Neural Network-based Modeling of Industrial Safety System in Korea 


Vol. 38,  No. 1, pp. 1-8, Feb.  2023
10.14346/JKOSOS.2023.38.1.1


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  Abstract

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

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  Cite this article

[IEEE Style]

최기흥, "Neural Network-based Modeling of Industrial Safety System in Korea," Journal of the Korean Society of Safety, vol. 38, no. 1, pp. 1-8, 2023. DOI: 10.14346/JKOSOS.2023.38.1.1.

[ACM Style]

최기흥. 2023. Neural Network-based Modeling of Industrial Safety System in Korea. Journal of the Korean Society of Safety, 38, 1, (2023), 1-8. DOI: 10.14346/JKOSOS.2023.38.1.1.

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