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Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition

dc.contributor.authorChen, J.
dc.contributor.authorPisonero Castro, Jorge 
dc.contributor.authorChen, S.
dc.contributor.authorWang, X.
dc.contributor.authorFan, Q.
dc.contributor.authorDuan, Y.
dc.date.accessioned2020-06-26T07:56:45Z
dc.date.available2020-06-26T07:56:45Z
dc.date.issued2020
dc.identifier.citationSpectrochimica Acta - Part B Atomic Spectroscopy, 166, p. 105801- (2020); doi:10.1016/j.sab.2020.105801
dc.identifier.issn0584-8547
dc.identifier.urihttp://hdl.handle.net/10651/55097
dc.description.sponsorshipThe authors gratefully acknowledge the financial support from the National Major Scientific Instruments and Equipment Development Special Funds (No. 2011YQ030113)
dc.format.extentp. 105801-
dc.language.isoeng
dc.relation.ispartofSpectrochimica Acta - Part B Atomic Spectroscopy
dc.rights© 2020 Elsevier
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85080133872&doi=10.1016%2fj.sab.2020.105801&partnerID=40&md5=35d6ee1dcd40f4d6581a321f115a7745
dc.titleConvolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.sab.2020.105801
dc.type.dcmitext
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.sab.2020.105801


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