A floating threshold quantization algorithm to suppressmixing interferences in partial discharge

Due to the obvious difference of energy distribution frequencies from partial discharge (PD) signal and its mixing interferences (white noise and narrow brand), we uses the characteristic that node decomposition coefficients of wavelet packet transform can effectively show the energy change of signals to build up a floating threshold quantization algorithm (FTQA) varying with the noise energy of PD decomposition coefficients. It makes the node thresholds under the optimal base various with the noise strength in decomposition coefficients to self adaptively reality the choice of optimal threshold to finely partition PD decomposition coefficients. For simulated and real PD signals with mixing interferences, the conditional global threshold quantization algorithm (GTQA) and the proposed floating threshold quantization algorithm are employed to suppress the mixing interferences in PD signals and compared, and the results show that the proposed algorithm has the stronger suppression ability to mixing interference on PD signal and keeps perfect PD waveform via suppression.     

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.     

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.     

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.     

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.     

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.     

Multiresolution Wavelet Transform for Detection of EEG Abnormal Rhythms

The signal of brain activity is a non-stationary random signal including lots of physiology and disease information, which is of important action for doctors to judge pathological changes in brain. So the analysis and process of the EEG signals are always attended. In this paper, the authors take account of the time-frequency localization of wavelet transform and use multiresolution wavelet transform to detect EEG abnormal rhythms. The signals of different scales after EEG signals are transformed by multiresolution wavelet transform not only reflect the frequency information of the signals, namely the more great scale is the lower of the frequency of the signals,but also reflect the time information of the signals, namely EEG state at that time. The test results indicate that the abnormal rhythms of the EEG signals can be detected effectively if right wavelet basis is selected.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

PD Pattern Recognition Using Complex Wavelet Transform Coefficient As The Feature

Complex wavelet transform can characterize the partial feature of the PD signal in time-domain and frequency-domain,and provides the unique phasic information.In this paper,the PD pulse waveforms which are created by 4 typical insulated defects are transformed by complex wavelet,and then the complex wavelet coefficient's real part,imaginary part and compound coefficient are clustered by the Fuzzy c-means,the energy of the cluster is the feature of pattern recognition.Discharge samples are got through large number of experiments,and BPNN can identify the PD created by 4 typical insulated defects effectively.The results show that the feature extracted from compound coefficient is better than the feature extracted form the real part and imaginary part of complex wavelet coefficient or wavelet coefficient.     

Application of Wavelet Transform to De-noising Method

The wavelet transform is a new subject developed quickly in the past ten years. Compared with the Fourier transform, the wavelet transform is a part of time-frequency transform. The most important character is that it can be used to transform a signal into basic units at different scales and location, each unit represents a component of original signal difference from others. The wavelet transform has been proven to be a powerful and efficient tool for processing signal due to this character. This paper introduces the de-noising principles of the wavelet transform. It is proved to be an effective method by the simulating analysis.     

Wavelet-Based Multiresolution Analysis to Noise Elimination

The wavelet transform is a new subject developed quickly in the past ten years Compared with Fourier transform and Gabor transform, the wavelet transform is a part of time-frequency transform, so the message can be obtained from the signals effectively. By means of the fractionized multiresolution analysis to the signals, many problems unalbe to be solved by Fourier tranform have been solved in this way.Based on the fact that the maxima of the noise wavelet transform reduces dramatically with the increase of the scale, we obtain the result that this way is more advanced than the Fourier transform multiresolution analysis to the noise elimination.     

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.     

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.     

Denoise Based On Translation Invariance Wavelet TransformAnd Its Applications

De_noising algorithm based on traditional wavelet transform may produce artifacts on discontinuities of the signal. The reason is that the de_noising algorithm lacks of wavelet translation invariant. This paper proposes a de_noising method based on translation invariant. The method performs the cycle_spinning for the signal to be analyzed. And then, the soft (hard) thresholding is used to shrink the wavelet coefficient of the signal and reconstruct the signal. Consequently, the shift dependence of wavelet basis is eliminated. This method can suppress the artifacts effectively so that de_noised signal is more smooth and has better approximation to original signal.     

A Wavelet Function Generator Using Switched Capacitor Filter

A wavelet function generator making u se of the characteristics of switched capacitor filter is constructed,it can produce anveniently the waveforms of wavelet signals in different scales.A concrete practical scheme is also proposed.The mean square error between practical signal and theoretical signal can be made as small as possible so long as the number of samples is large enough and sample duration is small enough.     

