• 제목/요약/키워드: nuclear

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IMAGE OF HSV1-TK GENE EXPRESSION WITH $^{123}IVDU$

  • Kim, S.Y.;Woo, K.S.;Chung, W.S.;Choi, T.H.;Lee, T.S.;Chung, H.K.;Lee, M.J.;Jung, J.H.;Cheon, G.J.;Kim, S.E.;Lim, S.M.;Choi, C.W.
    • 한국원자력학회:학술대회논문집
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    • 한국원자력학회 2005년도 춘계학술발표회
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    • pp.1018-1019
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    • 2005

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Optimization of preventive maintenance of nuclear safety-class DCS based on reliability modeling

  • Peng, Hao;Wang, Yuanbing;Zhang, Xu;Hu, Qingren;Xu, Biao
    • Nuclear Engineering and Technology
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    • 제54권10호
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    • pp.3595-3603
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    • 2022

  • Nuclear safety-class DCS is used for nuclear reactor protection function, which is one of the key facilities to ensure nuclear power plant safety, the maintenance for DCS to keep system in a high reliability is significant. In this paper, Nuclear safety-class DCS system developed by the Nuclear Power Institute of China is investigated, the model of reliability estimation considering nuclear power plant emergency trip control process is carried out using Markov transfer process. According to the System-Subgroup-Module hierarchical iteration calculation, the evolution curve of failure probability is established, and the preventive maintenance optimization strategy is constructed combining reliability numerical calculation and periodic overhaul interval of nuclear power plant, which could provide a quantitative basis for the maintenance decision of DCS system.

  • https://doi.org/10.1016/j.net.2022.05.011 인용 PDF KSCI

2009-2022 Thailand public perception analysis of nuclear energy on social media using deep transfer learning technique

  • Wasin Vechgama;Watcha Sasawattakul;Kampanart Silva
    • Nuclear Engineering and Technology
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    • 제55권6호
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    • pp.2026-2033
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    • 2023

  • Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

  • https://doi.org/10.1016/j.net.2023.03.036 인용 PDF