dc.contributor.author | Zhang, Jianhua | |
dc.contributor.author | Li, Jianrong | |
dc.date.accessioned | 2023-01-27T10:28:53Z | |
dc.date.available | 2023-01-27T10:28:53Z | |
dc.date.created | 2022-02-18T16:15:58Z | |
dc.date.issued | 2021-04-14 | |
dc.identifier.citation | IFAC-PapersOnLine. 2020, 53 (2), 10242-10249. | en_US |
dc.identifier.issn | 2405-8963 | |
dc.identifier.uri | https://hdl.handle.net/11250/3046810 | |
dc.description.abstract | Real-time monitoring and analysis of human operator’s mental workload (MWL) is crucial for development of adaptive/intelligent human-machine cooperative systems in various safety/mission-critical application fields. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, it is usually difficult to acquire sufficient labeled data to train the ML model. This paper proposes semi-supervised extreme learning machines (SS-ELM) for MWL pattern classification using solely a small number of labeled data. The experimental data analysis results are presented to show the effectiveness of the proposed SS-ELM paradigm to effectively exploit a large number of unlabeled data for the real-world 3- or 4-class MWL classification problem. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartofseries | IFAC-PapersOnLine;Volume 53, Issue 2 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | Human factors | en_US |
dc.subject | Mental workloads | en_US |
dc.subject | Operator functional states | en_US |
dc.subject | EEG signals | en_US |
dc.subject | Neuroimaging | en_US |
dc.subject | Semi-supervised learning | en_US |
dc.subject | Extreme learning machines | en_US |
dc.title | Classifying Mental Workload Levels Using Semi-Supervised Learning Technique | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2020 The Authors | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | https://doi.org/10.1016/j.ifacol.2020.12.2755 | |
dc.identifier.cristin | 2003490 | |
dc.source.journal | IFAC-PapersOnLine | en_US |
dc.source.volume | 53 | en_US |
dc.source.issue | 2 | en_US |
dc.source.pagenumber | 10242-10249 | en_US |