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Semi-supervised bayesian classification of materials with impact-echo signals

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Semi-supervised bayesian classification of materials with impact-echo signals

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dc.contributor.author Igual García, Jorge es_ES
dc.contributor.author Salazar Afanador, Addisson es_ES
dc.contributor.author Safont Armero, Gonzalo es_ES
dc.contributor.author Vergara Domínguez, Luís es_ES
dc.date.accessioned 2016-07-14T12:04:22Z
dc.date.available 2016-07-14T12:04:22Z
dc.date.issued 2015-05
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10251/67602
dc.description.abstract [EN] The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process. es_ES
dc.description.sponsorship This work has been supported by Generalitat Valenciana under Grants PROMETEO II/2014/032, ISIC/2012/006 and GV/2014/034.
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation GV/PROMETEO II/2014/032 es_ES
dc.relation GV/ISIC/2012/006 es_ES
dc.relation GV/GV/2014/034 es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Impact echo es_ES
dc.subject Accelerometers es_ES
dc.subject Mixture of Gaussians es_ES
dc.subject Semi-supervised es_ES
dc.subject Bayes classification
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Semi-supervised bayesian classification of materials with impact-echo signals es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s150511528
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation Igual García, J.; Salazar Afanador, A.; Safont Armero, G.; Vergara Domínguez, L. (2015). Semi-supervised bayesian classification of materials with impact-echo signals. Sensors. 15(5):11528-11550. doi:10.3390/s150511528 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/s150511528 es_ES
dc.description.upvformatpinicio 11528 es_ES
dc.description.upvformatpfin 11550 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 15 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 298056 es_ES
dc.identifier.pmid 25996512 en_EN
dc.identifier.pmcid PMC4481956 en_EN
dc.contributor.funder Generalitat Valenciana


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