Wavelet packet denoising and feature extraction for flaw echo signal in ultrasonic testing

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作者：
School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China [1 ]
不详 [2 ]
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来源：
Yi Qi Yi Biao Xue Bao | 2006年 / 1卷 / 94-97+105期
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摘要：
Because of the application of wavelet packet transform in denoising and feature extraction of non-stationary ultrasonic flaw signals, the sort separability criterion and RBF neural network can be respectively used for evaluating the validity of feature classification. Mean threshold is introduced on which wavelet packet denoising is studied. The energy of the frequency domain selected based on wavelet packet decomposition is taken as the feature information. The experimental results over welding flaw signals demonstrate the effectiveness of the proposed schemes.
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