Deep convolutional neural network for mill feed size characterization

Olivier, Laurentz Eugene; Maritz, M.G. (Michael); Craig, Ian Keith

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dc.contributor.author	Olivier, Laurentz Eugene
dc.contributor.author	Maritz, M.G. (Michael)
dc.contributor.author	Craig, Ian Keith
dc.date.accessioned	2020-09-07T05:40:41Z
dc.date.available	2020-09-07T05:40:41Z
dc.date.issued	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	https://www.journals.elsevier.com/ifac-papersonline	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.identifier.uri	http://hdl.handle.net/2263/76055
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