Papers by Keyword: Lipschitz Exponent

Abstract: Using a order identification method based on wavelet transform to detect the positions of step disturbances, a adaptive control scheme of disturbance based on the order identification method is designed. The simulation shows that the controller can not only track the set point accurately, but also suppress the step disturbance appeared in different position timely. This control scheme is simple, effective and useful in engineering application.

Abstract: The method for analysis of stationary harmonics in power system is FFT, but it is unsuitable for non-stationary harmonics. Because of the feature that non-stationary harmonics’ frequency spectrum has a certain bandwidth and with some noise interference usually. A new method for detection, based on wavelet packet transform and neural network was presented in this paper. This method improved the traditional wavelet analysis method. The non-stationary harmonics were decomposed in different frequency bands by wavelet packet transform at first, and then complete the analysis of the non-stationary harmonic in different frequency bands. Through software simulation, the analysis results show that, the method has better accuracy, and provided an effective means for analyzing non-stationary harmonics.

Abstract: The paper introduces a novel algorithm to determine the optimal decomposition level in wavelet de-noising. The algorithm selects the optimal decomposition level by comparing the sparsity of wavelet coefficients at adjacent levels. The level whose wavelet coefficient has the maximum sparsity can be confirmed as the optimal decomposition level. We demonstrate experimentally that wavelet de-noising performs better using optimal decomposition level determined by our proposed algorithm than White Noise Test (WNT) method and Maximum Energy (ME) method.

Abstract: According to analyzing the different wavelet coefficients' transmission property of signals and noises under different scales of the wavelet transform, LEFC denoising algorithm based on fuzzy clustering and wavelet transform is proposed. Our experimental evaluations show that the algorithm is effective and robust to restore the images compared with the other wavelet soft-thresholding algorithms. When the ratio exceeds 40 %, LEFC gives superior performance.