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dc.contributor.authorSánchez-Andrades, Ignacio 
dc.contributor.authorVelasco García, Juan María
dc.contributor.authorSánchez Lozano, Miguel
dc.contributor.authorCabrera-Carrillo, Juan Antonio 
dc.contributor.authorCastillo-Aguilar, Juan Jesús 
dc.date.accessioned2021-10-08T08:33:33Z
dc.date.available2021-10-08T08:33:33Z
dc.date.created2021-10-07
dc.date.issued2021-08-18
dc.identifier.urihttps://hdl.handle.net/10630/22969
dc.description.abstractDetermining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectDatos - Adquisiciónes_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subjectAutomóvileses_ES
dc.subject.otherVibrationses_ES
dc.subject.otherData acquisitiones_ES
dc.subject.otherSurface estimationes_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherAutomobile systems.es_ES
dc.titleLow-Cost Surface Classification System Supported by Deep Neural Modelses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.relation.eventtitleIAVSD 2021es_ES
dc.relation.eventplaceSan Petersburgoes_ES
dc.relation.eventdate17-19 de agosto de 2021es_ES


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