Work place: Academy of Equipment Command & Technology, Department of Space Equipment, Beijing, China
Research Interests: Physics, Physics & Mathematics, Computational Physics
Huairong Shen was born in Shucheng, Anhui province, China, in 1954. He received Ph.D. degree of engineering in 1985 from National University of Defense Technology, Changsha, Hunan, China.
He is currently a professor and doctor supervisor of Aeronautics in Academy of Equipment Command & Technology. His research interests include fault diagnosis, Unmanned Aerial Vehicle technique, Avionic Material, etc.
DOI: https://doi.org/10.5815/ijitcs.2011.02.07, Pub. Date: 8 Mar. 2011
In the paper, an engineering model for the im-pact breakup of the space target is studied based on the thin plate. The average fragment size model for the impact breakup of the thin plate is established depending on the strain rate, according as Poisson statistic fragments are discrete and distribution model is figured out. On the foundation of the constitution analysis for the target and projectile, the target equivalent model based on the thin plate is established, and projectile equivalent model is also given. The length and velocity degraded model are set up against the cylindrical projectile. The simulation case is analyzed and the result indicates that the paper model is effective, flexible and has important engineering reference value.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2011.01.05, Pub. Date: 8 Feb. 2011
Clustering belief functions is not easy because of uncertainty and the unknown number of clusters. To overcome this problem, we extend agglomerative algorithm for clustering belief functions. By this extended algorithm, belief distance is taken as dissimilarity measure between two belief functions, and the complete-link algorithm is selected to calculate the dissimilarity between two clusters. Before every merging of two clusters, consistency test is executed. Only when the two clusters are consistent, they can merge, otherwise, dissimilarity between them is set to the largest value, which prevents them from merging and assists to determine the number of final clusters. Typical illustration shows same promising results. Firstly, the extended algorithm itself can determine the number of clusters instead of needing to set it in advance. Secondly, the extended algorithm can deal with belief functions with hidden conflict. At last, the algorithm extended is robust.[...] Read more.
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