Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43952
Title: Identification of flight state under different simulator modes using improved diffusion maps
Authors: Jia, B
Wei, CF
Mao, JF
Law, R 
Fu, S
Wu, Q
Keywords: Bacterial foraging
Diffusion maps
Flight simulator
Kernel fuzzy c-means algorithm
Simulator motion
Issue Date: 2016
Publisher: Urban & Fischer
Source: Optik, 2016, v. 127, no. 9, p. 3905-3911 How to cite?
Journal: Optik 
Abstract: 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.
URI: http://hdl.handle.net/10397/43952
ISSN: 0030-4026
EISSN: 1618-1336
DOI: 10.1016/j.ijleo.2015.12.162
Appears in Collections:Journal/Magazine Article

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