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dc.contributor.authorBardinas, Jason
dc.contributor.supervisorChris Aldrichen_US
dc.date.accessioned2019-02-11T06:46:30Z
dc.date.available2019-02-11T06:46:30Z
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/73577
dc.description.abstract

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.

en_US
dc.publisherCurtin Universityen_US
dc.titleCharacterisation of Dynamic Process Systems by Use of Recurrence Texture Analysisen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentMinerals Engineering and Extractive Metallurgyen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US


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