• International Journal of Safety
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• 제4권2호
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• pp.1-5
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• 2005

Design of manufacturing process, in general, facilitates the creation of new process that may potentially harm the workers. Design of safety-guaranteed manufacturing process is, therefore, very important since it determines the ultimate outcomes of manufacturing activities involving safety of workers. This study discusses application of discrimination information (cross entropy) to safety assessment of manufacturing processes. The idea is based on the general principles of design and their applications. An example of Cartesian robotic movement is given.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.298-299
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• 2018

본 연구에서는 의사결정자의 사전정보가 필요하지 않은 DEA 모형들을 대상으로 변별력 평가를 실시하였다. 변별력 평가를 위한 DEA모형으로 Entropy 모형, Bootstrap 모형, Benevolent Cross Efficiency 모형, Aggressive Cross Efficiency 모형, Game Cross Efficiency 모형을 선정하였다. 변별력 평가척도인 변동계수(coefficient of variation)와 중요도(degree of importance) 평가기준을 이용하여 5개 DEA 모형의 변별력을 평가하였다. 평가결과에 따르면 변별력 순위는 2개 평가 지표 모두에서 Entropy 모형, Aggressive CE 모형, Benevolent CE 모형, Game CE 모형, Bootstrap 모형 순으로 평가되었다.

• 한국안전학회지
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• 제38권1호
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• pp.1-8
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• 2023

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.