Optical Classification Of Quartz Lascas By Artificial Neural Networks

dc.creatorFujiwara
dc.creatorEric; Marques Dos Santos
dc.creatorMurilo Ferreira; Schenkel
dc.creatorEgont Alexandre; Ono
dc.creatorEduardo; Suzuki
dc.creatorCarlos Kenichi
dc.date2015-SEP
dc.date2016-06-07T13:21:35Z
dc.date2016-06-07T13:21:35Z
dc.date.accessioned2018-03-29T01:41:23Z
dc.date.available2018-03-29T01:41:23Z
dc.identifier
dc.identifierOptical Classification Of Quartz Lascas By Artificial Neural Networks. Taylor & Francis Inc, v. 36, p. 281-287 SEP-2015.
dc.identifier0882-7508
dc.identifierWOS:000354545800001
dc.identifier10.1080/08827508.2014.978315
dc.identifierhttp://www.tandfonline.com/doi/pdf/10.1080/08827508.2014.978315
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/243093
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1306791
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionA gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.
dc.description36
dc.description5
dc.description
dc.description281
dc.description287
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description
dc.description
dc.description
dc.languageen
dc.publisherTAYLOR & FRANCIS INC
dc.publisher
dc.publisherPHILADELPHIA
dc.relationMINERAL PROCESSING AND EXTRACTIVE METALLURGY REVIEW
dc.rightsembargo
dc.sourceWOS
dc.subjectMetallurgy & Metallurgical Engineering
dc.subjectMining & Mineral Processing
dc.titleOptical Classification Of Quartz Lascas By Artificial Neural Networks
dc.typeArtículos de revistas


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