Journal of Mechanical Science and Technology

  • 제19권3호
  • /
  • Pages.743-755
  • /
  • 2005
  • /
  • 1738-494X(pISSN)
  • /
  • 1976-3824(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지

Development of a Rule-Based Inference Model for Human Sensibility Engineering System

  • Yang Sun-Mo (Six-Sigma Strategic Management Consulting Co., Ltd.) ;
  • Ahn Beumjun (Department of Industrial Information and Systems Engineering, Sangmyung University) ;
  • Seo Kwang-Kyu (Department of Industrial Information and Systems Engineering, Sangmyung University)
  • 발행 : 2005.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

키워드

참고문헌

  1. Han, S. B., Yang, S. M. and Lee, S. Y., 1996, 'Development of a Design Process of Human Sensibihty Ergonomics,' Journal of Eng. Sci and Tech. of Korea University, Vol. 32, pp. 113-119
  2. Hayashi, C., 1976, Method of Quantification, Tokyo, Japan, Toyokeizai Press
  3. Imamura, K., Nomura, J. and Nagamachi, M., 1994, 'Virtual Space Decision Support System Using Human Sensibihty Engineering,' Proceeding of Japan-U.S.A. Symposium on Flexible Automation, Vol. II, pp. 549-555
  4. Ishihara, S. and Nagamachi, M., 1995, 'An Automatic Builder for a Human Sensibility Engineering Expert System Using Self-Organizing Neural Network,' International Journal of lndustrial Ergonomics, Vol. 15, No. 1, pp. 13-24 https://doi.org/10.1016/0169-8141(94)15053-8
  5. John, L. Motloch, 1991, Introduction to Landscape Design, NewYork, USA, Van Nostrand Reinhold
  6. Kawaguchi, I., 1980, Introduction to Multivariate Analysis, Mori Press, Tyoko
  7. Matsubara, Y. and Nagamachi, M., 1997(a), 'Human sensibility Analysis Support System and Virtual HSES,' Human Sensibility Engineering-I, Tokyo, Japan, Kaibundo Press
  8. Matsubara, Y. and Nagamachi, M., 1997(b), 'Hybrid Human Sensibility Engineering System & Design Support,' International Journal of Industrial Ergonomics, Vol. 19, No. 1, pp. 81-92 https://doi.org/10.1016/S0169-8141(96)00005-4
  9. Nagamachi, M., 1986, 'Image Technology and its Application,' Japanese Journal of Ergonomics, Vol. 22, No. 6, pp. 316-324 https://doi.org/10.5100/jje.22.316
  10. Nagamachi, M., 1988, 'Image Technology,' Journal of Electronics Information and Communication Engineers, Vol. 71, No. 3, pp. 245-247
  11. Nagamachi, M., 1989, Human Sensibility Engineering, Kaibundo Press, Tokyo, Japan
  12. Nagamachi, M., 1992, 'Human Sensibility Engineering and its Technology,' Journal of Japan Industrial Management Association, Vol. 2, No. 2, pp. 97-98
  13. Nagamachi, M., 1995(a), 'Human Sensibility Engineering: A New Ergonomic Consumer- Oriented Technology for Product Development,' International Journal of Industrial Ergonomics, Vol. 15, No. 1, pp. 3-11 https://doi.org/10.1016/0169-8141(94)00052-5
  14. Nagamachi, M., 1995(b), A note on Human Sensibility Engineering, Japan Standardization Association Press
  15. Osgood, C. E., Suci, G. J. and Tannenbaum, P. H., 1957, The Measurement of Meaning, University of Illinois press, USA
  16. Park, J.- H. and Seo, K.- K, 2003, 'Approximate Life Cycle Assessment of Product Concepts Using Multiple Regression Analysis and Artificial Neural Networks,' KSME International Journal, Vol. 17, No. 12, pp. 1969-1976
  17. Shim, M. B. and Suh, M. W., 2002, 'Crack Identification Using Neuro-Fuzzy-Evolutionary Technique,' KSME International Journal, Vol. 16, No. 4, pp. 454-467
  18. Shimizu, Y. and Jindo, T,, 1995, 'A Fuzzy Logic Analysis Method for Evatuation Hhuman Sensitivities,' International Journal of lndustrial Ergonomics, Vol. 15, No. 1, pp. 39-47 https://doi.org/10.1016/0169-8141(95)91249-A
  19. Tanaka, Y.， Wakimoto, K., 1983, Methods of Multivariate Statistical Analysis, Gendai Sugaku, Toyko
  20. Tsuchiya, T., Nagamachi, M. et al., 1996, 'A Fuzzy Rule Induction Method Using Genetic Algorithm,' International Journal of Industrial Ergonomics, Vol. 15, No. 1, pp. 135-145 https://doi.org/10.1016/0169-8141(95)00076-3
  21. Yang, S. M. and Lee, S. Y., 1996, 'An Approach to a Human-Centered CIM Based on Production Information System,' International Journal of Human Factors in Manufacturing, Vol. 6, No. 4, pp. 349-353 https://doi.org/10.1002/(SICI)1522-7111(199622)6:4<349::AID-HFM3>3.0.CO;2-A