Application of Wavelet Denoising in Partial Discharge Online Monitoring of Transformer

Because of the strong interferences, such as discrete spectrum interferences (DSI), white noise and pulse-shaped interferences, it is still a difficult work to extract the partial discharge (PD) signal for transformer online monitoring techniques. Through deeply study on the automatic selection of threshold value and mother wavelet, the wavelet-based de-noising method for partial discharge signals is proposed. The analyzing results on simulation signal and field-measured signal indicate that the proposed method is fit for de-noising white noise but for DSI the comparably worse de-noising results is acquired. Hence it is predicted that good de-noising results will be achieved with the wavelet-based de-noising method combined with digital filtering method fitting for de-noising DSI.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

Suppression of Periodic Narrowband Interferences in Cable Partial Discharge Detections

There are background noises and interferences in the signal acquired,due to partial discharge(PD) detection system covers a broad frequency band.To suppress periodic narrowband signal which is a quite serious interference in PD measurement,the existing suppression method is introduced,the new method of wavelet packet transform is mainly studied to de-noise the periodic narrowband in XLPE cable PD detection system,which is based on the db4 basic wavelet using soft threshold.The results of the experiment show that wavelet packet transform is effective in restraining the periodic narrowband interferences to extract the PD pulses in XLPE cable PD monitoring system.     

Audio blind watermarking scheme combined DWT DCT QR

In order to protect the copyright of digital audio and video in Internet, we propose a novel audio blind watermarking scheme combined discrete wavelets transform, discrete cosine transform, QR decomposition and audio characteristics. In this algorithm, the audio are split into blocks, and each block are decomposed on two dimensional discrete wavelet transform (DWT), then the approximate sub band coefficients are decomposed on discrete cosine transform (DCT), and the first quarter of the DCT coefficients are decomposed on QR decomposition and get a triangle matrix. At last, the watermarking information is embedded into the triangle matrix. The experiments show that the algorithm can get better balance between transparency and robustness of watermark, and it has strong robustness against the common audio signal processing such as additive white Gaussian noise, re sampling, re quantization, low pass filter, MP3 compression and cutting replacement.     

Effective Coefficient Threshold for Complex Wavelet Transform

This paper constructs complex wavelet which used for suppressing white noise in PD,and then analyses some disadvantages of several existing wavelet threshold and presents a method of Effective Wavelet Coefficient(EWC) according to the characteristic of modulus maximum of PD signals.Then it is compared with maximin theory threshold selection and Stein unbiased risk estimate theory threshold selection.The findings suggest that the method of EWC is adaptive,and it can suppress noise completely with small distortion of PD signal.     

Multi scale and multi orientation features for image de noising

An adaptive algorithm for image de noising is proposed based on the multi scale and multi orientation features. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. Then thresholds functions are adaptively set with these coefficients. The texture of the image information is introduced by using the mean of decomposition scale and the energy of regional. The greater the energy, the more information of the texture while the same decomposition scales, the smaller the threshold is set. On the contrary, the greater the threshold is set. After the de noising and then reconstruction of these coefficients, image de noising is implemented. Compare to the wavelet transform threshold and contourlet transform threshold, the nonsubsampled contourlet transform pick up the image detail better and improve the quality of the image.     

Study on separation algorithm of convolutive mixing model and separation of mixing signals of multiple partial discharges

To identify individual partial discharges(PD)signals produced by multiple insulation defects in gas insolated switchgear (GIS),this paper analyses the mechanism of propagation and mixing of multiple PD electromagnetic wave signals in GIS cylinder and proposes the convolutive mixing model to describe it for a separation algorithm to acquire individual PD signals. With the non-stationary property,mixing PD signals are changed at time domain into a set consisting of short-time stationary PD signals and then the Molgedey-Schuster decorrelation approach is employed to separate these stationary PD signals at frequency domain. The correlation of the envelope of separated PD signals in this set is used to reconstruct PD signals to realize the separation of non-stationary UHF PD mixtures. The effective separation of actual UHF PD mixing signals validates the assumption of convolutive mixing process in GIS and also offers a new approach to the identification of mixing PD signals by external ultra-high frequency detection scheme from multiple insulation defects in GIS.     

Hilbert-Huang transform and denoising for partial discharge ultrsonic singals of large motor

Empirical mode decomposition (EMD) algorithm is introduded as the core of the Hilbert-huang transform (HHT), and implementation process of EMD is analyzed. Then data compression denoising algorithm based on EMD is proposed, simulation and experimental signals are used for verification of the effect of EMD. In the same data sources, the comparison of data compression denoising approaches based on the EMD, db2 wavelet and db8 wavelet are conducted. In addition, physical experiment of the same analysis and comparisons are conducted on a running motor in a Chongqing electrical plant. Simulation and experimental results show that data compression denoising algorithm based on EMD can achieve the same denoising effect, or even better than based on db2 wavelet, db8 wavelet. The former is more perfect than the latter in the real signal processing, and denoising based on EMD is not loss of the original signal energy.     

The Signals Analysis System Based on Fast Wavelet Transform

The fast wavelet transform (FWT) algorithm in wavelet analysis was introduced in the paper. With quadrature mirror filters (QMF) associated with popular wavelet bases, the fast wavelet decomposition and reconstruction for signals were implemented. Combined with virtual instrument technique, the FWT analysis system for signals was successfully developed. The system can break up signal not only into approximations, which are the high-scale and low-frequency components of the signal, but also into details which are the low-scale and high-frequency components. Especially it can identify singularity signal, which contain some important message of equipment condition and fault, and refine signal from noisy signal, which is corrupted by noise.     

