A Statistical Assessment of the Performance of FSV

作者

  • G. Zhang School of Electrical Engineering and Automation Harbin Institute of Technology, Harbin, 150001, China
  • H. Sasse School of Engineering De Montfort University, Leicester, LE1 9BH, UK
  • L. Wang School of Electrical Engineering and Automation Harbin Institute of Technology, Harbin, 150001, China
  • A. Duffy School of Engineering De Montfort University, Leicester, LE1 9BH, UK

关键词:

Computational electromagnetics, EMC, feature selective validation, FSV, statistical validity

摘要

This paper assesses the performance of the feature selective validation (FSV) method by applying probability density functions to the FSV point-by-point analysis. As an augmentation to confidence histograms, probability density functions offer two advantages: they (1) provide the users of FSV with more subtle information about the quality of the data comparison and (2) make a statistical analysis of the FSV results available. The application of probability density functions in the verification of FSV is presented in this paper, which provides a quantitative measure to support the qualitative conclusions drawn in early publications on the FSV method used as a foundation for IEEE Std. 1597.1.

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参考

IEEE Standard for Validation of Computational Electromagnetics Computer Modeling and Simulations, IEEE STD 1597.1-2008, pp. 1-41, 2008.

G. Zhang, A. Duffy, H. Sasse, and W. Lixin, “The use of probability density functions to improve the interpretation of FSV results,” IEEE International Symposium on EMC, U.S., pp. 685-689, 2012.

A. Hastings, G. Palafox, and K. Tamhankar, “Assessment of feature selective validation (FSV) method for comparing modeled antenna patterns against measurements,” 27th Annual Review of Progress in Applied Computational Electromagnetics (ACES), pp. 885-890, Virginia U.S., March 2011.

A. Denton, A. Martin, and A. Duffy, “Quantifying EMC measurement accuracy using feature selective validation,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 23, no. 1, pp. 104-109, March 2008.

A. Drozd, I. Kasperovich, C. Carroll, Jr., and A. Croneiser, “Antenna siting sensitivity study using the feature selective validation,” 26th Annual Review of Progress in Applied Computational Electromagnetics (ACES), pp. 573-580, Tampere, Finland, April 2010.

L. Hiltz and B. Archambeault, “Comparison of the modelled and measured antenna radiation pattern of a parabolic reflector using FSV,” 25th Annual Review of Progress in Applied Computational Electromagnetics (ACES), pp. 173-177, California, U.S., March 2009.

A. Duffy, A. Martin, A. Orlandi, G. Antonini, T. Benson, and M. Woolfson, “Feature selective validation (FSV) for validation of computational electromagnetics (CEM). Part I - The FSV method,” IEEE Transactions on Electromagnetic Compatibility, vol. 48, pp. 449-459, Aug. 2006.

A. Orlandi, A. Duffy, B. Archambeault, G. Antonini, D. Coleby, and S. Connor, “Feature selective validation (FSV) for validation of computational electromagnetics (CEM). Part II - Assessment of FSV performance,” IEEE Transactions on Electromagnetic Compatibility, vol. 48, pp. 460-467, Aug. 2006.

J. Simonoff, Smoothing Methods in Statistics, New York: Springer Verlag, 1996. [10] F. Massey, “The Kolmogorov-Smirnov test for goodness of fit,” Journal of the American Statistical Association, vol. 46, pp. 68-78, 1951.

N. Smirnov, “Tables for estimating the goodness of fit of empirical distributions,” Annals of Mathematical Statistics, vol. 19, pp. 279-281, 1948.

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已出版

2021-09-19

栏目

General Submission