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Jhih-Tsong Lin

Researcher at National Tsing Hua University

Publications -  8
Citations -  155

Jhih-Tsong Lin is an academic researcher from National Tsing Hua University. The author has contributed to research in topics: Workload & Situation awareness. The author has an hindex of 5, co-authored 8 publications receiving 145 citations.

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Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room

TL;DR: This study applied the concepts of the Shewhart control chart to design a pre-alarm system for the nuclear power plant control room, and indicated that participants had lower mental workload, but equal SA, when monitoring the system with either type of pre- alarm designs.
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Preventing human errors in aviation maintenance using an on-line maintenance assistance platform

TL;DR: In this article, an on-line maintenance assistance platform (on-line MAP) for technicians to perform maintenance tasks was developed, where the risk of human error was defined in each task procedure to prevent human errors and improve satisfaction with the job.
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A real-time warning model for teamwork performance and system safety in nuclear power plants.

TL;DR: A predictive teamwork performance model applying the GMDH algorithm and the RTWM with a fuzzy inference system was developed and can efficiently predict teamwork performance to maintain appropriate mental workload as well as ensure system safety.
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Evaluation and prediction of on-line maintenance workload in nuclear power plants

TL;DR: In this article, a mental workload model based on the neural network technique was established to predict the mental workload of maintenance engineers in maintaining digital systems in nuclear power plants (NPPs).
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Predicting visual fatigue in integrated circuit packaging plants

TL;DR: In this paper, the authors evaluate the relationship between the operators' visual fatigue and actual defect data gathered from May to June 2008 and find a positive relationship between visual fatigue, and the number of defects, and develop a visual fatigue prediction model using as significant predictors working time, rest time, number of inspections, and task difficulty.