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, "Multi-Objective Reconstruction of Random Media using GPU Computing", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 35, 2015. doi:10.4203/ccp.109.35

Keywords: image reconstruction, multi-objective optimization, non-dominated sorting genetic algorithm II, two-point probability function, lineal path function, GPU.

Summary

This paper deals with a reconstruction of random media. The optimization with statistical descriptors (namely two-point probability function and two-point lineal path function) and minimizing the errors between the statistical descriptors evaluated on the original medium and on the reconstructed surrogate medium are utilized. AS a result of doubts concerning the setting of the weights in the weighted-sum method, a purely multi-objective optimization routine is selected, specifically the non-dominated sorting genetic algorithm II. The volume constraint is satisfied using a special mutation operator that interchanges only pixels from different phases which is the only operator used in the work described in this paper. As a result of the high computational demands, the lineal path was evaluated in a parallel fashion utilizing graphical processing units. The main contribution is in the testing of the proposed methodology on several benchmark images.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 109 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping Paper 35 Multi-Objective Reconstruction of Random Media using GPU Computing A. Pospíšilová, J. Havelka and M. Lepš Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic doi:10.4203/ccp.109.35 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Multi-Objective Reconstruction of Random Media using GPU Computing", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 35, 2015. doi:10.4203/ccp.109.35 Keywords: image reconstruction, multi-objective optimization, non-dominated sorting genetic algorithm II, two-point probability function, lineal path function, GPU. Summary This paper deals with a reconstruction of random media. The optimization with statistical descriptors (namely two-point probability function and two-point lineal path function) and minimizing the errors between the statistical descriptors evaluated on the original medium and on the reconstructed surrogate medium are utilized. AS a result of doubts concerning the setting of the weights in the weighted-sum method, a purely multi-objective optimization routine is selected, specifically the non-dominated sorting genetic algorithm II. The volume constraint is satisfied using a special mutation operator that interchanges only pixels from different phases which is the only operator used in the work described in this paper. As a result of the high computational demands, the lineal path was evaluated in a parallel fashion utilizing graphical processing units. The main contribution is in the testing of the proposed methodology on several benchmark images. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £50 +P&P)
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