Feature-preserving data compression of stamping tonnage information using wavelets

Technometrics

Volumn 41, Issue 4, 1999, Pages 327-339

  Jin, Jionghua ^a   Shi, Jianjun ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Data shrinkage; Feature lossless; Multiresolution analysis; Process monitoring and diagnosis; Signal segmentation; Stamping tonnage information processing

Indexed keywords

ALGORITHMS; FORCE MEASUREMENT; METAL STAMPING; STATISTICAL METHODS; WAVELET TRANSFORMS;

FEATURE PRESERVING DATA COMPRESSION; STAMPING TONNAGE INFORMATION;

DATA COMPRESSION;

EID: 0033276876     PISSN: 00401706     EISSN: 15372723     Source Type: Journal    
DOI: 10.1080/00401706.1999.10485932     Document Type: Article

References (35)

1. ABC (1996), “Press Components’ Characteristic Frequencies,” Technical Report, NZS-102, National Institute of Standards and Technology, Advanced Manufacturing Program.
2. Abramovich, F., and Benjamini, Y. (1995), “Thresholding of Wavelet Coefficients as Multiple Hypotheses Testing Procedure,” in Wavelets and Statistics, Lecture Notes in Statistics, eds. A. Antoniadis and G. Oppenheim, New York: Springer-Verlag, pp. 103, 5-14.
3. Abramovich, F., and Benjamini, Y. (1996), “Adaptive Thresholding of Wavelet Coefficients,” Computational Statistics & Data Analysis, 22, 351-361.
4. Benjamini, Y., and Hochberg, Y. (1995), “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” Journal of the Royal Statistical Society, Ser. B, 57, 289-300.
5. Breiman, L. (1995), “Better Subset Regression Using the Nonnegative Garrote,” Technometrics, 37, 373-384.
6. Burrus, C., Gopinath, R., and Guo, H. (1998), Introduction to Wavelets and Wavelet Transforms: A Primer, Englewood Cliffs, NJ: Prentice-Hall.
7. Calzone, S., Chuang, C. C., and Divakaran, A. (1997), “Video Compression by Mean-Corrected Motion Compensation of Partial Quadtrees,” IEEE Transactions on Circuits and System for Video, 7, 86-96.
8. Chui, C. K. (1992), An Introduction to Wavelets, San Diego, CA: Academic Press.
9. Cohen, A. (1992), “Biorthogonal Wavelets,” in Wavelet: A Tutorial in Theory and Applications, ed. C. K. Chui, San Diego, CA: Academic Press, pp. 123-152.
10. Coifman, R. R., and Wickerhauser, M. V. (1994), “Adapted Waveform Analysis as a Tool for Modeling, Feature Extraction, and Denoising,” Optical Engineering, 33, 2170-2174.
11. Coifman, R. R., and Yale, F. M. (1992), “Adapted Waveform Analysis and Denoising,” in Progress in Wavelet Analysis and Applications, eds. Y. Meyer and S. Roques, Proceedings of the International Conference “Wavelets and Applications,” Toulouse, France, Gif-sur-Yvette, France: Editions Frontieres, pp. 63-93.
12. Daubechies, I. (1992), Ten Lectures on Wavelets, Philadelphia: Society for Industrial and Applied Mathematics.
13. Donoho, D. L. (1995), “De-Noising by Soft-Thresholding,” IEEE Transactions on Information Theory, 41, 613-627.
14. Donoho, D. L., and Johnstone, I. M. (1994), “Ideal Spatial Adaptation via Wavelet Shrinkage,” Biometrika, 81, 425-455.
15. Donoho, D. L., and Johnstone, I. M. (1995), “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of the American Statistical Association, 90, 1200-1224.
16. Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., and Picard, D. (1995), “Wavelet Shrinkage: Asymptopia?” Journal of the Royal Statistical Society, Ser. B, 57, 301-369.
17. Froment, J., and Mallat, S. (1992), “Second Generation Compact Image Coding With Wavelets,” in Wavelet: A Tutorial in Theory and Applications, ed. C. K. Chui, San Diego, CA: Academic Press, pp. 655-679.
18. Fukunaga, K. (1990), Introduction to Statistical Pattern Recognition, New York: Academic Press.
19. Gao, H. Y. (1997), “Wavelet Shrinkage Denoising Using Non-Negative Garrote,” Wavelet Digest, 6, 3, p. 2, preprints, available electronically (http://www. wavelet.org/wavelet/digest-06/digest-06.03html).
20. Jin, J., and Shi, J. (in press), “Diagnostic Feature Extraction From Stamping Tonnage Signal Based on Design of Experiment,” ASME Transactions, Journal of Manufacturing Science and Engineering, 122.
21. Johnstone, I. M., and Silverman, B. W. (1997), “Wavelet Threshold Estimators for Data Correlated Noise,” Journal of the Royal Statistical Society, Ser. B, 59, 319-351.
22. Koh, C. K. H., Shi, J., and Black, J. (1996), “Tonnage Signature Attribute Analysis for Stamping Process,” NAMRI/SME Transactions, 23, 193-198.
23. Koh, C. K. H., Shi, J., and Williams, W. (1995), “Tonnage Signature Analysis Using the Orthogonal (Harr) Transforms,” NAMRI/SME Transactions, 23, 229-234.
24. Lee, J. (1995), “Modern Computer-Aided Maintenance of Manufacturing Equipment and System: Review and Perspective,” Computers & Industrial Engineering, 28, 793-811.
25. Mallat, S. (1989), “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Pattern Analysis and Machine Intelligence, 11, 674-693.
26. Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J. M. (1996), Wavelet Toolbox: For Use with MATLAB, Natick, MA: The MATH Works Inc.
27. Montgomery, D. (1996), Introduction to Statistical Quality Control, New York: Wiley.
28. Nason, G. P. (1995), “Choice of the Threshold Parameter in Wavelet Function Estimation,” in Wavelets and Statistics (Lecture Notes in Statistics), eds. A. Antoniadis and G. Oppenheim, New York: Springer-Verlag, pp. 103, 261-280.
29. Nason, G. P. (1996), “Wavelet Shrinkage Using Cross-Validation,” Journal of the Royal Statistical Society, Ser. B, 58, 463-479.
30. Ogden, R. T., and Parzen, E. (1996), “Change-Point Approach to Data Analytic Wavelet Thresholding,” Statistics and Computing, 6, 93-99.
31. Rao, R., and Bopardikar, A. (1998), Wavelet Transforms: Introduction to Theory and Applications, Reading, MA: Addison-Wesley Longman.
32. th IFAC, IMS '97, Seoul, South Korea, Oxford, U.K.: Elsevier, pp. 213-218.
33. Strang, G., and Nguyen, T. (1996), Wavelets and Filter Banks, Wellesley, MA: Wellesley-Cambridge Press.
34. Vannucci, M., and Corradi, F. (1997), “Some Findings on the Covariance Structure of Wavelet Coefficients: Theory and Models in a Bayesian Perspective,” Wavelet Digest, 6, 2, p. 10, preprints, available electronically (http://www.wavelet.org/wavelet/digest_06/digest_06.02.html).
35. Weyrich, N., and Warhola, G. T. (1995), “De-Noising Using Wavelet and Cross-Validation,” in Approximation Theory, Wavelets and Applications (NATO ASI Ser. C), ed. S. P. Singh, Dordrecht: Kluwer, pp. 454, 523-532.