Simulated annealing based approach for near-optimal sensor selection in Gaussian Processes

Van Nguyen, L; Kodagoda, S; Ranasinghe, R; Dissanayake, G

Simulated annealing based approach for near-optimal sensor selection in Gaussian Processes

Van Nguyen, L

Kodagoda, S

Ranasinghe, R

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2012 International Conference on Control, Automation and Information Sciences, ICCAIS 2012, 2012, pp. 142 - 147
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Van Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012 International Conference on Control, Automation and Information Sciences, ICCAIS 2012, 2012, pp. 142 - 147	en_US
dc.identifier.isbn	9781467308137	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29457
dc.description.abstract	This paper addresses the sensor selection problem associated with monitoring spatial phenomena, where a subset of k sensor measurements from among a set of n potential sensor measurements is to be chosen such that the root mean square prediction error is minimised. It is proposed that the spatial phenomena to be monitored is modelled using a Gaussian Process and a simulated annealing based approximately heuristic algorithm is used to solve the resulting minimisation problem. The algorithm is shown to be computationally efficient and is illustrated using both indoor and outdoor environment monitoring scenarios. It is shown that, although the proposed algorithm is not guaranteed to find the optimum, it always provides accurate solutions for broad range real-world and computer generated datasets. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 International Conference on Control, Automation and Information Sciences, ICCAIS 2012	en_US
dc.relation.isbasedon	10.1109/ICCAIS.2012.6466575	en_US
dc.title	Simulated annealing based approach for near-optimal sensor selection in Gaussian Processes	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Ho Chi Minh City, Vietnam	en_US
dc.location.activity	VIETNAM
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper addresses the sensor selection problem associated with monitoring spatial phenomena, where a subset of k sensor measurements from among a set of n potential sensor measurements is to be chosen such that the root mean square prediction error is minimised. It is proposed that the spatial phenomena to be monitored is modelled using a Gaussian Process and a simulated annealing based approximately heuristic algorithm is used to solve the resulting minimisation problem. The algorithm is shown to be computationally efficient and is illustrated using both indoor and outdoor environment monitoring scenarios. It is shown that, although the proposed algorithm is not guaranteed to find the optimum, it always provides accurate solutions for broad range real-world and computer generated datasets. © 2012 IEEE.

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http://hdl.handle.net/10453/29457