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Analysis of Tunnel Monitoring Results Based on the Modulus Maxima Method of Wavelet Transform

机译:基于小波变换模数最大法方法的隧道监测结果分析

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In recent years, the theory and method of wavelet analysis is widely used in signal processing, pattern recognition, data compression, image processing, and quantum physics. Compared with modulus maxima, wavelet packet decomposition and coefficient shrinkage de-noising method of wavelet transform, their advantages and disadvantages are analysed and summarized, and their respective scopes are obtained. The Noissin chosen as the original signal with noise is analysed and de-noised by the modulus maxima method of wavelet transform, meanwhile the usage conditions and key computing parameters are also obtained. Finally, the modulus maxima method of wavelet transform are successfully adopted to de-noised the monitoring results of shield tunnel, the data revised are reliably provided for tunnel healthy diagnosis.
机译:近年来,小波分析的理论和方法广泛用于信号处理,模式识别,数据压缩,图像处理和量子物理学中。与模量最大值相比,小波分组分解和系数收缩的小波变换的脱光方法,分析并总结了它们的优点和缺点,并获得各自的范围。通过小波变换的模数最大方法分析并通过小波变换的模数来分析并断开所选择的Noissin,同时也获得了使用条件和关键计算参数。最后,成功采用了小波变换的模量最大化方法来发出屏蔽隧道的监测结果,可靠地为隧道健康诊断提供修订的数据。

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