The Study of Signal Denoising for Laser Cutting Based on Adaptive Wavelet Denoising

2012 ◽  
Vol 546-547 ◽  
pp. 686-690
Author(s):  
Hui Juan Hao ◽  
Ji Yong Xu ◽  
Juan Li
Keyword(s):  
Laser Cutting ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Denoising ◽  
Quality Analysis ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Soft Threshold

In order to reduce the noise of acquisition signal in laser cutting, an adaptive wavelet denoising method is proposed in this paper. Based on the analysis of the limitations of traditional threshold method, the particle swarm optimization algorithm is used to select the optimal threshold of wavelet. Compared with the commonly hard and soft threshold method, the experiment results show that the method used in this paper is relatively stable, and can reduce noise excellently. The method can provide more accurate signal for quality analysis in laser cutting .So the method can be used in noise denoising of pulse-induced acoustic sound.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

scholarly journals Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yaonan Tong ◽  
Jingui Li ◽  
Yaohui Xu ◽  
Lichen Cao
Keyword(s):  
Microchip Electrophoresis ◽  
Threshold Function ◽  
Signal Denoising ◽  
Threshold Method ◽  
Decomposition Level ◽  
Denoising Method ◽  
Capacitively Coupled ◽  
Signal Peak ◽  
Soft Threshold ◽  
Simulation Signal

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

scholarly journals DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 215-220
Author(s):  
S. H. Long ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
J. L. Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Signal Characteristics ◽  
Soft Threshold ◽  
Two Parameters ◽  
Unbiased Risk

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

An Improved Wavelet Threshold Denoising Method for Transformer Partial Discharge Signal

2012 ◽  
Vol 214 ◽  
pp. 148-153
Author(s):  
F.C. You ◽  
Y. Zhang
Keyword(s):  
Partial Discharge ◽  
Signal Denoising ◽  
Hard Thresholding ◽  
Threshold Method ◽  
Denoising Method ◽  
Soft Thresholding ◽  
Interference Noise ◽  
Soft Threshold ◽  
Simulation Results ◽  
Thresholding Function

In order to overcome the discontinuance of the hard thresholding function and the defect of slashing singularity more seriously in the soft thresholding function, and improve the denoising effect and detect the transformer partial discharge signal more accurately, this paper puts forward an improved wavelet threshold denoising method through analyzing the interference noise of transformer partial discharge signals and studying various wavelet threshold denoising method, especially the wavelet threshold denoising method that overcomes the shortcomings of the hard and soft threshold. Simulation results show that the denoising effect of the method has been greatly improved than the traditional hard and soft threshold method. This method can be widely used in practical transformer partial discharge signal denoising.

Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Denoising ◽  
Systematic Research ◽  
Gps Data ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method

Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

scholarly journals Optimizing Signal De-Noising Algorithm for Acoustic Emission Leakage of Wavelet

2016 ◽  
Vol 16 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Xin-Hua Wang ◽  
Yu-Lin Jiao ◽  
Yong-Chao Niu ◽  
Jie Yang
Keyword(s):  
High Pressure ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Weight Coefficient ◽  
Wavelet Denoising ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Pressure Pipe ◽  
Optimal Linear ◽  
Mean Square Error Criterion

Abstract Traditional wavelet denoising method cannot eliminate complex high-pressure pipe signals effectively. In the updated wavelet adaptive algorithm, this thesis defines the constraints in order to reconstruct the signals accurately. According to the minimum mean square error criterion, the results predict the weight coefficient and get the optimal linear predictive value. Adopting the improved algorithm under the same condition, this thesis concluded that Db6 increased the complexity of wavelet algorithm by 50% by comparative experiments. It will be more conducive to the realization of hardware and the feasibility of real-time denoising. Dual adaptive wavelet denoising method improved SNR by 50%. This denoising method will play a key role in the detection rate of high-pressure pipe in the online leakage detection system.

Doppler Signal Denoising Based on Feature Adaptive Wavelet Shrinkage Method

2012 ◽  
Vol 532-533 ◽  
pp. 702-707
Author(s):  
Zhao Yin ◽  
Jing Jin ◽  
Yan Wang ◽  
Yi Shen
Keyword(s):  
Circulatory System ◽  
Doppler Signal ◽  
Signal Spectrum ◽  
Wavelet Shrinkage ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Doppler Signals ◽  
Envelope Extraction ◽  
Flow Detection

The envelope extraction of Doppler signal spectrum is very important in ultrasonic blood flow detection, due to the fact that it can provide the diagnosis information of blood circulatory system. Doppler signals are often polluted by noises, which will affect the performance of the envelope extraction. Therefore, it is necessary to remove the noises before extracting the spectrum envelope. In this paper, a Doppler denoising method based on the Feature Adaptive Wavelet Shrinkage is proposed. The advantage of this method is that the threshold of each coefficient is set by using the coefficient at the current location and its two neighbor coefficients. Simulation results demonstrate that the proposed method can remove the noises of Doppler signals more effectively compared to the traditional wavelet threshold method.

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