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From ISLAB/CAISR
| Recurrent and Deep Learning for Machine Prognostics |
| Keywords | Recurrent Neural Networks, time series forecasting, supervised learning, Prognostics and Health Management, fault detection + |
|---|---|
| Level | Master + |
| OneLineSummary | Construct and optimise Recurrent Neural Networks for industrial applications on machine prognostics; Augmenting industrial data for supervised learning + |
| Prerequisites | Artificial Intelligence and Learning Systems courses; good knowledge in machine learning and neural networks; programming skills for implementing machine learning algorithms + |
| StudentProjectStatus | Finished + |
| Supervisors | Sławomir Nowaczyk + , Sepideh Pashami + , Yuantao Fan + |
| ThesisAuthor | Kunru Chen + |
| TimeFrame | Fall 2017 + |
| Title | Recurrent and Deep Learning for Machine Prognostics + |
| Categories | StudentProject + |
| Modification dateThis property is a special property in this wiki. | 14 October 2018 18:05:33 + |
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