Watermarking algorithm based on statistical property

A novel robust blind watermarking algorithm in DCT domain was proposed. The DCT coefficients are arranged in a specific way, and then the watermark is embedded based on odd even difference for optimal embedding. This algorithm works in a new pattern other than block division and search of the DCT coefficients in the middle frequencies in the traditional pattern, which can overcome the problem in searching suitable coefficients in the middle frequencies for watermark embedding and the small embedding capacity. Experimental results show that the algorithm is simple and good at perceptual transparency as well as robustness against noise and JPEG compression.     

A cooperative localization algorithm for wireless sensor networks

Aiming at the shortcomings of the existing RSS(received signal strength) based localization algorithm for wireless sensor networks (WSN), a cooperative localization algorithm (CLA) is proposed. A reference anchor node is introduced to tolerant some minor error including the node position error. Dixon detection method is applied to remove abnormal RSS values, while the standard deviation threshold of RSS and learning model are introduced to reduce the RSS ranging error and effectively improve the precision. Simulation experiments are performed to evaluate the performance of the proposed algorithm. The results demonstrate that the localization accuracy is improved effectively, while the stability and robustness are better.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

A Medical Image Quantization Method Suited for PACS

Image compression is very important in picture archiving and communication system(PACS). The author studied the statistical distribution of image wavelet subimage coefficients and concluded that the distribution of wavelet subimage coefficients is similar to that of Laplasian distribution. On the other hand, in image reconstruction, the coefficient with different amplitude owns different weight, and different accuracy can be applied to different coefficients according to their different weight. Then, the author has designed a image quantization encoding scheme for PACS. In this scheme, they selected the sample-standard-deviation of coefficients in every subimage as the quantization threshold, and accurately encoded those coefficients with higher weight. Also, this algorithm utilized the visual character of human. The test has proved that the main advantages of this method are the simplicity in computing and predictable encoded coefficients, and a high compression efficiency can obtain too.     

Improvement and performances of DV hop localization algorithm in wireless sensor networks

In order to improve the node localization precision of Range Free based DV Hop localization algorithm in wireless sensor networks (WSNs) with randomly distributed nodes and dynamic topology, the improved algorithm is proposed. After analyzing the DV Hop algorithm, considering the obvious errors of the estimated node coordinates calculated by Polygon based method in traditional DV Hop algorithm, the numerical iterative algorithm is constructed by employing Taylor series expansion, and simulation studies for the improved DV Hop algorithm are conducted. The selection criteria for the convergent threshold of iterative step is determined, the localization performance of the improved localization algorithm is analyzed by comparing with the traditional DV Hop algorithm under the same condition of selected convergent threshold and simulation parameters, while the calculation amount and convergence rate of the improved algorithm are also measured by the statistic iterations. The simulation results show that by selecting reasonable iterative threshold values and appropriately increasing calculation amount for node localization, the improved DV Hop localization algorithm greatly improves the localization precision and the error stability, which is feasible for node localization in WSNs with both randomly distributed nodes and dynamic topology.     

A Practical Method of Eliminating the White Noise of Partial Discharge with Wavelet

The white noise of PD(partial discharge) signal brings great difficult to the PD signal's processing, so eliminating the white noise is a necessary section. There are many methods of eliminating white noise, but none of them are suitable to the PD signal processing. Because PD signal and white noise have different Lipschitz exponents and different wavelet transform features in time-scales, a new eliminating white noise method has been brought forward that has simple operation and meets the timing need of PD signal's processing. After the processing with this method, the PD signal is not distortion and the effect of eliminating white noise is very good. This method can be applied to the processing of PD signal gotten from the site.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

Weighted fusion algorithm for frequency estimation of the short signal with the same frequency and length

For high precise frequency estimation of the short sinusoid signal at low signal-to-noise ratio (SNR), a weighted fusion algorithm for frequency estimation of the short signal with the same frequency and length (SFL-Signal) is proposed. The spectrum model of SFL-Signal and the phase compensation matrix with phase coherent and noise cancellation are constructed. Secondly, the SFL-signal spectrum is weight-fused with the phase compensation matrix to obtain the result almost the same as that of the spectrum of the phase-coherent sinusoid signal. Consequently, high frequency estimation precision is obtained with spectral peak searching of the weight-fusion spectrum. Algorithm analysis and simulation results show that, compared with the existing methods,the proposed algorithm works better in term of precision, calculation complexity, noise immunity, and fits for any type of SFL-Signal.     

Independent component analysis and its application in estimating VEP

Original signals can be effectively estimated from their linear mixed signals by independent component analysis (ICA), even though their frequency components are overlapped, which provides a way to estimate the faint electrophysiological signals. And many researchers who study biomedical signal processing have paid great attention to the technique. We discuss the ICA model and the implement of a fixed-point fast ICA algorithm based on negentropy criterion. With the algorithm, we availably pick up the simulated visual evoked potential (VEP) from the mixing signals.     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

The method for removing the spurious modes of eigensystem realization algorithm

Due to the influence of spurious modes on the eigensystem realization algorithm results,singular value decomposition(SVD) and model energy level are introduced to remove the spurious modes of eigensystem realization algorithm,reduce part of the noise modes and improve the accuracy by reducing measurement noise by SVD. The energy matrix of each mode can be calculated by the selection matrices,the eigenvalues and eigenvectors of the state matrix and the input distribution matrix. The largest singular value of the energy matrix obtained by SVD is a measure for the energy contribution of each mode,which is named mode energy level. Spurious modes resulting from noise or model redundancy are indicated according their mode energy level. A numerical example and an experimental example are presented to demonstrate the efficacy of the method.