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By Lanfeng Yan, Janjun Ma, Wei Wang, Qing Liu, Qiuyong Zhou (auth.), De-Shuang Huang, Xiao-Ping Zhang, Guang-Bin Huang (eds.)

The foreign convention on clever Computing (ICIC) used to be organize as an annual discussion board devoted to rising and hard subject matters within the numerous points of advances in computational intelligence fields, resembling synthetic intelligence, computer studying, bioinformatics, and computational biology, and so on. The objective of this convention was once to compile researchers from academia and in addition to practitioners to proportion principles, difficulties and ideas concerning the multifaceted elements of clever computing. This ebook constitutes the lawsuits of the foreign convention on clever Computing (ICIC 2005), held in Hefei, Anhui, China, in the course of August 23–26, 2005. ICIC 2005 bought over 2000 submissions from authors in 39 nations and areas. in response to rigorous peer experiences, this system Committee chosen 563 top of the range papers for presentation at ICIC 2005; of those, 215 papers have been released during this publication equipped into nine different types, and the opposite 348 papers have been released in 5 foreign journals. The organizers of ICIC 2005 made nice efforts to make sure the luck of this convention. We right here thank the contributors of the ICIC 2005 Advisory Committee for his or her information and suggestion, the participants of this system Committee and the referees for reviewing the papers, and the individuals of the book Committee for checking and compiling the papers. we'd additionally wish to thank the writer, Springer, for his or her help in publishing the complaints within the Lecture Notes in computing device technological know-how sequence. fairly, we want to thank the entire authors for contributing their papers.

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Example text

Xu x2 Safety G1(X) G2(X) x1 Fig. 4. FOSM Error results as long as the number of the samples is enough, which results in expensive computational efforts. Importance sampling method can reduce the number of samples a lot by introducing the importance sampling probability density function, however, the design point should be determined at first. In reliability analysis, two standard indices are used; the probability of failure and the reliability index. 8, which is accurate only for the specific case of random variables with normal distribution and linear limit state functions.

However, in addition to the proximity of the pseudo-polar coordinates to the polar ones, the other very important benefit is the ability to perform the necessary interpolations via the pure 1D operation without loosing accuracy. To be brief, PFFT decomposes the problem into two steps: first, a Pseudo-Polar FFT is applied, in which a pseudo-polar sampling set is used, and second, a conversion from pseudo-polar to polar Fourier transform is performed. Pseudo-Polar FFT. The pseudo-polar Fourier transform (PPFT) evaluates the 2D Fourier transform of an image on the pseudo-polar grid, which is an approximation to the polar grid.

Monte Carlo method can always produce good 28 W. Jiang, Y. Xu, and Y. Xu x2 Safety G1(X) G2(X) x1 Fig. 4. FOSM Error results as long as the number of the samples is enough, which results in expensive computational efforts. Importance sampling method can reduce the number of samples a lot by introducing the importance sampling probability density function, however, the design point should be determined at first. In reliability analysis, two standard indices are used; the probability of failure and the reliability index.

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