Study of the Functions of Wavelet Packet Transform （WPT） and Continues Wavelet Transform （CWT） in Recognizing the Damage Specification

Modem and efficient methods focus on signal analysis and have drawn researchers＇ attention to it in recent years. These methods mainly include Continuous Wavelet and Wavelet Packet transforms. The main advantage of the application of these Wavelets is their capacity to analyze the signal position in different occasions and places. However, in sites with high frequencies its resolution becomes much more difficult. Wavelet packet transform is a more advanced form of continuous wavelets and can make a perfect level by level resolution for each signal. Although very few studies have been done in the field. In order to do this, in the present study, f^st there was an attempt to do a modal analysis on the structure by the ANSYS finite elements software, then using MATLAB, the wavelet was investigated through a continuous wavelet analysis. Finally the results were displayed in 2-D location-coefficient figures. In the second form, transient-dynamic analysis was done on the structure to find out the characteristics of the damage and the wavelet packet energy rate index was suggested. The results indicate that suggested index in the second form is both practical and applicable, and also this index is sensitive to the intensity of the damage.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.

A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform to obtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observation de la Therre ) image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM (Histogram Matched)-based fusion algorithm and WT-based fusion approach.

基于WPT-CNN的复合绝缘子内部缺陷智能识别研究

超声波技术常用于复合绝缘子内部缺陷的检测,但缺陷识别过程依赖于试验人员专业经验。为实现复合绝缘子内部缺陷的智能识别,提出了一种基于小波包变换和卷积神经网络的超声波检测信号识别模型。首先,通过小波包变换对超声波检测信号进行时频特征提取,并将一维信息转化为二维特征矩阵;其次,将二维特征矩阵输入卷积神经网络中,实现对信号特征的智能识别;最后,采用试验信号样本集对模型进行训练与测试。结果表明,提出的模型能对缺陷、气孔、裂纹、界面脱粘和夹杂五类复合绝缘子超声波检测信号进行识别,且平均准确率可达98.7%,能为复合绝缘子内部缺陷的智能识别提供很好的工程应用参考。

基于WPT-IDBO-RELM和WPT-IDMO-RELM模型的日径流预测

为提高日径流时间序列预测精度,改进正则化极限学习机(RELM)的预测性能,对比验证改进蜣螂优化(IDBO)算法和改进侏獴优化(IDMO)算法与其他算法的优化效果,提出了基于小波包变换(WPT)的WPT-IDBO-RELM和WPT-IDMO-RELM日径流时间序列预测模型。对云南省暮底河水库、马鹿塘电站入库日径流进行预测,结果表明WPT-IDBO-RELM和WPT-IDMO-RELM模型对暮底河水库日径流预测的平均绝对百分比误差分别为1.048%、1.015%,对马鹿塘电站日径流预测的平均绝对百分比误差分别为1.493%、1.478%,优于其他对比模型;IDBO、IDMO算法对标准测试函数和实例目标函数的寻优效果均优于其他对比算法,且IDBO、IDMO算法优化效果越好,RELM超参数越优,WPT-IDBO-RELM、WPT-IDMO-RELM模型预测精度越高;WPT可将日径流序列分解为分量更少、规律性更强的子序列分量,在提高预测精度的同时显著降低模型复杂度和计算规模。

