Wavelet Transform-based Method Research Articles

A wavelet transform based stationary transformation method for estimating the extreme value of the non-stationary wind speeds

The rational estimation of extreme wind speeds is crucial. As reported, in the Chinese area, the measured wind speed variation from different meteorological observatories shows a general non-stationary trend in the past 40 years due to urbanization factors or temperature variation. Thus, the non-stationary extreme value analysis (TNGEV) of the measured wind speed is required. However, TNGEV is relatively complicated. Under such a background, in this paper, a novel simplified wavelet transform-based stationary transformation method (PNGEV) is proposed for the extreme value analysis of the non-stationary wind speeds. This paper firstly derives the proposed time-varying extreme value distribution parameter form, combining the wavelet transform and moving average. Secondly, a case study is provided to illustrate and verify this proposed method (PNGEV), using the 40-year daily maximum wind speeds recorded from 1981 to 2020 in Baoshan District Shanghai, China. The effectiveness of the proposed method (PNGEV) is verified by comparing it with TNGEV in the return period and the risk of failure. By comparison, it can be figured out that the proposed PNGEV is practicable and outperforms TNGEV in the stability of the return period calculation, as well as the time-varying extreme value distribution establishment.

Suppressing the Zero-Frequency Components in Single Quantitative Phase Imaging by Filtering the Low-Frequency Intrinsic Mode Function Components

In off-axis quantitative phase imaging, existing methods of suppressing the zero-frequency component (ZFC) always cause the loss of high-frequency phase information, thus degrading the accuracy of phase reconstruction. To overcome these problems, this paper proposes to preserve the high-frequency information by filtering the intrinsic mode function. In this method, empirical mode decomposition is employed to decompose the interferometric image into a series of intrinsic mode function (IMF) components from high to low frequencies. The decomposed low-frequency IMF components are processed by Gaussian high-pass filters for ZFC suppression, and the high-frequency IMF components and the filtered low-frequency IMF components are combined to obtain the reconstructed hologram. Hilbert transform is then performed on the reconstructed hologram to filter out the conjugate image, leaving only the original image. In order to verify the performance of our proposed method, the phase maps processed by our proposed method are compared with those processed by the Fourier filtering method, wavelet transform-based method and Laplace operator method. The experimental results show that the proposed method not only suppresses ZFC but also achieves higher accuracy in phase reconstruction.

Experimental study on flow kinematics of dam-break induced surge impacting onto a vertical wall

Tsunami surges are frequently simulated by dam-break flows over dry beds. The purpose of this study is to quantitatively investigate the flow kinematics and turbulent characteristics of a surge impacting onto a vertical wall. To quantify the flow kinematics, the particle image velocimetry technique was used in the non-aerated region, while the bubble image velocimetry technique was employed to measure the impact-induced turbulent flow with air entrainment. The measured velocity fields of the impact-induced splash confirmed the feasibility of Ko and Yeh's [Coastal Eng. 131, 1–11 (2018)] model employing a solid-body motion assumption of splash that estimates the impact force by bores and surges at the initial impact stage. Velocity fields and streamlines revealed that the main water body overturned backward and formed a large two-phase vortex, while a small counter rotating vortex was also formed at the corner of the wall-bed junction. The mean velocity magnitude of the small corner vortex is about two-thirds that of the main water body. The mean turbulent intensity of these aerated regions is about 3.4 times that of the non-aerated regions. Based on a wavelet transform-based method, the result reveals that the mean turbulence length scale of the aerated region is about two-thirds that of the non-aerated region. This study reveals for the first time the quantitative flow field results of the surge impact process, which deepen insight of tsunami risk in coastal engineering, thus improving the accuracy of post-damage prediction in coastal areas.

Dalgacık Dönüşümü Esaslı Yöntem Kullanılarak İki ve Üç Boyutlu Paradigmaların Olay İlişkili Potansiyeller Üzerindeki Etkilerinin İncelenmesi

In event-related P300 potential-based Brain Computer Interface (BCI) systems, the main focus of the studies is how different stimulus types affect system performance. In a study, a data set was created using classical two-dimensional row-column flashing-based and proposed three-dimensional column flashing-based paradigms. According to the results obtained, the proposed three-dimensional column flashing shows high performance in terms of classification accuracy of stimulus presentation. However, how this paradigm changes the P300 potential has not been demonstrated. In this study, the effect of both paradigms on the event-related P300 potential is discussed using a wavelet transform-based method in terms of both time and frequency space. According to the results obtained, it was observed that the proposed paradigm activated more frequency bands on the P300 potential. In addition, using the proposed method, higher P300 amplitude was obtained in many channels. As a result, more effective P300 signals are received in stimulus presentation using the proposed paradigm, increasing the BCI system performance.

