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

机译:改进的小波阈值脱光数据处理方法研究变形监测

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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.
机译:由于柔软和硬阈值函数存在缺点。这将降低小波脱模的性能。为了解决这个问题,本文提出了模数方形方法。新方法避免了硬阈值函数的不连续性,并且还降低了软阈值方法的估计小波系数和小波系数之间的固定偏压。仿真结果表明,SNR和MSE优于简单地使用柔软和硬阈值,在变形监测中具有良好的脱气效果。

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