Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

Nguyen, L; Thiyagarajan, K; Ulapane, N; Kodagoda, S

Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

Nguyen, L Thiyagarajan, K

Ulapane, N Kodagoda, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 1181-1186
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Nguyen, L
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Ulapane, N
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.date	2021-08-01
dc.date.accessioned	2021-12-27T10:30:15Z
dc.date.available	2021-12-27T10:30:15Z
dc.date.issued	2021-08-01
dc.identifier.citation	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 1181-1186
dc.identifier.isbn	9781665422482
dc.identifier.uri	http://hdl.handle.net/10453/152532
dc.description.abstract	The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matérn cross-covariance function, cross-covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications
dc.relation.hasversion	Accepted Manuscript version	en
dc.relation.isbasedon	10.1109/iciea51954.2021.9516255
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Multimodal Sensor Selection for Multiple Spatial Field Reconstruction
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Chengdu, China
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 - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-08-30T00:00:00+1000Z
dc.date.updated	2021-12-27T10:30:14Z
pubs.finish-date	2021-08-04
pubs.publication-status	Published
pubs.start-date	2021-08-01
pubs.volume	00

Abstract:

The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matérn cross-covariance function, cross-covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.

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http://hdl.handle.net/10453/152532