Identification of flight state under different simulator modes using improved diffusion maps

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• 作者：B. Jia^a ; ^{icesea137@163.com" class="auth_mail" title="E-mail the corresponding author} ; C.F. Wei^b ; J.F. Mao^c ; R. Law^d ; S. Fu^a ; Q. Wu^a ; ^e ; ^{wuqi7812@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Flight simulator ; Simulator motion ; Diffusion maps ; Bacterial foraging ; Kernel fuzzy c-means algorithm
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：127
• 期：9
• 页码：3905-3911
• 全文大小：2205 K

文摘

To identify the difference between dynamic and static simulator modes, a novel data analyzing method was presented in this paper using flight data sampled from manual flight task. The proposed method combined diffusion maps and kernel fuzzy c-means algorithm (KFCM) to identify types of flight data. Hybrid bacterial foraging (BF) and particle swarm optimization (PSO) algorithm (BF-PSO) was also introduced to optimize unknown parameters of the KFCM. This algorithm increased the possibility to find the optimal values avoided being trapped in local minima. The clustering accuracy of the proposed method applied in flight dataset demonstrated this method had the ability to recognize the types of flight state. The results of the paper indicated that the pilots movement sensing influenced pilot performance under the manual departure task.