scholarly journals A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Can He ◽  
Jianchun Xing ◽  
Juelong Li ◽  
Qiliang Yang ◽  
Ronghao Wang
Keyword(s):  
Simulation Experiment ◽  
Simple Calculation ◽  
Signal Denoising ◽  
Determination Method ◽  
Propagation Characteristics ◽  
Correlation Index ◽  
Denoising Method ◽  
Threshold Determination ◽  
New Correlation ◽  
Simulation Results

Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions.

A Denoising Method Based on EEMD and Interval-Thresholding Strategy

2014 ◽  
Vol 902 ◽  
pp. 336-340 ◽  
Author(s):  
Zhi Zhou ◽  
Xing Man Yang ◽  
Gang Chen
Keyword(s):  
Simulation Experiment ◽  
Adaptive Signal Processing ◽  
Basis Functions ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Signal Processing Method ◽  
Adaptive Signal ◽  
Conventional Signal ◽  
Selection Of

As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.

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.

Signal Denoising Based on Slip Threshold Value of Wavelet Packets

2014 ◽  
Vol 556-562 ◽  
pp. 4971-4974
Author(s):  
Can Jun Li ◽  
Qiu Fen Yang ◽  
Shu Ren Zhou ◽  
Zhen Jun Li ◽  
Xian Lin Yang
Keyword(s):  
Wavelet Packet ◽  
Threshold Value ◽  
Noise Signal ◽  
Wavelet Packets ◽  
Signal Denoising ◽  
Wavelet Packet Decomposition ◽  
Denoising Method ◽  
Reconstructed Signal ◽  
Simulation Results ◽  
Decomposition Coefficient

The signal obtained through measurement is inevitably with noise and interference. Traditional signal denoising method often takes the threshold way to process the wavelets or wavelet packets of noise signal for the purpose of denoising. Denoising by making use of slip threshold value of wavelet packets is proposed by this paper, to quantize the slip threshold value of wavelet packet decomposition coefficient, so as to obtain the reconstructed signal denoising. It is shown by the computer simulation results that the slip threshold value is with good practical value.

Analysis of ECG signal denoising method based on S-transform

IRBM ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 362-370 ◽  
Author(s):  
M.K. Das ◽  
S. Ari
Keyword(s):  
Signal Denoising ◽  
Ecg Signal ◽  
Denoising Method ◽  
S Transform

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

Partial discharge signal denoising method based on frequency spectrum clustering and local mean decomposition

2020 ◽  
Vol 14 (10) ◽  
pp. 853-861
Author(s):  
Shanjun Li ◽  
Sashuang Sun ◽  
Qin Shu ◽  
Minwei Chen ◽  
Dakun Zhang ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Partial Discharge ◽  
Signal Denoising ◽  
Denoising Method ◽  
Local Mean Decomposition ◽  
Local Mean

scholarly journals Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 351
Author(s):  
André Berchtold
Keyword(s):  
Markov Chains ◽  
Markov Model ◽  
Em Algorithm ◽  
Hidden Markov Model ◽  
Confidence Intervals ◽  
Hidden Markov ◽  
Simple Calculation ◽  
Standard Methods ◽  
Transition Distribution ◽  
Simulation Results

The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.

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 An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Tan ◽  
Yanping Wang ◽  
Xin Zhou ◽  
Zhongbin Wang ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Processing Method ◽  
Signal Denoising ◽  
Current Signal ◽  
Energy Correlation ◽  
Denoising Method ◽  
Impact Noise ◽  
Key Technologies

In order to solve the problem of industrial sensor signal denoising, an integrated denoising method for sensor mixed noises based on wavelet packet transform and energy-correlation analysis is proposed. The architecture of proposed method is designed and the key technologies, such as wavelet packet transformation, energy-correlation analysis, and processing method of wavelet packet coefficients based on energy-correlation analysis, are presented. Finally, a simulation example for a specific signal and an application of shearer cutting current signal, which mainly contain white Gaussian noise and impact noise, are carried out, and the simulation and application results show that the proposed method is effective and is outperforming others.

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