Papers by Keyword: Wavelet Analysis

Abstract: The model capability of Support Vector Machine (SVM) relies on the selection of kernel function. To obtain a better application modeling of SVM, the wavelet kernel function that satisfies Merce condition is introduced to use the kernel function of SVM, achieving a good effect. In the paper, on the basis of wavelet kernel function, a wavelet derivation kernel function is proposed in the application of SVM for higher accuracy. An actual example on nonlinear function approximation shows that SVM regression model has a satisfactory approximation effect, and also support an effective nonlinear modeling method.

Abstract: Systematic approach for the transmission line positive sequence parameters, temperature, and sag based on wavelet analysis to detect error is developed in this work. Unbiased (random/Gaussian) error such as, transient meter failures, transient meter malfunction, and measurements captured during system transients, are inherently in the form of large abrupt change of short duration in a measurement-sequence. These should be detected before the data is used because their presence will lead to insecure and unstable of power grid. The test results of the proposed method based on data of Sichuan power grid are presented.

Abstract: This paper introduces conductive heat turning method using the high manganese steel as experimental materials.An experimental platform has been established, and vibration signal collected through continuous wavelet transform. By comparing and analyzing different processing parameters and working conditions,the effectiveness of conductive heating turning method’s reducing the vibration in the process of turning the size has been proved.

Abstract: Based on the large number of experimental data analysis and processing, a new inter-turn short circuit transformer diagnostic methods is proposed. In this paper, take the grounding current of transformer core as the signal source. Using wavelet multi-resolution technology the signal wavelet multiscale decomposition, we can get high frequency components of the signal. Achieve short-circuit fault diagnosis between transformer winding turns by comparing the number of the high-frequency component contained in different signals in the decomposition of the same scale.

Abstract: Wavelet analysis technique is proposed in the filter processing to remove the noise from random drift signal of fiber optic gyroscope (FOG). Based on the component elements of random drift signal of FOG, mathematical model of random drift signal of FOG is built. According to the shortcomings of hard threshold and soft threshold, threshold function of forced denoising is used in signal filtering. It is shown from simulation and statistic analysis that the wavelet analysis technique is an effective method to eliminate noise from random drift signal of FOG, and that selection of wavelet base and decomposition scale also has an impact on filtering effect of wavelet analysis.

Abstract: Wavelet analysis method is a promising tool for analysis of gamma-ray spectra due to its time-frequency localization ability and multi-scale resolution feature. This paper describes a method to locate peaks in gamma-ray spectra based on continuous wavelet transform (CWT) of the spectrum using Marr wavelet. The theoretical basis of using Marr wavelet to detect Gaussian peaks was explained. A scale range, 1 to FWHMmax+2, was then suggested to perform CWT. Several novel criteria were used to discriminate real peaks from noise. The detection ability of this method was verified with some measured spectra. The results indicated that Marr wavelet did well in locating gamma-ray peaks.

Abstract: This paper based on the theoretical basis of sampled fiber grating, using transfer matrix method to analyze sampled fiber grating. By changing the parameters of sampled fiber grating and simulating its reflective spectrum. Four optical comb filters based on sampled grating were designed. Then the wavelet de-noising principle was introduced. In order to optimize designing of sampling grating comb filter, a filtering analysis and denoising method by using wavelet analysis to denoise was proposed. Finally, the automatic one-dimensional denoising was used with db6 wavelet. It not only denoise reflective spectrum noise, but also play a certain inhibition in the side-lobe interference the signal received. The smooth reflective spectrum can be got after denoising.

Abstract: The LSQR algorithm is always used to solve the inverse problem of electrical impedance tomography (EIT). However, it always has relatively low reconstruction speed. In this paper, WALSQR (wavelet multi-resolution based Least Square QR-factorization) algorithm is proposed for EIT imaging. With the aid of wavelet transformation, the LSQR solution is obtained in the low-dimension scale space, where important information on the reconstructed image is contained. Hence the computational complexity of reconstruction is reduced without affecting the image quality. In order to verify the effectiveness of the new method, experiments of 2D and 3D EIT imaging are conducted. It lays the foundation for the study of 3D dynamic EIT image reconstruction algorithm.

Abstract: In view of the phenomenon of pile test results are greatly influenced by human , the paper puts forward that to combine wavelet analysis and neural network for pile testing, use the extreme value point of the wavelet analysis as the input of neural networks, depending on the output codes to determine the defect types and position. And it is believed that there is a good potential for use in future.

Abstract: In order to detect and identify the abnormal respiratory of pig and provide real-time warning, in this paper we propose to use machine vision that apply the feature of area operator to detect the porcine respiratory frequency .The structure of the paper is as follows .Firstly the videos of pig standing in the piggery are captured through the use of camera and transmitted to the computer. Then on the MATLAB platform, the changing abdominal area of pig is extracted. Secondly the characteristics of adaptive and multi-resolution analysis of wavelet analysis allow us to remove the burrs of the area signal. Moreover, we use the peak point detection algorithm to acquire pig’s respiratory frequency during the monitoring time, which finally is transformed into breath rate. The experimental results show that the detection accuracy of respiratory frequency is higher than 92% for the abnormal breathing pigs.