Deep convolutional neural network for mill feed size characterization

dc.contributor.authorOlivier, Laurentz Eugene
dc.contributor.authorMaritz, M.G. (Michael)
dc.contributor.authorCraig, Ian Keith
dc.contributor.emailian.craig@up.ac.zaen_ZA
dc.date.accessioned2020-09-07T05:40:41Z
dc.date.available2020-09-07T05:40:41Z
dc.date.issued2019
dc.description.abstractKnowing the characteristics of the feed ore size is an important consideration for operations and control of a run-of-mine ore milling circuit. Large feed ore variations are important to detect as they require intervention, whether it be manual by the operator or by an automatic controller. A deep convolutional neural network is used in this work to classify the feed ore images into one of four classes. A VGG16 architecture is used and the classifier is trained making use of transfer learning.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.librarianam2020en_ZA
dc.description.sponsorshipThe National Research Foundation of South Africaen_ZA
dc.description.urihttps://www.journals.elsevier.com/ifac-papersonlineen_ZA
dc.identifier.citationOlivier, L.E., Maritz, M.G. & Craig, I.K. 2019, 'Deep convolutional neural network for mill feed size characterization', IFAC-PapersOnLine, vol. 54, no. 14, pp. 105-110.en_ZA
dc.identifier.issn1474-6670 (print)
dc.identifier.issn2405-8963 (online)
dc.identifier.other10.1016/j.ifacol.2019.09.172
dc.identifier.urihttp://hdl.handle.net/2263/76055
dc.language.isoenen_ZA
dc.publisherInternational Federation of Automatic Controlen_ZA
dc.rights© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.en_ZA
dc.subjectDeep learningen_ZA
dc.subjectConvolutional neural networken_ZA
dc.subjectTransfer learningen_ZA
dc.subjectMillingen_ZA
dc.subjectRun-of-mine oreen_ZA
dc.titleDeep convolutional neural network for mill feed size characterizationen_ZA
dc.typeArticleen_ZA

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