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논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제52권 제5호
발행연도
2020.1
수록면
984 - 994 (11page)

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It is reported that about 20% of accidents at nuclear power plants in Korea and abroad are caused byhuman error. One of the main factors contributing to human error is fatigue, so it is necessary to preventhuman errors that may occur when the task is performed in an improper state by grasping the status ofthe operator in advance. In this study, we propose a method of evaluating operator's fitness-for-duty(FFD) using various parameters including eye movement data, subjective fatigue ratings, and operator'sperformance. Parameters for evaluating FFD were selected through a literature survey. We performedexperiments that test subjects who felt various levels of fatigue monitor information of indicatorsand diagnose a system malfunction. In order to find meaningful characteristics in measured data consistingof various parameters, hierarchical clustering analysis, an unsupervised machine-learning technique,is used. The characteristics of each cluster were analyzed; fitness-for-duty of each cluster wasevaluated. The appropriateness of the number of clusters obtained through clustering analysis wasevaluated using both the Elbow and Silhouette methods. Finally, it was statistically shown that thesuggested methodology for evaluating FFD does not generate additional fatigue in subjects. Relevance to industry: The methodology for evaluating an operator's fitness for duty in advance is proposed,and it can prevent human errors that might be caused by inappropriate condition in nuclearindustries.

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