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Anticipating Human Errors from Periodic Big Survey Data in Nuclear Power Plants

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dc.contributor.authorLee, Hyun-Chul-
dc.contributor.authorJang, Tong-Il-
dc.contributor.authorMoon, Kwangsu-
dc.date.accessioned2022-04-14T06:40:09Z-
dc.date.available2022-04-14T06:40:09Z-
dc.date.issued2017-12-
dc.identifier.issn2639-1589-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56433-
dc.description.abstractHuman errors are the critical factor for nuclear safety in nuclear power plants (NPPs). A human error anticipation method for nuclear power plant is proposed in this presentation. The main idea of the method is to catch a human error omen from survey data, so-called work condition survey (WCS), that the NPP collects periodically. The WCS data is so various and much because it is collected through questionnaires. A database is designed to process the big WCS data efficiently. A method for human error anticipation based on statistical methods is proposed and verified using the database containing WCS data collected for last 3 years. It is expected that the method contribute to monitor human errors likelihood and to plan human error prevention strategies.-
dc.format.extent2-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleAnticipating Human Errors from Periodic Big Survey Data in Nuclear Power Plants-
dc.typeArticle-
dc.identifier.doi10.1109/BigData.2017.8258539-
dc.identifier.bibliographicCitation2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), v.2018-January, pp 4777 - 4778-
dc.description.isOpenAccessN-
dc.identifier.wosid000428073704121-
dc.identifier.scopusid2-s2.0-85047739794-
dc.citation.endPage4778-
dc.citation.startPage4777-
dc.citation.title2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)-
dc.citation.volume2018-January-
dc.type.docTypeProceedings Paper-
dc.subject.keywordAuthorcomponent-
dc.subject.keywordAuthorhuman errors-
dc.subject.keywordAuthoranticipation-
dc.subject.keywordAuthorbig survey data-
dc.subject.keywordAuthorqualitative data-
dc.subject.keywordAuthornucleaer power plant-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.description.journalRegisteredClassscopus-
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