基于WPT-ISO-RELM模型的月径流时间序列预测研究

为提高月径流时间序列的预测精度,提升基本蛇群优化(SO)算法搜索能力,同时提升正则化极限学习机(RELM)预测性能,提出了小波包变换(WPT)-改进蛇群优化(ISO)算法-RELM预测模型。首先,利用WPT将月径流时间序列分解为低频分量和高频分量;其次,通过构建8个RELM超参数寻优适应度函数对ISO寻优能力进行检验,并与SO算法、灰狼优化(GWO)算法、变色龙群算法(CSA)、鲸鱼优化算法(WOA)、樽海鞘群体算法(SSA)、侏獴优化算法(DMO)、粒子群优化算法(PSO)的优化结果作对比;最后,建立WPT-ISO-RELM模型,并构建包含WPT-SO-RELM在内的17种模型作对比模型,通过黑河流域莺落峡水文站、讨赖河水文站2个月径流预测实例对各模型进行验证。结果表明:①ISO寻优精度优于SO、GWO、CSA、WOA、SSA、DMO、PSO,通过关键参数的改进,能有效提升ISO的极值寻优能力和平衡能力;②WPT-ISO-RELM模型对莺落峡水文站、讨赖河水文站月径流预测的平均绝对百分比误差分别为0.854%、0.447%,平均绝对误差分别为0.245、0.068 m^(3)/s,纳什效率系数均在0.9999以上,优于其他对比模型,具有更高的预测精度和更好的稳健性;③ISO对于高维和低维问题均具有较好的优化效果,算法寻优能力对提升RELM预测精度十分关键,算法优化性能越强,寻优精度越高,由此获得的RELM超参数越优,所构建的模型预测性能越好。

Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis Using Wavelet Packet Transform and Support Vector Machines

Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.

Damage Detection Methods for Offshore Platforms Based on Wavelet Packet Transform

The wavelet packet transform is used for the damage detection of offshore platforms. When some damage occurs, the dynamic response parameters of the structure will shift subtly. However, in some cases, the dynamic parameters, such as dynamic response, are not sensitive, and it is very difficult to predict the existence of damage. The present paper aims to describe how to find small damage by the use of wavelet packet transform. As the wavelet packet transform can be used to quickly find the singularity of the response signal on different scales, the acceleration signal of a damaged offshore platform in the time domain is transformed through the wavelet packet. Experimental results show that the Daubechies 4 wavelet transform can be used to detect damage.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Motion Classification of EMG Signals Based on Wavelet Packet Transform and LS-SVMs Ensemble

This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.

Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.

基于WPT-ITTA-RELM/ELM/LSSVM模型的日径流预测研究

为提高日径流预测精度,验证改进足球战术算法(ITTA)寻优正则化极限学习机(RELM)、极限学习机(ELM)、最小二乘支持向量机(LSSVM)超参数对日径流预测精度的影响,提出小波包分解(WPT)-ITTA-RELM/ELM/LSSVM时间序列预测模型,并通过德厚大型水库入库日径流预测实例进行验证.首先,利用WPT分解处理日径流时序数据,以获得更具规律的子序列分量;其次,通过典型测试函数和RELM/ELM/LSSVM超参数寻优适应度函数对ITTA寻优能力进行检验,并与基本足球战术算法(TTA)、灰狼优化(GWO)算法、倭黑猩猩优化(BO)算法、黏菌算法(SMA)、鲸鱼优化算法(WOA)的优化结果作对比;最后,建立WPT-ITTA-RELM/ELM/LSSVM模型对实例日径流进行预测,并构建WPT-TTA/GWO/BO/SMA/WOA-RELM、WPT-TTA/GWO/BO/SMA/WOA-ELM、WPT-TTA/GWO/BO/SMA/WOA-LSSVM、WPT-RELM/ELM/LSSVM作对比分析模型.结果表明:对于高维和低维优化问题,ITTA寻优精度均优于TTA、GWO、BO、SMA、WOA,表明通过Levy飞行策略及平衡系数等的改进,可有效提高ITTA全局搜索性能和全局、局部平衡能力.WPT-ITTA-RELM、WPT-ITTA-ELM模型对实例日径流预测的平均绝对百分比误差(E_(MAP))分别为0.521%与0.604%,平均绝对误差(E MA)分别为0.024 m^(3)/s与0.025 m^(3)/s,纳什效率系数(E_(NS))均为0.9992,优于其他对比模型;其中WPT-ITTA-ELM模型运行时间较长,不利于大容量样本的预测研究.对于RELM/ELM超参数高维寻优,ITTA优化效果最好,SMA、TTA次之,GWO、BO、WOA优化效果较差;对于LSSVM超参数低维寻优,由于优化维度低、问题简单,ITTA等6种算法均具有较好的优化效果,但ITTA优化效果最好.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks

In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.