Denoising Method for Partial Discharge Signal of Switchgear Based on Continuous Adaptive Wavelet Threshold

Partial discharge(PD)is an important reason for the insulation failure of the switchgear.In the process of PD detection,PD signal is often annihilated in strong noise.In order to improve the accuracy of PD detection in power plant switchgear,a method based on continuous adaptive wavelet threshold switchgear PD signals denoising is proposed in this paper.By constructing a continuous adaptive threshold function and introducing adjustment parameters,the problems of over⁃processing of traditional hard threshold functions and incomplete denoising of soft threshold functions can be improved.The analysis results of simulated signals and measured signals show that the continuous adaptive wavelet threshold denoising method is significantly better than the traditional denoising method for the PD signal.The proposed method in this paper retains the characteristics of the original signal.Compared with the traditional denoising methods,after denoising the simulated signals,the signal⁃to⁃noise ratio(SNR)is increased by more than 30%,and the root⁃mean⁃square error(RMSE)is reduced by more than 30%.After denoising the real signal,the noise suppression ratio(NRR)is increased by more than 40%.The recognition accuracy rate of PD signal has also been improved to a certain extent,which proves that the method has a certain practicability.

Implementation of Adaptive Wavelet Thresholding Denoising Algorithm Based on DSP

By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function.The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed.The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.

Wavelet-Based Hybrid Thresholding Method for Ultrasonic Liver Image Denoising

This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.

A Limited Deflection Routing Algorithm Based on Burst Loss Threshold in OBS Networks

The deflection routing protocol is an effective contention resolution in Optical Burst Switching network, However, it can worsen loss performance of non-deflected burst on the deflection route. To improve the burst loss performance, a limited deflection routing scheme based on burst loss threshold is proposed to prevent injudicious deflection routing. By using threshold check function, it restrainedly allows the deflected burst to preemptive network resource, consequently, improve the QoS performance of non-deflected burst. Simulation results show that the scheme can efficiently prevent deflected burst contending with non-deflected burst on deflection route, and effectively improve the burst loss performance of entire networks.

Rainbow k-connectivity of Random Bipartite Graphs

A path in an edge-colored graph G is called a rainbow path if no two edges of the path are colored the same color.The minimum number of colors required to color the edges of G such that every pair of vertices are connec ted by at least k internally ver tex-disjoint rainbow paths is called the rainbow k-connectivity of the graph G,denoted by rck(G).For the random graph G(n,p),He and Liang got a sharp threshold function for the property rck(G(n,p))≤d.For the random equi-bipartite graph G(n,n,p),Fujita et.al.got a sharp threshold function for the property rck(G(n,n,p))≤3.They also posed the following problem:For d≥2,determine a sharp threshold function for the property rck(G)≤d,where G is another random graph model.This paper is to give a solution to their problem in the general random bipartite graph model G(m,n,p).

Rainbow and Monochromatic Vertex-connection of Random Graphs

A vertex-colored path P is rainbow if its internal vertices have distinct colors;whereas P is monochromatic if its internal vertices are colored the same.For a vertex-colored connected graph G,the rainbow vertex-connection number rvc(G)is the minimum number of colors used such that there is a rainbow path joining any two vertices of G;whereas the monochromatic vertex-connection number mvc(G)is the maximum number of colors used such that any two vertices of G are connected by a monochromatic path.These two opposite concepts are the vertex-versions of rainbow connection number rc(G)and monochromatic connection number mc(G)respectively.The study on rc(G)and mc(G)of random graphs drew much attention,and there are few results on the rainbow and monochromatic vertex-connection numbers.In this paper,we consider these two vertex-connection numbers of random graphs and establish sharp threshold functions for them,respectively.