Development of Wavelet Transform-Based Image Fusion Technique with Improved PSNR for CT and PET Images in Comparison with Discrete Cosine Transform-Based Image Fusion Technique

The image fusion process is characterized as gathering all relevant data from multiple images and combining it into a smaller number of images, typically only one. The aim of image fusion is to merge data from multiple images of the same scene into a single image that, in theory, contains all of the original images' important features. As a result, the resulting fused image would be better suited to human and machine perception as well as image processing tasks. Materials and methods: In this work, wavelet transform-based image fusion technique was proposed and developed to improve the quality of images. The proposed work is compared with discrete cosine transform based image fusion technique using Peak Signal to Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) values and sample size for each group is 30. The threshold value is set to 0.05 and the confidence interval as 95%. Results: The performance of image fusion of both algorithms are measured by using parameters PSNR and UIQI. High values of PSNR and UIQI indicates the better image fusion. Discrete wavelet transform provides mean value of PSNR (15.8460 dB), mean value of UIQI (0.1966%), and discrete cosine transform provides the mean value of PSNR (15.8783 dB), mean value of UIQI (0.0802%). The insignificant values of PSNR 1 (P=0.894), PSNR 2 (P=0.643), UIQI (P=0.057). Conclusion: Based on the experimental results, it is observed that the discrete wavelet transform-based image fusion method performs better discrete cosine transform.

LVDC Microgrid를 위한 웨이브릿 변환 기반 지락고장 검출

LVDC Microgrid is a small-scale power grid that can increase the stability and efficiency of power supply in a small area, enabling independent operation and increasing the acceptability of DERs (Distributed Energy Resources). However, this is an ungrounded IT (Isolated Terre) grounding system, and even when a ground fault occurs, there is no fault current path and no significant difference compared to the steady state current, so fault detection is very difficult. WT (Wavelet Transform), an easy tool for transient analysis and signal identification, is to be applied to the detection of ground faults in LVDC Microgrid.<BR>In this paper, we propose a Wavelet transform-based ground fault detection method for an ungrounded LVDC Microgrid. By changing the level and order of various MW (Mother Wavelets) using the current collected in the existing pulse injection method, the optimal MW for ground fault detection method is derived. First, an ungrounded LVDC Microgrid is modeled using PSCAD s/w, and ground faults are simulated by varying the fault point and fault resistance. We implement the selected optimal Wavelet transform-based ground fault detection method using Matlab s/w. Finally, performance evaluation is performed through simulation using various collected signals.

Synchrosqueezed wavelet transform-based method for characterizing the dispersive nature of laser-excited surface acoustic waves propagating through the coated or damaged medium

Dispersion of surface acoustic waves (SAWs) can be used for determination of the mechanical parameters of the film or the damaged layer. The correct evaluation of the experimental dispersion curves is important for backcalculation. In this paper, we present a method to identify experimental dispersion curves from two-station time-varying signals based on time–frequency analysis. Synchrosqueezed wavelet transform (SWT) provides the quantitative description of the propagation of different frequency components of SAWs. Discrete phase velocity estimation is accomplished based on the cross-correlation function. Rényi entropy-based SWT spectrum noise reduction and cosine interpolation are applied for improving the quality of dispersion curves. An example is presented to illustrate the application of the proposed method. Finally, laser ultrasonic experiments are performed on the surface of three typical test samples. The comparison of the resulting dispersion curves shows the effectiveness of the proposed method and the necessity of noise reduction and interpolation.

Estimation of Displacement Response in Steel Plate Girder Bridge Using a Single MEMS Accelerometer

Live load-induced deformation is a significant evaluation index for bridge maintenance. To measure a structural displacement response, a method using several MEMS (Micro Electro Mechanical Systems) accelerometers has previously been developed. However, this method requires several sensors, and for improved practicality, a reduction in the number of sensors is desired. Therefore, in this article, a simple displacement monitoring method is proposed that uses only one MEMS accelerometer, which is installed at the longitudinal center of the target bridge. A wavelet transform-based method is applied to the acceleration records to identify the integration range for displacement estimation. The accuracy was evaluated on an actual in-service bridge. The result indicates high accuracy of displacement measurements, with only 5% error when compared with the values recorded by a contact displacement gauge.

