Human factors quantification via boundary identification of flight performance margin

• 作 者: Yang Changpeng Yin Tangwen Zhao Weina Huang Dan Fu Shan
• 机 构: School of Aeronautics and Astronautics, Shanghai Jiao Tong University,
• 出 处: Chinese Journal of Aeronautics
• 发表日期: 2014年04期
• 关键词: Computational pilot model Flight performance Human factors quantifica-tion Monte

A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled: information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo(MC) simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor(k-NN) classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors.

A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled: information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight Kernel density estimation algorithm is selected and find the rank of influential human factors. Kernel density estimation algorithm is selected and find the rank of the influential human factors. -NN) classifier. Simulation-based analysis sho ws that flight performance can be dramatically improved with the quantitative human factors.