Characterization of Ore and Bulk Solid Systems by Use of Multivariate Image Analysis and Deep Learning Neural Networks
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...
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| Format: | Thesis |
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Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/92723 |
| _version_ | 1848765656434999296 |
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| author | Fu, Yihao |
| author_facet | Fu, Yihao |
| author_sort | Fu, Yihao |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:38:43Z |
| format | Thesis |
| id | curtin-20.500.11937-92723 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:38:43Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-927232025-07-17T05:11:11Z Characterization of Ore and Bulk Solid Systems by Use of Multivariate Image Analysis and Deep Learning Neural Networks Fu, Yihao 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. 2022 Thesis http://hdl.handle.net/20.500.11937/92723 Curtin University fulltext |
| spellingShingle | Fu, Yihao Characterization of Ore and Bulk Solid Systems by Use of Multivariate Image Analysis and Deep Learning Neural Networks |
| title | Characterization of Ore and Bulk Solid Systems by Use of
Multivariate Image Analysis and Deep Learning Neural Networks |
| title_full | Characterization of Ore and Bulk Solid Systems by Use of
Multivariate Image Analysis and Deep Learning Neural Networks |
| title_fullStr | Characterization of Ore and Bulk Solid Systems by Use of
Multivariate Image Analysis and Deep Learning Neural Networks |
| title_full_unstemmed | Characterization of Ore and Bulk Solid Systems by Use of
Multivariate Image Analysis and Deep Learning Neural Networks |
| title_short | Characterization of Ore and Bulk Solid Systems by Use of
Multivariate Image Analysis and Deep Learning Neural Networks |
| title_sort | characterization of ore and bulk solid systems by use of
multivariate image analysis and deep learning neural networks |
| url | http://hdl.handle.net/20.500.11937/92723 |