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dc.contributor.authorFu, Yihao
dc.contributor.supervisorChris Aldrichen_US
dc.date.accessioned2023-07-10T02:21:04Z
dc.date.available2023-07-10T02:21:04Z
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/92723
dc.description.abstract

The development of soft sensor technologies facilitates the characterization and modelling of complex systems in the mining and mineral processing industry. This thesis is aimed to investigate the state-of-the-art convolutional neural networks in the mineral processing and geometallurgy applications such as froth flotation system characterization, drill core recognition, and particle size segmentation. These results outperformed traditional multivariate image analysis methods by a significant margin.

en_US
dc.publisherCurtin Universityen_US
dc.titleCharacterization of Ore and Bulk Solid Systems by Use of Multivariate Image Analysis and Deep Learning Neural Networksen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentWASM: Minerals, Energy and Chemical Engineeringen_US
curtin.accessStatusFulltext not availableen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidFu, Yihao [0000-0003-3379-7194]en_US
dc.date.embargoEnd2025-07-07


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