Continuous wavelet transform-based method for enhancing estimation of wind turbine blade natural frequencies and damping for machine learning purposes

In the current study, an operational modal analysis is performed on a wind turbine blade with pull-and-release excitation at the tip. The experiments were carried out in large-scale facility of Risoe campus in Technical University of Denmark. Two different blade configurations are compared – blade on the block and a configuration for fatigue testing comprising of blade on the block with mass resonance exciter and seismic masses. A continuous wavelet transform is employed for estimation of modal parameters of the fundamental flapwise bending mode of the blade from acceleration responses. Statistical analysis along with an outlier removal from the extracted modal parameters is carried out to provide clear estimates of the extracted values. These results are compared to the values extracted with other algorithms. The proposed method for modal parameter extraction aims at extraction of numerous observations allowing for statistical decision making and machine learning capabilities.

Relation and Application Method of Deep Learning Sea Target Detection and Segmentation Algorithm

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

A method for editing multi-axis load spectrums based on the wavelet transforms

Abstract A wavelet transform-based spectrum editing method is proposed for deriving effective load spectrums for accelerated durability testing of mechanical components. A modified down-sampling algorithm is also proposed for pre-processing of a measured load spectrum for preserving peaks and valleys in the original spectrum by reassigning the neglected peak/valley samples. The effectiveness of down-sampling and wavelet transform based spectrum editing methods is demonstrated considering multi-axis load spectrum acquired for the handling bushing of an automotive suspension control arm. The results showed with identical pseudo damage values, while the conventional method revealed relative error of 0.4% and 3.3% for down sampling ratios of 2 and 4, respectively. The edited load spectrum obtained from the proposed wavelet transform-based method revealed time duration reduction ratio of about 68%, which was considerably lower than 75% obtained using time-domain editing method. The results also show very good agreements between the proposed spectrum and time-domain editing methods, while the proposed method is considered to be more efficient for accelerated durability tests.

Prediction of gait intention from pre-movement EEG signals: a feasibility study

BackgroundPrediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence.MethodsAn eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between ’walk vs. stop’ and ’rest vs. start’ classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme.ResultsUsing a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for ’rest vs. start’ classification. While for ’walk vs. stop’ classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min.ConclusionExtensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks

Online monitoring systems have been developed for real-time detection of high impedance faults in power distribution networks. Sources of distributed generation are usually ignored in the analyses. Distributed generation imposes great challenges to monitoring systems. This paper proposes a wavelet transform-based feature-extraction method combined with evolving neural networks to detect and locate high impedance faults in time-varying distributed generation systems. Empirically validated IEEE models, simulated in the ATPDraw and Matlab environments, were used to generate data streams containing faulty and normal occurrences. The energy of detail coefficients obtained from different wavelet families such as Symlet, Daubechies, and Biorthogonal are evaluated as feature extraction method. The proposed evolving neural network approach is particularly supplied with a recursive algorithm for learning from online data stream. Online learning allows the neural models to capture novelties and, therefore, deal with nonstationary behavior. This is a unique characteristic of this type of neural network, which differentiate it from other types of neural models. Comparative results considering feed-forward, radial-basis, and recurrent neural networks as well as the proposed hybrid wavelet-evolving neural network approach are shown. The proposed approach has provided encouraging results in terms of accuracy and robustness to changing environment using the energy of detail coefficients of a Symlet-2 wavelet. Robustness to the effect of distributed generation and to transient events is achieved through the ability of the neural model to update parameters, number of hidden neurons, and connection weights recursively. New conditions could be captured on the fly, during the online operation of the system.

A new method for measurement of motor unit action potential duration based on correlation, a pilot study.

We present a new, automatic, correlation-based method for measuring the duration of motor unit action potentials (MUAPs). The method seeks to replicate the way an expert elctromyographer uses his or her eyes, calculating the start and end of the MUAP waveform on the basis of the degree of similarity of non-excluded discharges. We analysed 68 potentials from normal deltoid muscles during slight contraction. For each MUAP, two experienced electromyographers manually determined start and end marker positions, which were used as gold standard duration positions (GSP) in our subsequent tests. The novel method was compared with Nandedkar's method and a wavelet transform-based method. To compare the three methods, the differences between the automatic marker positions and GSPs were statistically evaluated using one-factor ANOVA, the estimated mean square error, and a Chi-square test on the numbers of automatic marker placements with gross errors. All these parameters showed smaller values for the novel method and in most of the cases were statistically significant. In addition, the parameters of the new method were subjected to a sensitivity study, showing its good performance within a range of clinically useful parameter values. The new automatic method determined start and end markers in a more accurate and reliable manner than both of the acknowledged state-of-the art methods used in our comparison study. Graphical abstract The description of a new automatic duration measurement algorithm based on the similarity among discharges of the same MUAP. This method gave better results than the Nandedkar method and a highly regarded wavelet-based method. The new correlation-based method also had the lowest rate of gross aberrant errors in automatic placements.

