본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Journal of mechanical science and technology v.24 no.6, 2010년, pp.1211 - 1218   SCIE 피인용횟수: 1
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Choosing a suitable sample size in descriptive sampling

Lee, Yong-Kyun    (Department of Mathematics, Korea Air-Force Academy   ); Choi, Dong-Hoon    (School of Mechanical Engineering, Hanyang University   ); Cha, Kyung-Joon    (Department of Mathematics and Research Institute for Natural Sciences, Hanyang University  );
  • 초록

    Descriptive sampling (DS) is an alternative to crude Monte Carlo sampling (CMCS) in finding solutions to structural reliability problems. It is known to be an effective sampling method in approximating the distribution of a random variable because it uses the deterministic selection of sample values and their random permutation. However, because this method is difficult to apply to complex simulations, the sample size is occasionally determined without thorough consideration. Input sample variability may cause the sample size to change between runs, leading to poor simulation results. This paper proposes a numerical method for choosing a suitable sample size for use in DS. Using this method, one can estimate a more accurate probability of failure in a reliability problem while running a minimal number of simulations. The method is then applied to several examples and compared with CMCS and conventional DS to validate its usefulness and efficiency.


  • 주제어

    Crude Monte Carlo sampling .   Descriptive sampling .   Reliability .   Sample size.  

  • 참고문헌 (30)

    1. D. H. Evans, An application of numerical integration techniques to statistical tolerancing, Technometrics, 9 (3) (1967) 441-456. 
    2. H. S. Seo and B. M. Kwak, Efficient statistical tolerance analysis for general distributions using three-point infonnation, International Journal of Production Research, 40 (4) (2000) 931-944. 
    3. J. H. Min, Reliability analysis technique using local approximation of cumulative distribution Function, MS. Thesis, Hanyang University, Korea, (2005). 
    4. A. D. Kiureghian, H. Z. Lin and S. J. Hwang, Second order reliability analysis approximations, Journal of Engineering Mechanics, 113 (8) (1987) 1208-1225. 
    5. B. Fiessler, H. J. Neumann and R, Rackwitz, Quadratic limit states in structural reliability, Journal of Engineering Mechanics, 1095 (4) (1979) 661-676. 
    6. C. A. Comell, A probability-based structural code, Journal of the American Concrete Institute, 66 (12) (1969) 974-985. 
    7. O. S. Lee, D. H. Kim and Y. C. Park, Reliability of structures by using probability and fatigue theories, Journal of Mechanical Science and Technology, 22 (4) (2008) 672-682.     
    8. S. J. Yoon and D. H. Choi, Reliability-based design optimization of slider air bearings, Journal of Mechanical Science and Technology, 18 (10)(2004) 1722-1729.     
    9. A. H. Ang and W. H. Tang, Probability concepts in engineering planning and design, John Wiley & Sons, New York, USA, (1984). 
    10. A. Harbitz, An efficient sampling method for probability of failure calculation, Structural Safety, 3 (1986) lO9-115. 
    11. N. P. Buslenko, D. I. Golenko, Y. A. Shreider, I. M. Sobol and V. G. Sragowich, The Monte Carlo method, Pergamon Press, New York, USA, (1964). 
    12. M. L. Shooman, Probability reliability: An engineering approach, McGraw-Hill, New York, USA, (1968). 
    13. R. E. Melcher, Structural reliability: Analysis and Prediction, Ellis Horwood, (1987). 
    14. E. Saliby, A reappraisal of some simulation fundamentals, Ph.D. Thesis, University of Lancaster, (1980). 
    15. E. Saliby, Descriptive sampling: A better approach to Monte Carlo simulation, Journal of the Operational Research Society, 41 (12) (1990) 1133-1142. 
    16. E. Saliby, Rethinking simulation: descriptive sampling, Sao Paulo: Atlas/EDUFRJ, Portuguese, (1989). 
    17. G. S. Fishman, Monte-Carlo: Concepts, algorithms and applications, Springer-Vedag, (1997). 
    18. K. W. Ross, D. Tsang and J. Wang, Monte-Carlo summation and integration applied to multichain Queueing networks, Journal Association Computer Machine, 41 (6) (1994) 1110-1135. 
    19. K. Ziha, Descriptive sampling in structural safety, Structural Safety, 17 (1995) 33-41. 
    20. B. A. Cullimore, Dealing with uncertainties and variations in thermal design, Proceedings of InterPack '01 Pacific Rim International Electronic Packaging Conference, Kauai, Hawaii (2001). 
    21. D. J. McCormick and J. R. Olds, A design of experimentsbased method for point selection in approximating output distributions, 2002 AIAAlISSMO Symposium on Multidisciplinary Analysis and Design Optimization, Atlanta, GA (2002). 
    22. E. Saliby and F. Pacheco, An empirical evaluation of sampling methods in risk analysis simulation: Quasi-Monte Carlo, descriptive sampling and Latin Hypercube Sampling, Proceedings of the 2002 Winter Simulation Conference, 1606-16l0 (2002). 
    23. J. Staum, S. Ehrlichman and V. Lesnevski, Work reduction in financial simulations, Proceedings of the 2003 Winter Simulation Conference (2003). 
    24. R Development Core Team, R(ver. 2.6.1):A language and environment for statistical computing, R Foundation for statistical computing, Vienna, Austria, URL http://www.rproject.project.org., (2007) . 
    25. E. Saliby, Understanding the variability of simulation estimates: an empirical study, Journal of the Operational Research Society, 41 (1990) 319-327. 
    26. E. Saliby and R. J. Paul, Implementing descriptive sampling in three-phase discrete event simulation models, Journal of the Operational Research Society, 44 (1993) 147-160. 
    27. E. Saliby, Input sample size determination when using descriptive sampling, Proceedings 13th International Conference ITI-1991, Dubrovnik, Croatia (1991). 
    28. L. Wang and R. V. Gradhi, Efficient safety index calculation for structural reliability analysis, Computer and Structures, 52 (1) (1994) 103-111. 
    29. Southwest Research Institute, Probabilistic structural analysis methods (PSAM) for select space propulsion .systerns components, NESSUS Version 6.0 release notes, (1992). 
    30. H. Madsen, S. Krenk and N. Lind, Methods of structural safety, Prentice-Hall, (1986). 
  • 이 논문을 인용한 문헌 (1)

    1. 2011. "" Journal of mechanical science and technology, 25(9): 2151~2159     

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.
유료다운로드

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기