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dc.contributor.authorCugueró Escofet, Miquel Àngel
dc.contributor.authorQuevedo Casín, Joseba Jokin
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorGarcía Valverde, Diego
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2014-09-16T09:00:03Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationCuguero, M.A. [et al.]. Inconsistent sensor data detection/correction: application to environmental systems. A: IEEE International Joint Conference on Neural Networks. "Proceedings IEEE 2014 International Joint Conference on Neural Networks". Beijing: 2014, p. 84-90.
dc.identifier.isbn978-1-4799-1483-8
dc.identifier.urihttp://hdl.handle.net/2117/24065
dc.description.abstractIn this paper, a data detection/correction approach is proposed for a real environmental monitoring system, in order to provide a reliable dataset when sensor faults occur. This is the case of communication faults that may prevent the acquisition of a complete dataset, which is of paramount importance in order to successfully apply further system tasks such as fault diagnosis. Sensor detection/correction method presented here is based on the combined used of spatial and time series models. Spatial models take advantage of the physical relation between different variables emplaced in the system (temperature sensors here) while time series models take advantage of the temporal redundancy of the measured variables, by means of Holt-Winters models here. The proposed approach is successfully applied to the rock collapse forecasting system in the Torrioni di Rialba located in Lombardy (Italy).
dc.format.extent7 p.
dc.language.isoeng
dc.subject.lcshEnvironmental monitoring
dc.subject.lcshSensor network
dc.titleInconsistent sensor data detection/correction: application to environmental systems
dc.typeConference report
dc.subject.lemacTorrioni di Rialba (Lombardia)
dc.subject.lemacSeguiment ambiental
dc.subject.lemacXarxes de sensors
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1109/IJCNN.2014.6889741
dc.identifier.dlIEEE catalog number CFP14IJS-CDR
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac15141738
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorCuguero, M.A.; Quevedo, J.; Puig, V.; Garcia, D.
local.citation.contributorIEEE International Joint Conference on Neural Networks
local.citation.pubplaceBeijing
local.citation.publicationNameProceedings IEEE 2014 International Joint Conference on Neural Networks
local.citation.startingPage84
local.citation.endingPage90


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