적 배경
Ⅲ. 연구 설계
Ⅳ. 데이터분석
Ⅴ. 결론
참고문헌
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