Correlation analysis between team communication characteristics and frequency of inappropriate communications

Kim, Ar Ryum; Lee, Seung Woo; Park, Jinkyun; Kang, Hyun Gook; Seong, Poong Hyun

doi:10.1016/j.anucene.2013.03.003

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…When implementing the emergency tasks on the full‐digital instrumentation (all the physical buttons and controls have been digitalized in displays) control systems without physical buttons and controls, control crew operators have to deal with dynamic alarm messages, changing equipment parameters, and complex operation tasks through close team coordination (Kim et al, 2013; She & Li, 2017; Wu et al, 2017). Higher team cohesion (means higher attractiveness for the individual members and more synergistic group interactions; LeDoux et al, 2012; Zohar & Tenne‐Gazit, 2008) may benefit the information exchange and workload management of control crews during emergencies.…”

Section: Hypothesesmentioning

confidence: 99%

Coordination‐behavior patterns of control crews in digital nuclear power plants during emergencies from a network perspective: An exploratory study

Zhang

Wang²,

Gao

et al. 2022

Hum Ftrs & Erg Mfg Svc

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This study aims to investigate the coordination‐behavior patterns of control crews in digital nuclear power plants (NPPs) during emergencies from a network perspective. We observed and coded 12 coordination processes (each from one crew) of handling simulated emergencies on a full‐scope dynamic simulator in Tianwan NPP of China. By calculating the proportion of coordination breakdowns and referring to the subjective evaluation of instructors, these control crews (all male) were classified into two performance levels (high and low). To compare the coordination‐behavior patterns between high‐ and low‐performing crews, we conducted social network analysis based on the number and direction of coordination behaviors. By examining intracrew relations in team coordination, this study indicated that high‐performing crews exhibited higher cohesion and more balanced behavioral patterns in team coordination than low‐performing ones. By investigating coordination‐behavior patterns from the hierarchical structure of relations in the team, this study revealed that high‐performing crews exhibited higher degree of team autonomy and self‐management among junior operators, as well as reactor operator (RO)‐centered pattern, whereas the low‐performing crews exhibited senior reactor operator‐centered pattern. For researchers, the results advance the understanding of the team coordination mechanism in NPP control rooms during emergencies and enrich team coordination theory in process control industries from the network perspective. In practice, this study suggested that the team coordination training of NPP control crews might be centered around junior operators (especially RO), with focuses on facilitating information sharing and mutual assistance between junior operators and enhancing the awareness of active cooperation of crew members. The results of the study provide nuclear instructors with practical reference to team coordination training and processes optimization.

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Section: Hypothesesmentioning

confidence: 99%

Coordination‐behavior patterns of control crews in digital nuclear power plants during emergencies from a network perspective: An exploratory study

Zhang

Wang²,

Gao

et al. 2022

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

show abstract

“…It can be utilized to analyze statistical interrelations between two probability variables in various fields of engineering. For example, we can study correlation of wind and hydro with electricity demand and prices [16] and develop a method to evaluate the characteristics of team communications based on social network analysis [17]. To measure the correlation and similarity between two probability variables, covariance can be utilized as follows:…”

Section: Sequential Correlation Analysismentioning

confidence: 99%

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

2018

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Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62-2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing.

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An evaluation method of team communication based on a task flow analysis

2015

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Correlation analysis between team communication characteristics and frequency of inappropriate communications

Cited by 8 publications

References 16 publications

Coordination‐behavior patterns of control crews in digital nuclear power plants during emergencies from a network perspective: An exploratory study

Coordination‐behavior patterns of control crews in digital nuclear power plants during emergencies from a network perspective: An exploratory study

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

An evaluation method of team communication based on a task flow analysis

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