Huairong Shen

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.

Author Articles
Study on the Impact Breakup Model of the Space Target Based on the Thin Plate

By Weijie Wang Huairong Shen Yiyong Li

DOI:, 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.

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Clustering Belief Functions using Extended Agglomerative Algorithm

By Ying Peng Huairong Shen Zenghui Hu Yongyi Ma

DOI:, 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.

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