Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
An intelligent simulation methodology to characterize defects in materials
Information Sciences: an International Journal
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
EUROMICRO '03 Proceedings of the 29th Conference on EUROMICRO
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Time sequence data mining using time-frequency analysis and soft computing techniques
Applied Soft Computing
Power quality time series data mining using S-transform and fuzzy expert system
Applied Soft Computing
Hi-index | 0.00 |
Discrete wavelet transform (DWT) coefficients of ultrasonic test signals are considered useful features for input into classifiers due to their effective time-frequency representation of non-stationary signals. However, DWT exhibits a time-variance problem that has resulted in reservations for its wide acceptance. In this paper, a new technique to derive a preprocessing method for time-domain A-scans signal is presented. This technique offers consistent extraction of a segment of the signal from long signals that occur in the non-destructive testing of shafts. Two different classifiers using artificial neural networks and support vector machines are supplied with features generated by our new preprocessing method and their classification performance are compared and evaluated. Their performances are also compared with other alternatives and report the results here. This investigation establishes experimentally that DWT coefficients can be used as a feature extraction scheme more reliably by using our new preprocessing technique.