Feature-Preserving Data Compression of Stamping Tonnage Information Using Wavelets

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

[1]  I. Johnstone,et al.  Wavelet Threshold Estimators for Data with Correlated Noise , 1997 .

[2]  Jarkko Kari,et al.  Video compression by mean-corrected motion compensation of partial quadtrees , 1997, IEEE Trans. Circuits Syst. Video Technol..

[3]  G. T. Warhola,et al.  DE-NOISING USING WAVELETS AND CROSS VALIDATION , 1995 .

[4]  Hong-Ye Gao,et al.  Wavelet Shrinkage Denoising Using the Non-Negative Garrote , 1998 .

[5]  Jay Lee Modern computer-aided maintenance of manufacturing equipment and systems: review and perspective , 1995 .

[6]  S. Mallat,et al.  Second generation compact image coding with wavelets , 1993 .

[7]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[8]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[9]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[10]  Y. Benjamini,et al.  Adaptive thresholding of wavelet coefficients , 1996 .

[11]  Jionghua Jin,et al.  Diagnostic Feature Extraction From Stamping Tonnage Signals Based on Design of Experiments , 2000 .

[12]  Ronald R. Coifman,et al.  Adapted waveform analysis as a tool for modeling, feature extraction, and denoising , 1994 .

[13]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[14]  Y. Benjamini,et al.  Thresholding of Wavelet Coefficients as Multiple Hypotheses Testing Procedure , 1995 .

[15]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[16]  M. Vannucci,et al.  Covariance structure of wavelet coefficients: theory and models in a Bayesian perspective , 1999 .

[17]  G. Nason Choice of the Threshold Parameter in Wavelet Function Estimation , 1995 .

[18]  Charles K. Chui,et al.  An Introduction to Wavelets , 1992 .

[19]  Albert Cohen,et al.  Biorthogonal wavelets , 1993 .

[20]  L. Breiman Better subset regression using the nonnegative garrote , 1995 .

[21]  G. Nason Wavelet Shrinkage using Cross-validation , 1996 .

[22]  Emanuel Parzen,et al.  Change-point approach to data analytic wavelet thresholding , 1996, Stat. Comput..

[23]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[24]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[25]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Jay Lee,et al.  Research Challenges and Opportunities in Remote Diagnosis and System Performance Assessment , 1997 .

[27]  Ajit S. Bopardikar,et al.  Wavelet transforms - introduction to theory and applications , 1998 .