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SCIESCOPUS
Application of Deep Neural Network to an Accelerated Prediction of a Severe Accident in Nuclear Power PlantsGo to Linkopen access

Joo, Semin; Lee, Yeonha; Song, Seok Ho; Song, Kyusang; Seo, Mi Ro; Kim, Sung Joong; Lee, Jeong Ik

ArticleIssue Date2025CitationInternational Journal of Energy Research, v.2025, no.1, pp 1 - 18PublisherJohn Wiley & Sons Inc.
SCIESCOPUS
Application of reinforcement learning to deduce nuclear power plant severe accident scenarioGo to Link

Song, Seok Ho; Lee, Yeonha; Bae, Jun Yong; Song, Kyu Sang; Seo, Mi Ro; Kim, SungJoong; Lee, Jeong Ik

ArticleIssue Date2024CitationAnnals of Nuclear Energy, v.205, pp 1 - 13PublisherElsevier Ltd.
SCIESCOPUS
Improving Accuracy of a Machine Learning Approach for Forecasting Severe AccidentsGo to Link

Joo, Semin; Lee, Yeonha; Song, Seok Ho; Song, Kyusang; Seo, Mi Ro; Kim, Sung Joong; Lee, Jeong Ik

ArticleIssue Date2026CitationNUCLEAR TECHNOLOGY, v.212, no.6, pp 1497 - 1512PublisherTAYLOR & FRANCIS INC
SCIESCOPUS
Leveraging explainable AI for reliable prediction of nuclear power plant severe accident progressionGo to Link

Joo, Semin; Lee, Yeonha; Song, Seok Ho; Song, Kyusang; Seo, Mi Ro; Kim, Sung Joong; Lee, Jeong Ik

ArticleIssue Date2025CitationReliability Engineering and System Safety, v.264, pp 1 - 19PublisherElsevier BV
SCIESCOPUS
Surrogate model for predicting severe accident progression in nuclear power plant using deep learning methods and Rolling-Window forecastGo to Link

Lee, Yeonha; Song, Seok Ho; Bae, Joon Young; Song, Kyusang; Seo, Mi Ro; Kim, Sung Joong; Lee, Jeong Ik

ArticleIssue Date2024CitationAnnals of Nuclear Energy, v.208, pp 1 - 11PublisherElsevier Ltd.
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