Analysis of electrochemical noise data by use of recurrence quantification analysis and machine learning methods
© 2017 By use of recurrence quantification analysis (RQA), twelve features were extracted from the electrochemical noise signals generated by three types of corrosion: uniform, pitting and passivation. Machine learning methods, i.e. linear discriminant analysis (LDA) and random forests (RF), were us...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Pergamon
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/57708 |