Wavelet Decomposition Process Research Articles

Short-term wind power forecasting method based on spatial-temporal graph neural network

Abstract In order to solve the problem that the weather factor is neglected in the current short-term wind power forecasting process, which leads to a big difference between the power forecasting result and the actual power, a short-term wind power forecasting method based on the spatial-temporal graph neural network is proposed. According to the operating power data, the short-term wind power forecasting sequence is calculated, and the wind farm is regarded as a graph. The dependence of space and time series is captured by a graph neural network, and the spatial-temporal graph neural network model is constructed. Combined with the wavelet decomposition process, short-term wind power forecasting is realized. The experimental results show that the average absolute error and average relative error of this method are less than 10%, and the wind power prediction results at different times are all on the actual wind power curve, which shows that this method can accurately predict short-term wind power.

A multistep forecasting method for online car-hailing demand based on wavelet decomposition and deep Gaussian process regression

A multistep forecasting method for online car-hailing demand based on wavelet decomposition and deep Gaussian process regression

Denoising Speech Based on Deep Learning and Wavelet Decomposition

The work proposed a denoising speech method using deep learning. The predictor and target network signals were the amplitude spectra of the wavelet-decomposition vectors of the noisy audio signal and clean audio signal, respectively. The output of the network was the amplitude spectrum of the denoised signal. Besides, the regression network used the input of the predictor to minimize the mean square error between its output and input targets. The denoised wavelet-decomposition vector was transformed back to the time domain by the output amplitude spectrum and the phase of the wavelet-decomposition vector. Then, the denoised speech was obtained by the inverse wavelet transform. This method overcame the problem that the frequency and time resolution of the short-time Fourier transform could not be adjusted. The noise reduction effect in each frequency band was improved due to the gradual reduction of the noise energy in the wavelet-decomposition process. The experimental results showed that the method has a good denoising effect in the whole frequency band.

Variability Evaluation of Signal in Two-dimensional Wavelet Decomposition Using Fractal Dimension

The paper presents the possibilities of using the fractal dimension to evaluate the signals in wavelet decomposition process. The tests have been carried out on samples produced by face milling process for the six types of materials. It has been shown that the fractal dimension enables characterize signal irregularities in quantitatively and qualitatively way.

An Evaluation of Changeability of Parameters Describing Abbot Curve during a Wavelet Decomposition Process

Results of signals analysis was done using wavelet transform, which have different curves of wavelets and depend on the basic wavelet, which were applied. Thus, it is sometimes not possible to conduct a wavelet transform of a given profile with the use of any basic wavelets and to obtain results that are similar to the measured signal. The aim of this was work was an optimization of a basic wavelet selection used for an analysis of surface roughness. In the work an analysis of Abbot-Firestone curve parameters was performed on subsequent decomposition levels and for various basic wavelets.

Boundary Wavelet Coefficients for Real-Time Detection of Transients Induced by Faults and Power-Quality Disturbances

In the last few years, much research has been focused on wavelet coefficients for high-speed detection of transients induced by faults, high impedance faults (HIFs), and some power-quality (PQ) disturbances. However, the performance of the wavelet coefficient-based analysis in the real-time disturbance detection substantially changes with the choice of the mother wavelet, presents time delay for long mother wavelets, and may fail when the transients are overdamped, which limits the applicability of the conventional wavelet coefficients in actual digital relays. Therefore, in order to overcome all of the aforementioned drawbacks of the wavelet coefficients, this paper proposes a new wavelet decomposition process which provides additional wavelet coefficients with border distortions of a one-cycle sliding window (boundary wavelet coefficients), yielding the real-time detection of the transients scarcely affected by the choice of the mother wavelet, with no time delay for compact and long wavelets, as well as a more reliable disturbance detection than the conventional wavelet coefficients, even in cases of damped transients. In addition, a boundary wavelet coefficient requires equivalent computational burden of a conventional wavelet coefficient. The performance of the proposed boundary wavelet coefficients was assessed through the real-time analysis of records with faults, HIFs, and some PQ disturbances as well as with simulated data from transmission and distribution power systems, taking into account the effects of the fault inception angle, resistance, and distance.

Steganography Technique for JPEG2000 Compressed Images Using Histogram in Wavelet Domain

In this work, a steganography technique using histogram shifting for JPEG2000 compressed images is proposed. Histogram of the wavelet coefficients of each wavelet subband is calculated and shifted to embed secret image data. This embedding is performed on the peak wavelet coefficients during wavelet decomposition process of JPEG2000 encoder using Lifting scheme. Optimal Pixel Adjustment Process (OPAP) is performed on stego images to enrich their visual quality. The results given in this work show that proposed technique provide large embedding capacity and better visual quality of stego images than existing steganography techniques for JPEG2000 compressed images. Extracted secret image using proposed technique is similar to its original secret image.

