Wavelet Transform Support Vector Machine Research Articles

Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam

Accurately evaluating hydraulic fracturing (HF) activity characteristics and impact range is critical to optimizing the HF scheme design. This study proposes an adaptive threshold peak detection method and a wavelet transform-support vector machine (WT-SVM) polarity analysis method based on microseismic (MS) monitoring data from a coal mine in Guizhou, China. The results show that the adaptive threshold peak detection method effectively improves the accuracy and stability of the P-wave initial arrival pickup for low signal-to-noise ratio (SNR) signals. The main frequency band of the MS signal of HF is focused on 150–300 Hz. The improved particle swarm optimization algorithm was used by introducing a random-weight strategy to determine the high-density impact areas of the HF within approximately 20 m on both sides of the fracturing borehole. Density cloud map analysis revealed a small area of the blank zone at the junction of the fracturing section, which provided a basis for fracturing parameter design and scheme optimization. The directional characteristics of the MS waveform were quantitatively described by polarity analysis, where the dip angle distribution ranged from 39° to 49°, the strike angle ranged from 61° to 74°, the slip angle was concentrated near 44°, and the stretch angle ranged from 61° to 74°. The WT-SVM proposed in this study overcame the effects of low SNR signals and the limitations of a small-area station network; 62% of the shear ruptures and 38% of the tensile ruptures of the HF were obtained. Shear cracks were the primary expression of the HF rupture type.

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

As the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis.

Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.

To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user’s forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young’s modulus (286 KPa) and exceptional breathability (18 g m−2 h−1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Quantitative Analysis of Near-Infrared Spectroscopy by Combined Stationary Wavelet Transform–Support Vector Machine

ABSTRACT Data preprocessing and multivariate regression methods are two key factors influencing the model prediction ability of near-infrared (NIR) spectroscopy. The present paper evaluated the application of the combined stationary wavelet transform–support vector machine method for developing juice NIR models. The performance of this method has been compared with other methods, such as stand normal variate–partial least squares, stationary wavelet transform–partial least squares, and stand normal variate–stationary wavelet transform methods. The result showed that compared with other methods, the stationary wavelet transform–support vector machine method can provide good quantitative analysis on saccharose concentration in juice.

Short-Term Wind-Power Prediction Based on Wavelet Transform–Support Vector Machine and Statistic-Characteristics Analysis

The prediction algorithm is one of the most important factors in the quality of wind-power prediction. In this paper, based on the principles of wavelet transform and support vector machines (SVMs), as well as the characteristics of wind-turbine generation systems, two prediction methods are presented and discussed. In method 1, the time series of model input are decomposed into different frequency modes, and the models are set up separately based on the SVM theory. The results are combined together to forecast the final wind-power output. For comparison purposes, the wavelet kernel function is applied in place of the radial basis function (RBF) kernel function during SVM training in method 2. The operation data of one wind farm from Texas are used. Mean relative error and relative mean square error are used to evaluate the forecasting errors of the two proposed methods and the RBF SVM model. The means of evaluating the prediction-algorithm precision is also proposed.

Variable Step-Size Wavelet Vector Machine Blind Equalization Algorithm

In order to overcome the contradiction of the CMA with a constant step-size between the convergence rate and the residual mean square error(MSE), on the basis of analyzing the idea of variable step-size, the feature of Support Vector Machine(SVM) and Wavelet Transform, a Variable step-size Wavelet transform Support vector machine Constant Modulus blind equalization Algorithm (VWSCMA) is proposed. In the proposed algorithm, the variable step-size is used to solve the contradiction between the convergence rate and the residual MSE, SVM is employed to optimize the weight vector of equalizer, and wavelet transform is used to reduce the autocorrelation of input signals of equalizer. Simulation results show that the proposed algorithm can effectively overcome the contradiction between the convergence rate and the residual error and has good equalization performance.

Intelligent approaches using support vector machine and extreme learning machine for transmission line protection

This paper proposes two approaches based on wavelet transform-support vector machine (WT-SVM) and wavelet transform-extreme learning machine (WT-ELM) for transmission line protection. These methods uses fault current samples for half cycle from the inception of fault. The features of the line currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and extracted features are applied as inputs to SVM and ELM for faulted phase detection, fault classification, location and discrimination between fault and switching transient condition. The feasibility of the proposed methods have been tested on a 240-kV, 225-km transmission line for all the 10 types of fault using MATLAB Simulink. Upon testing on 9600 fault cases with varying fault resistance, fault inception angle, fault distance, pre-fault power level, and source impedances, the performance of the proposed methods are quite promising. The performance of the proposed methods is compared in terms of classification accuracy and fault location error. The results indicate that SVM based approach is accurate compared to ELM based approach for fault classification. For fault location, the maximum error is less with SVM than ELM and the mean error of SVM is slightly higher than ELM.