Fuli Shi

Work place: School of Information Systems & Management, National University of Defense Technology, Changsha, China

E-mail: shifuli_83@163.com


Research Interests: Computer systems and computational processes, Data Structures and Algorithms, Analysis of Algorithms, Models of Computation


Fuli Shi received the Bachelor of Mathematics and Applied Mathematics from the Sichuan University, Chengdu, China, in 2004, received the Master of Engineering in System Engineering from the National University of Defense Technology, Changsha, China, in 2006. She is a full-time PhD student in Military Equipment of the National University of Defense Technology at present. During her master study, she mainly studies Weapon Equipment Requirement Analysis methods. Currently her research interest covers simulation and assessment for Weapon Equipment System of Systems. She has published more than 15 papers. 

Author Articles
Robustness Evaluation for Military Communication Effectiveness based on Multiple Data Sources and Monte Carlo Simulation

By Fuli Shi Chao Li Yifan Zhu

DOI: https://doi.org/10.5815/ijmecs.2011.05.01, Pub. Date: 8 Oct. 2011

In the choice process of optimal military commu-nication (MC) alternative, evaluation data mainly come from expert judgments, simulation results and test bed data, and they cannot be directly used in evaluation because of differences in form and attribute; and the MC environment changes rapidly as the operation tempo increasing. It is an important effort to judge the effectiveness robustness of MC alternative, since both the evaluation data and the MC envi-ronment are full of uncertainty. A robustness evaluation method based on multiple data sources and Monte Carlo simluation is proposed with respect to the characteristics of them. Mainly include Belief map as data expression form; Regression relational model built with Support Vector Re-gression (SVR) to acquire simulation data’s confidence with test bed data as training example; Extensive Bayesian Algo-rithm (EBA) to fuse data from multiple sources; Beta distri-bution fitting method for each criterion of each alternative by using the fused results; and calculation of the Probability of Best (PoB) of each alternative through Monte Carlo simu-lation. Take MCE evaluation of a Naval Vessels Fleet as an example, the proposed method is compared with some gen-eral methods. The results indicate that the proposed method helps to obtain relatively conservative alternative and is effective in guaranteeing the robustness.

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