A fuzzy dual expert system for managing situation awareness in a safety supervisory system

Naderpour, M; Lu, J

A fuzzy dual expert system for managing situation awareness in a safety supervisory system

Naderpour, M

Lu, J

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2012
Issue Date:: 2012-10-23

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Field	Value	Language
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-10-23	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2012	en_US
dc.identifier.isbn	9781467315067	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30850
dc.description.abstract	Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators' understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2012.6251164	en_US
dc.title	A fuzzy dual expert system for managing situation awareness in a safety supervisory system	en_US
dc.type	Conference Proceeding
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brisbane, Australia
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators' understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts. © 2012 IEEE.

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http://hdl.handle.net/10453/30850