[논문]신경회로망 기반 우리나라 산업안전시스템의 모델링

최기흥

doi:10.14346/jkosos.2023.38.1.1

[국내논문] 신경회로망 기반 우리나라 산업안전시스템의 모델링
Neural Network-based Modeling of Industrial Safety System in Korea 원문보기

한국안전학회지 = Journal of the Korean Society of Safety, v.38 no.1, 2023년, pp.1 - 8

Abstract ▼ AI-Helper

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.

Keyword

표/그림 (14)

그림 Fig. 1. Selection of input variables where classes are clearly discriminated based on the output variables.
표 Table 1. Classes and normalized values of input variable 1 (Business Type(Large)) used in neural network-based modeling of industrial safety system in Korea
표 Table 2. Classes and normalized values of input variable 1 (Company Size (Number of Workers)) used in neural network- based modeling of industrial safety system in Korea
표 Table 3. Classes and normalized values of input variable 5 (Age of Workers) used in neural network-based modeling of industrial safety system in Korea
표 Table 4. Classes and normalized values of input variable 1 (Work Duration) used in neural network-based modeling of industrial safety system in Korea
그림 Fig. 2. Structure of neural network used in modeling of industrial safety system in Korea.
표 Table 5. Classes and normalized values of input variable 1 (Work Type) used in neural network-based modeling of industrial safety system in Korea.
그림 Fig. 3. Probability distribution of output variable 1 (frequency of accident) for classes of 5 input variables used in neural network-based modeling of industrial safety system in Korea.
그림 Fig. 4. Probability distribution of output variable 2 (severity of accident) for classes of 5 input variables used in neural network-based modeling of industrial safety system in Korea.
표 Table 6. Probability distribution of output variable 1 (frequency of accident), output variable 2 (severity of accident) and cross entropy between output variable 1 and 2 for classes of 5 input variables used in neural network-based modeling of industrial safety system in Korea
그림 Fig. 5. Comparison of the actual and the predicted frequency and severity of 3-level 1-2-3 input neural network model (Business type-company size-work type model).
그림 Fig. 6. Comparison of the actual and the predicted frequency and severity of 3-level 1-2-4 input neural network model (Business type-company size-work duration model).
그림 Fig. 7. Comparison of the normalized frequency and the severity predicted by the 3-level 1-2-3 input neural network model (Business type-company size-work type model).
그림 Fig. 8. Comparison of the normalized frequency and the severity predicted by the 3-level 1-2-4 input neural network model (Business type-company size-work duration model).

참고문헌 (12)

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G. H. Choi, "Application of Information -theoretic Measure？(Entropy) to Safety Assessment in Manufacturing？Processes", Int. J. Safety, Vol. 4, No. 1, pp. 8-13, 2005.
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G. H. Choi, "Cause Analysis of Accidents at Workplaces in？Korea", J. Korean Soc. Saf. (Submitted for publication)

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