In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on...In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.展开更多
An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single process...An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.展开更多
Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure t...Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.展开更多
针对旋挖钻机钻杆振动信号具有非线性、非平稳的特点,以及多源振动耦合影响,提出了一种基于多层联合信号降噪方法,对振动信号进行降噪处理。首先,采用局域均值分解(local mean decomposition,LMD),得到一系列乘积函数(product functions...针对旋挖钻机钻杆振动信号具有非线性、非平稳的特点,以及多源振动耦合影响,提出了一种基于多层联合信号降噪方法,对振动信号进行降噪处理。首先,采用局域均值分解(local mean decomposition,LMD),得到一系列乘积函数(product functions,PF),根据计算得出的相关系数,挑选出含噪声成分最多的PF分量,舍弃残余分量,实现第一层降噪;其次,利用小波阈值降噪(wavelet threshold denoising,WTD),对挑选分量实现了第二层降噪;最后,将WTD重构信号设为奇异值分解(singular value decomposition,SVD)的前置处理单元,实现第三层降噪。基于MATLAB仿真实验与轴承数据降噪实验,分别使用EMD-SVD、LMD-SVD两种算法对目标信号进行降噪处理,LMD-WTD-SVD方法可以提高信噪比,并对比波形图与频谱图结果表明,此方法是一种更有效的降噪方法。展开更多
It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on...It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on pulse wave signals. In order to remove these noises, an adaptive de-noising method based on empirical mode decomposition(EMD) and wavelet threshold is proposed in this paper. Compared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.展开更多
Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on b...Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method?is able to recognize the murmur efficiently and superior to BP?neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.展开更多
文摘In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.
基金Supported by the National Natural Science Founda-tion of China (49984001)
文摘An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.
文摘Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.
文摘针对旋挖钻机钻杆振动信号具有非线性、非平稳的特点,以及多源振动耦合影响,提出了一种基于多层联合信号降噪方法,对振动信号进行降噪处理。首先,采用局域均值分解(local mean decomposition,LMD),得到一系列乘积函数(product functions,PF),根据计算得出的相关系数,挑选出含噪声成分最多的PF分量,舍弃残余分量,实现第一层降噪;其次,利用小波阈值降噪(wavelet threshold denoising,WTD),对挑选分量实现了第二层降噪;最后,将WTD重构信号设为奇异值分解(singular value decomposition,SVD)的前置处理单元,实现第三层降噪。基于MATLAB仿真实验与轴承数据降噪实验,分别使用EMD-SVD、LMD-SVD两种算法对目标信号进行降噪处理,LMD-WTD-SVD方法可以提高信噪比,并对比波形图与频谱图结果表明,此方法是一种更有效的降噪方法。
基金The Specialized Research Fund for the Doctoral Program of Higher Educationgrant number:20110042120037+1 种基金Liaoning Provincial Natural Science Foundation of Chinagrant number:201102067
文摘It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on pulse wave signals. In order to remove these noises, an adaptive de-noising method based on empirical mode decomposition(EMD) and wavelet threshold is proposed in this paper. Compared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.
文摘Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method?is able to recognize the murmur efficiently and superior to BP?neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.
