Fatigue Detection of Pilots' Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model.

Wu, EQ; Lin, C-T; Zhu, L-M; Tang, ZR; Jie, Y-W; Zhou, G-R

Fatigue Detection of Pilots' Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model.

Wu, EQ Lin, C-T Zhu, L-M Tang, ZR Jie, Y-W Zhou, G-R

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Cybern, 2022, PP, (11), pp. 12302-12314
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	Fatigue_Detection_of_Pilots_Brain_Through_Brains_Cognitive_Map_and_Multilayer_Latent_Incremental_Learning_Model.pdf	Published version	3.69 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, EQ
dc.contributor.author	Lin, C-T
dc.contributor.author	Zhu, L-M
dc.contributor.author	Tang, ZR
dc.contributor.author	Jie, Y-W
dc.contributor.author	Zhou, G-R
dc.date.accessioned	2023-03-23T04:10:18Z
dc.date.available	2023-03-23T04:10:18Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Trans Cybern, 2022, PP, (11), pp. 12302-12314
dc.identifier.issn	2168-2267
dc.identifier.issn	2168-2275
dc.identifier.uri	http://hdl.handle.net/10453/168191
dc.description.abstract	This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Cybern
dc.relation.isbasedon	10.1109/TCYB.2021.3068300
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Brain
dc.subject.mesh	Cognition
dc.subject.mesh	Humans
dc.subject.mesh	Learning
dc.subject.mesh	Markov Chains
dc.subject.mesh	Pilots
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Markov Chains
dc.subject.mesh	Cognition
dc.subject.mesh	Learning
dc.subject.mesh	Pilots
dc.subject.mesh	Brain
dc.subject.mesh	Cognition
dc.subject.mesh	Humans
dc.subject.mesh	Learning
dc.subject.mesh	Markov Chains
dc.subject.mesh	Pilots
dc.title	Fatigue Detection of Pilots' Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-23T04:10:16Z
pubs.issue	11
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	11

Abstract:

This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168191