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

B. Jia, C. F. Wei, J. F. Mao, Chun Hung Roberts Law, S. Fu, Q. Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)3905-3911
Number of pages7
Issue number9
Publication statusPublished - 1 May 2016


  • Bacterial foraging
  • Diffusion maps
  • Flight simulator
  • Kernel fuzzy c-means algorithm
  • Simulator motion

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering


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