Deep learning based prediction system to minimize the effect of thermal effluent without domain knowledge배출 냉각수에 의한 영향 최소화/예방을 위한 딥러닝 기반 예측 시스템

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dc.contributor.advisorShin, Insik-
dc.contributor.advisor신인식-
dc.contributor.authorAn, Jaeyeong-
dc.date.accessioned2021-05-13T19:38:08Z-
dc.date.available2021-05-13T19:38:08Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925151&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284990-
dc.description학위논문(석사) - 한국과학기술원 : 전산학부, 2020.8,[iii, 16 p. :]-
dc.description.abstractCoal-fired power plants, the most common power plants, take more than half of the total power generation in Korea. However, it emits not only carbon dioxide which causes air pollution but also emits thermal effluents which degrades the water quality. In this research, our main goal is to limit the effect of thermal effluents (T5) to the sea (T1) by operating extra Cooling Water Pump (CWP). We can sim-ply formalize the problem as ”T5-T1 - 7”. It requires domain specific experts in traditional approach,however, we propose the prediction system that does not require domain specific experts. Our prediction system achieved 0.84 degree of RMSE with real plants data and we found rooms for optimization and improvements. The main contribution of this work is that we have shown the possibility of AI techniques, which could exploit the complex environment like marine environments, without domain specific experts.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectsmart factory▼amachine learning▼aartificial inteligence▼athermal effluent▼amarine ecosystem-
dc.subject스마트 팩토리▼a머신러닝▼a인공지능▼a배출냉각수▼a해양 생태계-
dc.titleDeep learning based prediction system to minimize the effect of thermal effluent without domain knowledge-
dc.title.alternative배출 냉각수에 의한 영향 최소화/예방을 위한 딥러닝 기반 예측 시스템-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전산학부,-
dc.contributor.alternativeauthor안재영-
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