Enhanced photoacoustic signal processing using empirical mode decomposition and machine learning

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dc.authorid0000-0003-4236-3646
dc.authorid0000-0002-1389-1784
dc.contributor.authorBalci, Zekeriya
dc.contributor.authorMert, Ahmet
dc.date.accessioned2026-02-08T15:15:34Z
dc.date.available2026-02-08T15:15:34Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, we propose a robust photoacoustic (PA) signal processing framework for a material independent defect detection using empirical mode decomposition (EMD) and machine learning algorithms. First, a database of the PA signals with 960 samples has been obtained from aluminium, iron, plastic and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Second, the EMD based time and time-frequency domain techniques are proposed to extract robust cross-material feature space focusing on laser induced acoustic signal, and the decomposed intrinsic mode (IMF) with 14 extracted features are performed on totally 960 samples PA signals to evaluate k-nearest neighbour (k-NN), decision tree (DT) and support vector machine (SVM) classifiers. Inter- material and cross-material evaluations are performed, and the accuracy rates up to 100% for SVM and 97.77% for k-NN are yielded.
dc.description.sponsorshipBursa Technical University [210D003]
dc.description.sponsorshipThe work was supported by the Research Fund of Bursa Technical University [210D003].
dc.identifier.doi10.1080/10589759.2024.2373318
dc.identifier.endpage2056
dc.identifier.issn1058-9759
dc.identifier.issn1477-2671
dc.identifier.issue5
dc.identifier.scopus2-s2.0-105002907021
dc.identifier.scopusqualityQ2
dc.identifier.startpage2044
dc.identifier.urihttps://doi.org/10.1080/10589759.2024.2373318
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5850
dc.identifier.volume40
dc.identifier.wosWOS:001258134700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofNondestructive Testing and Evaluation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectPhotoacoustic
dc.subjectempirical mode decomposition
dc.subjectsupport vector machine
dc.subjectk-nearest neighbour
dc.subjectdecision tree
dc.subjectnon-destructive testing
dc.titleEnhanced photoacoustic signal processing using empirical mode decomposition and machine learning
dc.typeArticle

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