Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis
This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and...
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| Format: | Thesis |
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Curtin University
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/73577 |
| _version_ | 1848763038625169408 |
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| author | Bardinas, Jason |
| author_facet | Bardinas, Jason |
| author_sort | Bardinas, Jason |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches. |
| first_indexed | 2025-11-14T10:57:07Z |
| format | Thesis |
| id | curtin-20.500.11937-73577 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:57:07Z |
| publishDate | 2018 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-735772019-02-11T06:46:30Z Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis Bardinas, Jason This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches. 2018 Thesis http://hdl.handle.net/20.500.11937/73577 Curtin University fulltext |
| spellingShingle | Bardinas, Jason Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title | Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title_full | Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title_fullStr | Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title_full_unstemmed | Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title_short | Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis |
| title_sort | characterisation of dynamic process systems by use of recurrence texture analysis |
| url | http://hdl.handle.net/20.500.11937/73577 |