{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:47:06Z","timestamp":1780444026556,"version":"3.54.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171274"],"award-info":[{"award-number":["62171274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1933125"],"award-info":[{"award-number":["U1933125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61825305"],"award-info":[{"award-number":["61825305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Major Project","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China through the Main Research Project on Machine Behavior and Human\u2013Machine Collaborated Decision Making Methodology","doi-asserted-by":"publisher","award":["72192820"],"award-info":[{"award-number":["72192820"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Third Research Project on Human Behavior in Human\u2013Machine Collaboration","doi-asserted-by":"publisher","award":["72192822"],"award-info":[{"award-number":["72192822"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1109\/tits.2022.3189981","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T20:35:43Z","timestamp":1660077343000},"page":"21729-21739","source":"Crossref","is-referenced-by-count":10,"title":["Inferring Cognitive State of Pilot\u2019s Brain Under Different Maneuvers During Flight"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-9870","authenticated-orcid":false,"given":"Edmond Q.","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengtao","family":"Cao","sequence":"additional","affiliation":[{"name":"Medical Center, PLA, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6156-1766","authenticated-orcid":false,"given":"Poly Z. H.","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-4172","authenticated-orcid":false,"given":"Dongfang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7199-3757","authenticated-orcid":false,"given":"Rob","family":"Law","sequence":"additional","affiliation":[{"name":"Asia-Pacific Academy of Economics and Management, University of Macau, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3238-745X","authenticated-orcid":false,"given":"Xin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, Institute of Unmanned Systems, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3194-6731","authenticated-orcid":false,"given":"Li-Min","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengsun","family":"Yu","sequence":"additional","affiliation":[{"name":"Medical Center, PLA, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","article-title":"ROpenPose: A rapider OpenPose model for astronaut operation attitude detection","author":"wu","year":"2020","journal-title":"IEEE Trans Ind Electron"},{"key":"ref32","article-title":"A latent factor analysis-based approach to online sparse streaming feature selection","author":"wu","year":"2021","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2019.2963476"},{"key":"ref30","article-title":"Accurate, large minibatch SGD: Training ImageNet in 1 hour","author":"goyal","year":"2017","journal-title":"arXiv 1706 02677"},{"key":"ref36","article-title":"Scalable gamma-driven multilayer network for brain workload detection through functional near-infrared spectroscopy","author":"wu","year":"2021","journal-title":"IEEE Trans Cybern"},{"key":"ref35","article-title":"Fatigue detection of pilots’ brain through brain cognitive map and multi-layer latent incremental learning model","author":"wu","year":"2021","journal-title":"IEEE Trans Cybern"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2022.3148141"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3166911"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3167271"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3165353"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3163458"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IST48021.2019.9010483"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886414"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2019.8914286"},{"key":"ref17","first-page":"2264","article-title":"Driver fatigue detection through deep transfer learning in an electroencephalogram-based system","volume":"41","author":"wang","year":"2019","journal-title":"J Electron Inf Technol"},{"key":"ref18","first-page":"98","article-title":"Analysis of fatigue EEG characteristics based on restricted Boltzmann machines","volume":"39","author":"gan","year":"2020","journal-title":"Meas Control Technol"},{"key":"ref19","article-title":"A temporal–spatial deep learning approach for driver distraction detection based on EEG signals","author":"li","year":"2021","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"ref28","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICICT43934.2018.9034368"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/sleep\/14.3.221"},{"key":"ref3","first-page":"1072","article-title":"Comparative analysis of deep convolutional generative adversarial network and conditional generative adversarial network using hand written digits","author":"vishwakarma","year":"2020","journal-title":"Proc 4th Int Conf Intell Comput Control Syst (ICICCS)"},{"key":"ref6","first-page":"1","article-title":"Interference suppression generative adversarial nets","volume":"42","author":"li","year":"2020","journal-title":"J Nat'l Univ of Defense Technology"},{"key":"ref29","first-page":"iii-1139","article-title":"On the importance of initialization and momentum in deep learning","volume":"28","author":"sutskever","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref5","first-page":"188","article-title":"An joint generative adversarial network model for classification of benign and malignant pulmonary nodules","volume":"41","author":"wang","year":"2020","journal-title":"Chin J Sci Instrum"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2016.2608003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3160502"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3052801"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3173397"},{"key":"ref1","article-title":"Inferring flight performance under different maneuvers with pilot’s multi-physiological parameters","author":"wu","year":"2021","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2018.2829981"},{"key":"ref22","first-page":"1613","article-title":"Weight uncertainty in neural networks","volume":"37","author":"blundell","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3159602"},{"key":"ref24","first-page":"2611","article-title":"Fast and scalable Bayesian deep learning by weight-perturbation in Adam","volume":"80","author":"khan","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref23","first-page":"5852","article-title":"Noisy natural gradient as variational inference","volume":"80","author":"zhang","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.043"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3136602"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9942712\/09852816.pdf?arnumber=9852816","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:33:18Z","timestamp":1670873598000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9852816\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":36,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2022.3189981","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11]]}}}