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dc.contributor.authorLee, Kyungmien_US
dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.date.accessioned2017-04-24T11:31:05Z
dc.date.available2017-04-24T11:31:05Z
dc.date.issued2007en_US
dc.identifier.issn15684946en_US
dc.identifier.doi10.1016/j.asoc.2005.05.003en_US
dc.identifier.urihttp://hdl.handle.net/10072/17246
dc.description.abstractDiscrete wavelet transform (DWT) coefficients of ultrasonic test signals are considered useful features for input into classifiers due to their effective time-frequency representation of non-stationary signals. However, DWT exhibits a time-variance problem that has resulted in reservations for its wide acceptance. In this paper, a new technique to derive a preprocessing method for time-domain A-scans signal is presented. This technique offers consistent extraction of a segment of the signal from long signals that occur in the non-destructive testing of shafts. Two different classifiers using artificial neural networks and support vector machines are supplied with features generated by our new preprocessing method and their classification performance are compared and evaluated. Their performances are also compared with other alternatives and report the results here. This investigation establishes experimentally that DWT coefficients can be used as a feature extraction scheme more reliably by using our new preprocessing technique.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevier B.V.en_US
dc.publisher.placeHollanden_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom156en_US
dc.relation.ispartofpageto165en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalApplied Soft Computingen_US
dc.relation.ispartofvolume7en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode280207en_US
dc.titleFeature extraction and gating techniques for ultrasonic shaft signal classificationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.date.issued2015-02-12T00:44:14Z
gro.hasfulltextNo Full Text


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