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    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 期刊論文 >  A real-time warning model for teamwork performance and system safety in nuclear power plants

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46939

    Title: A real-time warning model for teamwork performance and system safety in nuclear power plants
    Authors: Hwang, Sheue-Ling;Liang, Guo-Feng;Lin, Jhih-Tsong;Yau, Yi-Jan;Yenn, Tzu-Chung;Hsu, Chong-Cheng;Chuang, Chang-Fu
    教師: 黃雪玲
    Date: 2009
    Publisher: Elsevier
    Relation: Safety Science, Volume 47, Issue 3, March 2009, Pages 425-435
    Keywords: Real-time warning model (RTWM)
    Mental Workload
    Fuzzy logic
    Abstract: In order to increase system safety and team performance, this study aimed to develop a real-time warning model (RTWM) by assessing team response time, error rates, and mental workload. Toward this goal, the group method of data handling (GMDH) algorithm was applied to physiological indices to predict team performance. Then fuzzy logic, fuzzy inference and linguistic variable sets representing the Team Performance and Safety Index were applied to construct the RTWM. To model the RTWM, experiments were conducted on computer-supported cooperative work (CSCW) in the personal computer transient analyzer (PCTRAN) simulator. The simulator and teamwork are designed to simulate the real tasks of the control room of the Fourth Nuclear Power Plant (FNPP) in Taiwan. In addition, important physiological parameters, the NASA-TLX questionnaire, team response time, and team error rates were collected from 39 participants. The results revealed that there was a significant positive correlation between the error rates of teamwork and the interval of event arrival time. This indicated that a pre-alarm device is necessary because vigilance decreased with time. Moreover, a predictive teamwork performance model applying the GMDH algorithm and the RTWM with a fuzzy inference system was developed in this study. The proposed model can efficiently predict teamwork performance to maintain appropriate mental workload as well as ensure system safety.
    URI: http://www.elsevier.com/
    Appears in Collections:[工業工程與工程管理學系] 期刊論文

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