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Data-Driven Time-Frequency Classification Techniques Applied To Tool-Wear Monitoring (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Gillespie, Bradford W. Atlas, Les E. |
| Description | In many pattern recognition applications features are traditionally extracted from standard time-frequency representations (e.g. the spectrogram) and input to a classifier. This assumes that the implicit smoothing of, say, a spectrogram is appropriate for the classification task. It is better to begin with no implicit smoothing assumptions and optimize the time-frequency representation for each specific classification task. Here we describe two different approaches to data-driven time-frequency classification techniques, one supervised and one unsupervised. We show that a certain class of quadratic time-frequency representations will always provide best classification performance. Using our techniques we explore the wear process of milling cutters. Our initial experiments give strong evidence to the nonlinear nature of the wear process and the importance of capturing nonstationary information about each flute-strike to accurately understand the wear process. |
| File Format | |
| Language | English |
| Publisher Date | 2000-01-01 |
| Publisher Institution | University of Wisconsin |
| Access Restriction | Open |
| Subject Keyword | Initial Experiment Tool-wear Monitoring Many Pattern Recognition Application Feature Implicit Smoothing Classification Task Certain Class Strong Evidence Different Approach Specific Classification Task Classification Performance Data-driven Time-frequency Classification Technique Applied Nonlinear Nature Time-frequency Representation Standard Time-frequency Representation Data-driven Time-frequency Classification Technique Implicit Smoothing Assumption Quadratic Time-frequency Representation Wear Process Nonstationary Information |
| Content Type | Text |
| Resource Type | Article |
