Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien...Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.展开更多
Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is dif...Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%.展开更多
文摘Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.
基金supported in part by the National Natural Science Foundation of China under Grant No. 60873216Scientific and Technological Research Priority Projects of Sichuan Province under Grant No. 2012GZ0017Basic Research of Application Fund Project of Sichuan Province under Grant No. 2011JY0100
文摘Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%.
