Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator

The decrease of situation awareness (SA) is one of reasons leading to human factor accidents in nuclear power plants. The main purpose of this paper is to the evaluation and prediction the operators’ SA in digital main control room. Firstly, this paper used both the entropy weight method...

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Autores principales:	Shengyuan Yan, Kai Yao, Cong Chi Tran
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Situation awareness nuclear power plants influencing factor artificial neural network prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/99a3affd900946bda2f6f662834bf4dd
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spelling	oai:doaj.org-article:99a3affd900946bda2f6f662834bf4dd2021-11-19T00:06:00ZUsing Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator2169-353610.1109/ACCESS.2021.3055345https://doaj.org/article/99a3affd900946bda2f6f662834bf4dd2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9337894/https://doaj.org/toc/2169-3536The decrease of situation awareness (SA) is one of reasons leading to human factor accidents in nuclear power plants. The main purpose of this paper is to the evaluation and prediction the operators’ SA in digital main control room. Firstly, this paper used both the entropy weight method and variation coefficient method to determine the relevant influencing factors. Secondly, principal component analysis (PCA) was used to concentrate the input variables into common component. Then, an artificial neural network (ANN) model was conducted based on influencing factors and SA data. The result showed that there are identified fifteen factors that have a greater impact on SA reliability, accounting for 65.2% of the weight of all factors. The PCA result showed that the contribution rate of eight common factors reached 80.6% for the total variance of variables and the cumulative variance. Therefore, these variables were explained by eight common components. The 8-14-1 network structure was can obtain the minimum of the MSE (0.0058) and the maximum of R<sup>2</sup> (0.9814). The predicted data can obtain the minimal MSE value (0.0035) and maximum R<sup>2</sup> (0.9886) when the ‘Relu’ function was used as the activation function of both the hidden layer and output layer. The average prediction accuracy of the constructed ANN model is more than exceeded 92% for the test data. This result indicated that the developed ANN model can accurately evaluate operator’s SA.Shengyuan YanKai YaoCong Chi TranIEEEarticleSituation awarenessnuclear power plantsinfluencing factorartificial neural networkprediction accuracyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 20143-20155 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Situation awareness nuclear power plants influencing factor artificial neural network prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Situation awareness nuclear power plants influencing factor artificial neural network prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shengyuan Yan Kai Yao Cong Chi Tran Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
description	The decrease of situation awareness (SA) is one of reasons leading to human factor accidents in nuclear power plants. The main purpose of this paper is to the evaluation and prediction the operators’ SA in digital main control room. Firstly, this paper used both the entropy weight method and variation coefficient method to determine the relevant influencing factors. Secondly, principal component analysis (PCA) was used to concentrate the input variables into common component. Then, an artificial neural network (ANN) model was conducted based on influencing factors and SA data. The result showed that there are identified fifteen factors that have a greater impact on SA reliability, accounting for 65.2% of the weight of all factors. The PCA result showed that the contribution rate of eight common factors reached 80.6% for the total variance of variables and the cumulative variance. Therefore, these variables were explained by eight common components. The 8-14-1 network structure was can obtain the minimum of the MSE (0.0058) and the maximum of R<sup>2</sup> (0.9814). The predicted data can obtain the minimal MSE value (0.0035) and maximum R<sup>2</sup> (0.9886) when the ‘Relu’ function was used as the activation function of both the hidden layer and output layer. The average prediction accuracy of the constructed ANN model is more than exceeded 92% for the test data. This result indicated that the developed ANN model can accurately evaluate operator’s SA.
format	article
author	Shengyuan Yan Kai Yao Cong Chi Tran
author_facet	Shengyuan Yan Kai Yao Cong Chi Tran
author_sort	Shengyuan Yan
title	Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
title_short	Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
title_full	Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
title_fullStr	Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
title_full_unstemmed	Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator
title_sort	using artificial neural network for predicting and evaluating situation awareness of operator
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/99a3affd900946bda2f6f662834bf4dd
work_keys_str_mv	AT shengyuanyan usingartificialneuralnetworkforpredictingandevaluatingsituationawarenessofoperator AT kaiyao usingartificialneuralnetworkforpredictingandevaluatingsituationawarenessofoperator AT congchitran usingartificialneuralnetworkforpredictingandevaluatingsituationawarenessofoperator
_version_	1718420681874472960

Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator

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