Deep convolutional neural network for mill feed size characterization

Show simple item record Olivier, Laurentz Eugene Maritz, M.G. (Michael) Craig, Ian Keith 2020-09-07T05:40:41Z 2020-09-07T05:40:41Z 2019
dc.description.abstract Knowing 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.department Electrical, Electronic and Computer Engineering en_ZA
dc.description.librarian am2020 en_ZA
dc.description.sponsorship The National Research Foundation of South Africa en_ZA
dc.description.uri en_ZA
dc.identifier.citation Olivier, 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.issn 1474-6670 (print)
dc.identifier.issn 2405-8963 (online)
dc.identifier.other 10.1016/j.ifacol.2019.09.172
dc.language.iso en en_ZA
dc.publisher International Federation of Automatic Control en_ZA
dc.rights © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. en_ZA
dc.subject Deep learning en_ZA
dc.subject Convolutional neural network en_ZA
dc.subject Transfer learning en_ZA
dc.subject Milling en_ZA
dc.subject Run-of-mine ore en_ZA
dc.title Deep convolutional neural network for mill feed size characterization en_ZA
dc.type Article en_ZA

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