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Logical Consistency in Risk Assessment using the Korean Fuzzy Linguistic Variables

한국어 퍼지 언어변수를 이용한 리스크 평가의 논리적 일관성

  • Lim, Hyeon-Kyo (Department of Safety Engineering, Chungbuk National University) ;
  • Byun, Sanghun (Department of Safety Engineering, Chungbuk National University)
  • 임현교 (충북대학교 안전공학과) ;
  • 변상훈 (충북대학교 안전공학과)
  • Received : 2016.02.29
  • Accepted : 2016.08.12
  • Published : 2016.08.31

Abstract

Usually, a risk can be expressed as a product of likelihood and consequence of a hazard factor. Therefore, conventional risk assessment is carried out by frequency analysis and severity analysis, in turns. However, it is well known that intuitive thinking is another excellent way of thinking of human beings. This study aimed to confirm whether there exist any difference in risk assessment results derived by two different procedures - intuitive and analytical. Thus, the present study showed 10 different illustrations to 30 undergraduate students. Their responses were organized as fuzzy membership functions, and summarized as risk assessments, and compared. The results were also verified with the help of statistical hypothesis testing, which showed no significant difference. On the contrary, however, similarity measure used in fuzzy set theory was not credible as anticipated. Many cases failed to satisfy statistical hypothesis even with similarity measure higher than 0.60 so that only a trend could be accepted. In addition, a subject showed a somewhat consistent logical discrepancy in his response, which implied the necessity of sincere analysis in fuzzy formulations.

Acknowledgement

Supported by : 충북대학교

References

  1. MIL-STD-882, System Safety Program Require- ments, U.S. Department of Defense, 1969.
  2. S.F. Davis and W. Bukist, 21st Century Psychology - A Reference Handbook, Sage Publications, 2008.
  3. MIL-STD-882E, System Safety Program Require- ments, U.S. Department of Defense, 2012.
  4. H.K. Lim, S.H. Byun, H.J. Kim, "Risk Assessment using Fuzzy Linguistic Variables in Korean", Journal of the Korean Society of Safety, Vol.30, No.4, pp.151-158, 2015. https://doi.org/10.14346/JKOSOS.2015.30.4.151
  5. S.H.Kim, Illustrations for Hazard Anticipation Training, Korea Occupational Safety and Health Agency, 2007.
  6. L.A. Zadeh, "Fuzzy Sets", Information and Control, 8, pp.338-353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X
  7. Y. Deng, X. Su, W. Jiang, J. Xu and P. Xu, "Risk Analysis Method : A Fuzzy Approach", Proceedings of the 3rd International Symposium on Electronic Commerce and Security Workshops, pp.146-150, 2010.
  8. S.H. Chen and C.H. Hsieh, "Graded Mean Integration Representation of Generalized Fuzzy Number", Proceeding of 1998 Sixth Conference on Fuzzy Theory and Its Application, Chinese Fuzzy Systems Association, pp.1-6, 1998.
  9. F.J. Massey, Jr., "The Kolmogorov-Smirnov Test for Goodness of Fit", Journal of the American Statistical Association, Vol.46, No.253, pp.68-78, 2010. https://doi.org/10.1080/01621459.1951.10500769
  10. Nagi, E. W. T., Wat, F. K. T., "Fuzzy Decision Support System for Risk Analysis in e-commerce Development", Decision Support Systems, 40(2), pp.235-255, 2005. https://doi.org/10.1016/j.dss.2003.12.002