Kim Jung Hwan, Kim Chul Min, Jung Eun-Soo, Yim Man-Sung
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Technology Research, Samsung SDS, Seoul, South Korea.
Front Comput Neurosci. 2020 Dec 21;14:596531. doi: 10.3389/fncom.2020.596531. eCollection 2020.
In the main control room (MCR) of a nuclear power plant (NPP), the quality of an operator's performance can depend on their level of attention to the task. Insufficient operator attention accounted for more than 26% of the total causes of human errors and is the highest category for errors. It is therefore necessary to check whether operators are sufficiently attentive either as supervisors or peers during reactor operation. Recently, digital control technologies have been introduced to the operating environment of an NPP MCR. These upgrades are expected to enhance plant and operator performance. At the same time, because personal computers are used in the advanced MCR, the operators perform more cognitive works than physical work. However, operators may not consciously check fellow operators' attention in this environment indicating potentially higher importance of the role of operator attention. Therefore, remote measurement of an operator's attention in real time would be a useful tool, providing feedback to supervisors. The objective of this study is to investigate the development of quantitative indicators that can identify an operator's attention, to diagnose or detect a lack of operator attention thus preventing potential human errors in advanced MCRs. To establish a robust baseline of operator attention, this study used two of the widely used biosignals: electroencephalography (EEG) and eye movement. We designed an experiment to collect EEG and eye movements of the subjects who were monitoring and diagnosing nuclear operator safety-relevant tasks. There was a statistically significant difference between biosignals with and without appropriate attention. Furthermore, an average classification accuracy of about 90% was obtained by the k-nearest neighbors and support vector machine classifiers with a few EEG and eye movements features. Potential applications of EEG and eye movement measures in monitoring and diagnosis tasks in an NPP MCR are also discussed.
在核电站（NPP）的主控室（MCR）中，操作员的绩效质量可能取决于他们对任务的关注程度。操作员注意力不集中占人为错误总原因的26%以上，是错误类别中占比最高的。因此，有必要在反应堆运行期间，作为监督者或同行检查操作员是否足够专注。最近，数字控制技术已引入到核电站主控室的运行环境中。这些升级有望提高电厂和操作员的绩效。同时，由于先进的主控室使用了个人电脑，操作员进行的认知工作比体力工作更多。然而，在这种环境下，操作员可能不会自觉检查其他操作员的注意力，这表明操作员注意力的作用可能更为重要。因此，实时远程测量操作员的注意力将是一个有用的工具，可为监督者提供反馈。本研究的目的是调查能够识别操作员注意力的定量指标的发展情况，以诊断或检测操作员注意力的缺乏，从而防止先进主控室中潜在的人为错误。为了建立操作员注意力的稳健基线，本研究使用了两种广泛使用的生物信号：脑电图（EEG）和眼动。我们设计了一个实验，收集正在监测和诊断与核操作员安全相关任务的受试者的脑电图和眼动数据。有适当注意力和没有适当注意力时的生物信号之间存在统计学上的显著差异。此外，通过使用一些脑电图和眼动特征的k近邻和支持向量机分类器，获得了约90%的平均分类准确率。还讨论了脑电图和眼动测量在核电站主控室监测和诊断任务中的潜在应用。