A wavelet transform-based image segmentation method

In order to retain the details of image and automatically determine the segmentation threshold of image segmentation, an image segmentation method based on wavelet transform is proposed in this paper. The detailed information of horizontal, vertical and diagonal directions can be obtained by using image decomposition based on wavelet transform theory. The proposed method is tested on different images and it has better visual effect than other traditional methods, such as maximum variance segmentation, bi-modal segmentation and histogram valley threshold segmentation. Among the three image quality evaluation indicators, the proposed method also has obvious advantages, especially in medical image segmentation. This will provide a new method for medical image segmentation in the future, and also provide a direction for further research of medical image segmentation methods.

An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy

Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet-visible spectroscopy (UV-Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV-Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.

Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

One of the most promising techniques for condition monitoring of high voltage equipment insulation is partial discharge (PD) measurement using radio frequency (RF) antenna. Nevertheless, the accuracy of monitoring, classification, localization, or lifetime estimation could be negatively affected due to the interferences and noises measured simultaneously and contaminate the RF signals. Therefore, to achieve high accuracy of PD assessment, exploiting the denoising algorithms is inevitable. Hence, this paper seeks to introduce a new technique to suppress white noise, the most prevalent type of noise, especially for RF signals. In the proposed method, the ability of artificial neural network (ANN) in curve fitting is applied to denoising of different types of measured RF signals emitted from PD sources including ‘crack’, ‘internal void’, in the insulator discs and ‘sharp points’ from external hardware. The processes of denoising for named signals with the proposed method are carried out, and the obtained results are compared with the outputs of a wavelet transform-based method named energy conversation-based thresholding. In all tested signals, the proposed technique showed superior denoising capability.

Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1°C to 1.2°C at 40.0°C, 50.0°C and 60.0°C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

Robust optimal sub-band wavelet cepstral coefficient method for speech recognition

The objective of this paper is to propose a robust feature extraction technique for speech recognition system which is insusceptible in the adverse environments. Efficacy of the speech recognition system depends on the feature extraction method. This paper proposes an auditory scale like filter banks using optimal sub-band tree structuring based on wavelet transform. The optimised wavelet filter banks along with energy, logarithmic, discrete cosine transform and cepstral mean normalisation blocks form a robust feature extraction method. This method is validated on a hidden Markov model (HMM)-based single Gaussian isolated word recognition system for additive white Gaussian noise, street and airport noises with different noise levels. Compared with Fourier transform-based methods such as mel-frequency cepstral coefficient (MFCC) and perceptual linear predictive (PLP) methods, the wavelet transform-based method yielded significant improvement across all the noise levels. The experiments also performed with higher dimensions of MFCC features including delta, acceleration features (MFCC_D_A). This study proves that the outcome of wavelet transform-based method gives an increased recognition accuracy of 13% over MFCC_D_A for non-stationary noises.

Possibility for Recognition of Psychic Brain Activity with Continuous Wavelet Analysis of EEG

The brain is a unique organization in nature, possessing the ability for psychic activity, which manifests itself in thoughts, feelings and emotions. In present days, numbers of mathematical methods for analysis of electroencephalogram (EEG) were developed with continuous wavelet transform being one of the most successive approaches for studying of brain activity. This paper is aimed to develop methods for investigation of psychic brain activity with help of continuous wavelet analysis of EEG. Ability of human to realize semantic content of the image presented on the screen was tested. Experiment was accompanied with simultaneous EEG recording, which was held with developed software and PC-based experimental setup. The information capabilities of continuous wavelet transform-based method for EEG analysis were improved for the recognition of specific patterns in human brain activity. Comparative wavelet analysis was carried out for EEG recordings at the moment of awareness of the semantic content of the image and for EEG recordings in the absence of conscious (subjective) perception of the semantic content of the image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Continuous wavelet analysis of EEG showed that the alpha rhythm is the main EEG rhythm, which can be used to estimate the presence of subjective perception of the visual image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Conducted studies allow to conclude that revealing of brain activity related to visual image awareness is possible through analysis of EEG.