3차원 오일러 방정식의 계산 효율성 증대를 위한 Adaptive Wavelet 기법의 적용

The adaptive wavelet method is studied for the enhancement of computational efficiency of three-dimensional flows. For implementation of the method for three-dimensional Euler equation, wavelet decomposition process is introduced based on the previous two-dimensional adaptive wavelet method. The order of numerical accuracy of an original solver is preserved by applying modified thresholding value. In order to assess the efficiency of the proposed algorithm, the method is applied to the computation of flow field around ONERA-M6 wing in transonic regime with 4th and 6th order interpolating polynomial respectively. Through the application, it is confirmed that the three-dimensional adaptive wavelet method can reduce the computational time while conserving the numerical accuracy of an original solver.

A WAVELET-BASED PROCEDURE FOR MINING OF PULSE-LIKE GROUND MOTIONS FEATURES IN RESPONSE SPECTRA

The main objective of this paper is to present a wavelet-based procedure to characterize principle features of a special class of motions called pulse-like ground motions. Initially, continues wavelet transform (CWT) which has been known as a powerful technique both in earthquake engineering and seismology field is applied easily in automated detecting of strong pulse of earthquakes. In this procedure, BiorSpline (bior1.3) basis from biorthognal wavelet families is applied for extracting of the largest velocity pulse from normal-fault component of selected records. The selected wavelet decomposition process aids quantification of the effect of extracted pulses on acceleration and displacement response spectra. The results on elastic spectra show that, these pulses cause a significant amplification in the region of pulse period which should be appropriately incorporated in design procedure of structures in near field area. The results also have been presented for inelastic response spectra in different ductility level.

Denoising algorithm based on wavelet adaptive threshold

the main idea of denoising algorithm based on wavelet adaptive threshold is that speech signals should be packet transformed to get the wavelet coefficients used in optimal wavelet. Since the signal and the noise have different relevance, there will be different attenuations in wavelet decomposition process. Based on above characteristics, the appropriate threshold can be calculated by a new threshold function and the minimum mean square algorithm, even if the noise coefficients can be removed and the signal coefficients can be saved. Finally, the retained coefficients can be reconstructed to restore the original signal for the purpose of de-noising.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details. A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy. A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment Signal analysis performed during surface texture measurement frequently involves applying the Fourier transform. The method is particularly useful for assessing roundness and cylindrical profiles. Since the wavelet transform is becoming a common tool for signal analysis in many metrological applications, it is vital to evaluate its suitability for surface texture profiles. The research presented in this paper focused on signal decomposition and reconstruction during roundness profile measurement and the effect of these processes on the changes in selected roundness profile parameters. The calculations were carried out on a sample of 100 roundness profiles for 12 different forms of mother wavelets using MATLAB. The use of Spearman's rank correlation coefficients allowed us to evaluate the relationship between the two chosen criteria for selecting the optimal mother wavelet.

Improved set-partitioning embedded block scheme for still image compression

Shu-Mei Guo and Bing-Hui Wu National Cheng Kung University, Department of Computer Science and Information Engineering, Tainan, Taiwan Jason Sheng-Hong Tsai National Cheng Kung University, Department of Electric Engineering, Tainan, Taiwan In the last few years, wavelet-based still image compression schemes have become one of the most popular fields of research. Several powerful methods of encoding the wavelet coefficients have been proposed and set-partitioning embedded block (SPECK) is one of the most popular techniques. We propose a method called HSPECK (Huffman SPECK), which is based on SPECK to further improve the compression ratio. By observation, we are able to establish the probability distribution over the significance pattern of four subblocks generated successively along the wavelet decomposition process. Therefore, the Huffman coding scheme can then be employed for coding wavelet coefficients. The experimental results show that under the same peak signal-to-noise ratio, the bit rates via the proposed approach improve between 6.1% and 14.9%.

Stereo matching algorithm based on modified wavelet decomposition process

Multiresolutional representation such as pyramidal structures is useful for stereo matching as coarse-to-fine strategy. However, conventional pyramidal structures using Gaussian or Laplacian filters lose much information due to their low-pass filtering characteristics and also cannot obtain any spatial orientation selectivity. The adoption of wavelet transform can remedy these problems, but at the time of image translation, it changes wavelet coefficients. In this paper, a pyramid using modified wavelet decomposition process is proposed to have translation invariance. The image transformed by the proposed method is converted into appropriate multiple features without loss of information. Since the importance of each feature is determined heuristically in the multiple feature-based stereo matching method, it is very difficult to fuse them adequately. In the proposed algorithm, the weight of each feature, that is, the relative importance of each feature, is decided from the similarity between the intensities in the local region of each left and right wavelet channels. Since the window size used for the decision of weight and disparity values greatly influences the processed result, the window is adaptively determined from the disparities estimated in the coarse resolution and low-varying channel of fine resolution. The window size must be large enough to obtain signal-to-noise ratio, but not too large as to induce the effects of projective distortion. Also, a new relaxation algorithm which can reduce false matches without blurring the disparity edge is proposed. By integrating adaptive weight, variable window selection method, and relaxation process, an accurate and stable disparity map is obtained. Experimental results for various images show that the proposed algorithm has good performance even if the image has the unfavorable conditions.