Full Text:
<3205>
CLC number: TN911.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 5
Clicked: 6787
Method and application of wavelet shrinkage denoising based on genetic algorithm
| |||||||||||||
Ma Qi-ming, Wang Xuan-yin, Du Shuan-ping. Method and application of wavelet shrinkage denoising based on genetic algorithm[J]. Journal of Zhejiang University Science A, 2006, 7(3): 361-367.
@article{title="Method and application of wavelet shrinkage denoising based on genetic algorithm",
author="Ma Qi-ming, Wang Xuan-yin, Du Shuan-ping",
journal="Journal of Zhejiang University Science A",
volume="7",
number="3",
pages="361-367",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0361"
}
%0 Journal Article
%T Method and application of wavelet shrinkage denoising based on genetic algorithm
%A Ma Qi-ming
%A Wang Xuan-yin
%A Du Shuan-ping
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 3
%P 361-367
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0361
TY - JOUR
T1 - Method and application of wavelet shrinkage denoising based on genetic algorithm
A1 - Ma Qi-ming
A1 - Wang Xuan-yin
A1 - Du Shuan-ping
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 3
SP - 361
EP - 367
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0361
Abstract: genetic algorithm (GA) based on wavelet transform threshold shrinkage (WTS) and translation-invariant threshold shrinkage (TIS) is introduced into the method of noise reduction, where parameters used in WTS and TIS, such as wavelet function, decomposition levels, hard or soft threshold and threshold can be selected automatically. This paper ends by comparing two noise reduction methods on the basis of their denoising performances, computation time, etc. The effectiveness of these methods introduced in this paper is validated by the results of analysis of the simulated and real signals.
[1] Chang, C.S., Jin, J., Kumar, S., Su, Q., Hoshino, T., Hanai, M., Kobayashi, N., 2005. Denoising of partial discharge signals in wavelet packets domain. IEE Proceedings of Science, Measurement and Technology, 152(3):129-140.
[2] Donoho, D.L., 1995. De-noise by soft-thresholding. IEEE Trans. on Information Theory, 41(3):613-627.
[3] Hsung, T.C., Lun, D.P.K., Ho, K.C., 2005. Optimizing the multiwavelet shrinkage denoising. IEEE Trans. on Signal Processing, 53(1):240-251.
[4] Jack, L.B., Nandi, A.K., 2002. Fault detection using support vector machines and artificial neural networks augmented by genetic algorithms. Mechanical Systems and Signal Processing, 16(2-3):373-390.
[5] Mallat, S.A., 1989. A theory for multi-resolution signal decompositions: the wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 11(7):674-693.
[6] Mallat, S.A., 2002. A Wavelet Tour of Signal Processing, Translated by Yang, L.H., Dai, D.Q. China Machine Press, Beijing, p.122-165 (in Chinese).
[7] Sun, Y.K., 1998. Wavelet Transform and its Applications. China Machine Press, Beijing, p.219-244 (in Chinese).
[8] Taswell, C., 2000. The what, how, and why of wavelet shrinkage denoising. IEEE Computational Science and Engineering, 3(2):12-19.
[9] Tu, K., Jiang, Y.Y., 2004. Development of Noise Reduction Algorithm for Underwater Signals. IEEE International Symposium on Underwater Technology, p.175-179.
[10] Xiao, M.H., Xue, J.Y., 2003. Research and application of genetic algorithm theory. Computer Engineering, 29(20):137-139 (in Chinese).
[11] Xu, Y., Weaver, J.B., Healy, D.M., Lu, J., 1994. Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans. on Image Processing, 3(6):747-758.
[12] Yang, X.J., Zheng, J.L., 2003. Artificial Neural Network and Blind Signal Processing. Tsinghua Press, Beijing, p.301-324 (in Chinese).
Open peer comments: Debate/Discuss/Question/Opinion
<1>