Comparative study on the identification of structural modal parameter using different methods

Chongqing Chaotianmen Yangtze River Bridge is the world's longest-span arch bridge. The node connections on its main truss’s lower chords are to bear millions of large-amplitude stress cycles caused by automobile and light-rail train loads. However, there exists no referential designing and testing experience in such detailing on long-span steel bridges at home and abroad as yet. High-cycle fatigue behavior testing on a detailing model has been conducted to study the fatigue reliability of the bridge’s critical node connections. The testing cycling load on the model is determined according to conventional specifications and the expected traffic flow on the Bridge. The 1/2-scale detailing model is carefully fabricated to simulate the node prototype and undergo the 2 million-cycle testing with the design load. Through testing, fatigue reliability of Chaotianmen Yangtze River Bridge’s main-truss lower-chord node connections during the design service life is verified. In addition, static test data are compared with those calculated by finite element analysis to prove the validity of the node model. In the end, fatigue destruction test of the model was carried out to get the fatigue failure law of the node connection and provide necessary parameters and reference for designing and monitoring of this bridge in the future.     

Application of the Wavelet Transform to the Pulse Signal Analysis of Druggers

According to the randomness of human pulse signals,the multiresolution analysis of the wavelet transform is used to analyze such signals.Its purpose is to extract the abnormal information from the pulse signals of heroin druggers and to obtain the primary judgment criterion which can be used to identify druggers from healthy persons.The scale spectrum based on the wavelet transform of pulse signals carries the important characteristic information of the health situation of human body.The pulse signals of 15 heroin druggers and 15 healthy persons are analyzed and the scale spectrum and the total signal energy of every signal are extracted.It is found that the ratio between the sum(i.e.,scale-wavelet energy) of the scale spectrum in a specific scale-time region and the total signal energy for heroin druggers is generally higher than that of healthy persons.Using the percentage of the ratio between the scale-wavelet energy in the specific scale-time region and the total signal energy as characteristic parameter,a critical parameter is determined that is used to classify heroin druggers and healthy persons.Thus,all of the 15 healthy persons are identified correctly from 30 subjects.Only two heroin persons are misjudged.The experiment results of classification show that the method presented is feasible and effective for detecting the pulse abnormalities of heroin druggers.     

Application of Wavelet & Multi-Resolution Analysis to Detection of the Time of PN Code Acquisition

In the field of CDMA system, DS-SS technology has been used widely. Thereby, a great deal research on acquisition method of PN code is based on DS-SS. In the traditional way, the power detection method of judgment is used widely. Based on the characteristic of PN code acquired signal (namely BPSK signal or QPSK signal) and characteristic of un acquired signal (namely white Gaussian noise), this paper introduces the wavelet detection method of PN code acquisition time. Meanwhile, the performance of wavelet threshold is also studied. In the end, the statistics of parameters in this detection method is made. The result indicates that the wavelet & multi resolution has practical value in the signal processing.     

Speedy Recognition of Noise in Real Time Signal Based on Wavelet Transform

It's necessary to process position and gesture of rocket on real time within the flight of rocket. This work should base exact data. But much noise has been found in the data and the noise recognize is regarded as a focus. A speedy online arithmetic recognizing noise is proposed based on wavelet transform. The computing complexity measured by time of this arithmetic is a constant which is greatly reduces the works of calculation of wavelet transform. It can recognize the noise fast when the signal is gathered. The applications in these problems show that the effective arithmetic satisfies the needs of real time and can handle the real time data measured in other yields.     

Fetal electrocardiogram extraction based on radial basis function neural networks

A novel method for extracting fetal electrocardiogram (FECG) from the abdominal composite signal of a pregnant woman is proposed. The maternal component in the abdominal electrocardiogram (ECG) signal is a nonlinearly transformed version of the mother's ECG (MECG). This nonlinear relationship was identified using radial basis function (RBF) neural networks. The FECG is extracted by subtracting the nonlinearly transformed version of the MECG from the abdominal ECG signal. The baseline shift and noise in the FECG are suppressed by wavelet packet denoising technique. Experimental results obtained from the actual ECG signals demonstrate the effectiveness of the proposed method in extracting FECG even when it is totally embedded within the maternal(QRS) complex.