期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 8, 页码 11338-11348出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3103068
关键词
Gaussian processes; Atmospheric modeling; Task analysis; Brain modeling; Pupils; Heart rate variability; Physiology; Flight performance; Gaussian process; sparse stacked Gaussian process network; physiological parameters; workload
资金
- National Natural Science Foundation of China [62171274, U1933125, 61903175, 62163024]
- Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies [075-15-2020-903]
The study introduces a SGPN model to analyze the relationship between flight performance and multi-physiological parameters, improving learning efficiency with an auxiliary variable strategy and achieving a sparse model. The model solves complex nonlinear relationships through variational approximate inference.
The relationship between flight performance and multi-physiological parameters under different flight operating patterns is unknown. This work proposes a Stacked Gaussian Process Network (SGPN) to reveal it. SGPN is a multi-layer network model formed by recursion from a regular Gaussian process and random disturbance. This work constructs an auxiliary variable strategy with the induced points to improve its learning efficiency, thus leading to a sparse SGPN model. In it, a Gaussian process acts as an activation function of each node, but the entire model is no longer a Gaussian process and thus very challenging to solve it. This work presents its solution via variational approximate inference. Experimental results of pilot flight performance evaluation show that the proposed model has stronger learning and generalization ability than its seven competitive peers. It is able to approximate non-linear coupling relationship between multi-physiological parameters and flight height differences.
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