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Conference Paper: Dendritic Cells for Behaviour Detection in Immersive Virtual Reality Training

TitleDendritic Cells for Behaviour Detection in Immersive Virtual Reality Training
Authors
KeywordsArtificial Immune Systems
Dendritic Cell Algorithm
Human behaviour detection
Virtual reality
Field operations training
Issue Date2016
PublisherSpringer International Publishing.
Citation
The 36th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, United Kingdom, 13-15 December 2016. In Bramer M & Petridis M. (eds), Research and Development in Intelligent Systems XXXIII. SGAI 2016, p. 371-376 How to Cite?
AbstractThis paper presents a cross-disciplinary research of artificial intelligence (AI) and virtual reality (VR) that presents a real application of an aircraft door operation training conducted in an immersive virtual environment. In the context of the study, AI takes an imperative role in distinguishing misbehaviour of trainees such as inappropriate steps and positions in the virtual training environment that mimics a real training scenario. Trainee’s behaviours are detected by the classical Dendritic Cell Algorithm (DCA) which is a signal-based classification approach that is inspired from the segmented detection and interaction with the signal molecules mechanisms of the human dendritic cells. The resulted approach demonstrated accurate detection and classification processes that are evidence in the experimental studies. This position paper also reveals the ability of the DCA method in human behaviour detection/classification in a dynamic environment.
Persistent Identifierhttp://hdl.handle.net/10722/241702
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLee, MYN-
dc.contributor.authorLau, HYK-
dc.contributor.authorWong, HK-
dc.contributor.authorTam, WLW-
dc.contributor.authorChan, LKY-
dc.date.accessioned2017-06-20T01:47:23Z-
dc.date.available2017-06-20T01:47:23Z-
dc.date.issued2016-
dc.identifier.citationThe 36th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, United Kingdom, 13-15 December 2016. In Bramer M & Petridis M. (eds), Research and Development in Intelligent Systems XXXIII. SGAI 2016, p. 371-376-
dc.identifier.isbn9783319471747-
dc.identifier.urihttp://hdl.handle.net/10722/241702-
dc.description.abstractThis paper presents a cross-disciplinary research of artificial intelligence (AI) and virtual reality (VR) that presents a real application of an aircraft door operation training conducted in an immersive virtual environment. In the context of the study, AI takes an imperative role in distinguishing misbehaviour of trainees such as inappropriate steps and positions in the virtual training environment that mimics a real training scenario. Trainee’s behaviours are detected by the classical Dendritic Cell Algorithm (DCA) which is a signal-based classification approach that is inspired from the segmented detection and interaction with the signal molecules mechanisms of the human dendritic cells. The resulted approach demonstrated accurate detection and classification processes that are evidence in the experimental studies. This position paper also reveals the ability of the DCA method in human behaviour detection/classification in a dynamic environment.-
dc.languageeng-
dc.publisherSpringer International Publishing.-
dc.relation.ispartofResearch and Development in Intelligent Systems XXXIII, SGAI 2016-
dc.subjectArtificial Immune Systems-
dc.subjectDendritic Cell Algorithm-
dc.subjectHuman behaviour detection-
dc.subjectVirtual reality-
dc.subjectField operations training-
dc.titleDendritic Cells for Behaviour Detection in Immersive Virtual Reality Training-
dc.typeConference_Paper-
dc.identifier.emailLee, MYN: h0171306@connect.hku.hk-
dc.identifier.emailLau, HYK: hyklau@hkucc.hku.hk-
dc.identifier.emailWong, HK: rocky321@hku.hk-
dc.identifier.emailTam, WLW: tamwlhku@hku.hk-
dc.identifier.emailChan, LKY: lkychan@hku.hk-
dc.identifier.authorityLau, HYK=rp00137-
dc.identifier.doi10.1007/978-3-319-47175-4_27-
dc.identifier.hkuros272871-
dc.identifier.spage371-
dc.identifier.epage376-
dc.publisher.placeCham-

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