An improved Wavelet Threshold De-noising Data Processing Method Research in Deformation Monitoring

被引：1
|
作者：
Li, Wei [1 ]
Wang, Xu [2 ]
机构：
[1] Univ Sci & Technol, Liaoning Resource & Civil Engn Acad, Hefei 114051, Peoples R China
[2] Bur Non Ferrous Geol, Shenyang 113015, Liaoning, Peoples R China
来源：
ADVANCES IN CIVIL ENGINEERING, PTS 1-4 | 2011年 / 90-93卷
关键词：
deformation monitoring; wavelet analysis; threshold de-noising; the modulus squared model;
D O I：
10.4028/www.scientific.net/AMM.90-93.2858
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Due to the soft and hard threshold function exist shortcomings. This will reduce the performance in wavelet de-noising. in order to solve this problem, This article proposes Modulus square approach. the new approach avoids the discontinuity of the hard threshold function and also decreases the fixed bias between the estimated wavelet coefficients and the wavelet coefficients of the soft-threshold method. Simulation results show that SNR and MSE are better than simply using soft and hard threshold, having good de-noising effect in Deformation Monitoring.
引用
收藏
页码：2858 / +
页数：2
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