인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
In this paper, in order to optimize the biological water treatment process, we review three representative methods among XAI's post-hoc explainability techniques. Among them, LIME and AEA methods are applied to the water treatment biological process to find an optimization method. presented. XAI's post-hoc explainability technique is applied to solve the black box problem of not knowing what is attributable to the water treatment artificial intelligence model, which is commonly used in water treatment process optimization, even if it produces good results. We analyzed which control variables were responsible for the improvement in the quality of treated water. As a result of the analysis, it was confirmed that the LIME method had a greater influence on the quality of treated water than the AEA method. In addition, it was found that this method contributes to solving the black box problem and improving the quality of treated water. In the case of the LIME method applied to the water treatment biological process in this paper, although it is not common, it was possible to analyze the characteristics of output variables even for input variables other than control variables by observing the results graphically. In the future, it is believed that by systematizing such graph analysis into an algorithm, it will be possible to propose a more effective LIME method.
본문·목차
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