Wavelet Multi-Resolution Interpolation Galerkin Method for Linear Singularly Perturbed Boundary Value Problems

In this study,a wavelet multi-resolution interpolation Galerkin method(WMIGM)is proposed to solve linear singularly perturbed boundary value problems.Unlike conventional wavelet schemes,the proposed algorithm can be readily extended to special node generation techniques,such as the Shishkin node.Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients.All the shape functions possess the Kronecker delta property,making the imposition of boundary conditions as easy as that in the finite element method.Four numerical examples are studied to demonstrate the validity and accuracy of the proposedwavelet method.The results showthat the use ofmodified Shishkin nodes can significantly reduce numerical oscillation near the boundary layer.Compared with many other methods,the proposed method possesses satisfactory accuracy and efficiency.The theoretical and numerical results demonstrate that the order of theε-uniform convergence of this wavelet method can reach 5.

NEW TECHNOLOGY FOR FAULT DIAGNOSIS BASED ON WAVELET DENOISING AND MODIFIED EXPONENTIAL TIME-FREQUENCY DISTRIBUTION

Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution ( MED) overcomes the problems of Wigner distribution( WD) ,can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components fmm signal with mighty harmonic components. According to the 'time' behavior, together with 'frequency' behavior in one figure,the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.

MULTI-RESOLUTION MOTION ESTIMATION AND COMPENSATION BASED ON ADJACENT PREDICTION OF FRAME DIFFERENCE IN WAVELET DOMAIN

Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.

Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis

This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.

Wavelet Transform for Image Compression Using Multi-Resolution Analytics: Application to Wireless Sensors Data

The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.

Application of multi-resolution analysis in sonar image denoising

Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.

A KIND OF PAPR REDUCTION METHOD BASED ON PRUNING WPM AND PTS TECHNOLOGY

Wavelet packet multicarrier system gains widespread concern because of its better resistance performance to Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI), as well as the higher spectrum efficiency. However, multicarrier system has a high Peak to Average Power Ratio (PAPR), which will lead to many problems such as lower system performance. In order to solve the problem, a kind of PAPR reduction method based on pruning Wavelet Packet Modulation (WPM) and Partial Transmit Sequences (PTS) technology is proposed in this paper, through proper pruning of the full-tree structure of wavelet packet modulation in the PTS technology to reduce the number of nodes in the system, and finally improve the reduction effect of PAPR. Simulation results show that when Complementary Cumulative Distribution Function (CCDF) is 10 -3 , PTS based on pruning WPM compared with PTS technique and pruning technique has improved about 1 dB and 1.5 dB, which will not affect the system's Bit Error Rate (BER) performance in the wavelet packet multicarrier system.

基于视觉传达技术的激光光斑图像超分辨率重建方法

激光光斑图像在成像过程中易受到成像条件和成像方式的限制,导致激光光斑图像的分辨率比较低,难以满足实际需求。为此,提出基于视觉传达技术的激光光斑图像超分辨率重建方法。采用视觉传达技术采集激光光斑图像,并使用双树复小波阈值方法对激光光斑图像去噪处理,通过改进稠密神经网络提取激光光斑图像特征,基于奇异值分解方法降低字典中原子的数目,改进稀疏表达正则化方法,实现激光光斑图像的超分辨率重建。实验结果表明,所提方法的低分辨率图像重建结果与原始图像更加接近,重建图像的结构相似度均在0.9以上,证明该方法的重建效果好、更适合实际应用。

基于空间变换技术的脉冲激光声信号检测

针对目前脉冲激光声信号检测方法极易受到环境噪声的影响,导致检测精确度不理想的问题。提出基于空间变换技术的脉冲激光声信号检测方法。首先,采用小波阈值法去除脉冲激光声信号中的噪声;其次,利用球傅里叶空间变换技术对声信号中的声源展开定位,获取声信号的准确位置;最后,分别提取声信号中的比值特征、比重特征、频率特征三种参数,在此基础上通过SVM算法对其展开分类识别,以此实现脉冲激光声信号的有效检测。实验结果表明,所提方法的声信号检测精度超过96%。

基于多智能体技术的舰船电缆绝缘故障检测

为了快速、有效地定位故障点,提出了一种基于多智能体技术的电缆绝缘故障检测方法。搭建多智能体故障检测框架,采用小波方法分析每个分量,并处理小波分解后的故障信号,实现绝缘故障信号变分模态分解。将多智能体视为多个节点,分析多智能体采集温度信号和电流信号,建立等效电路方程,检测电缆绝缘状态。对比实验显示,该方法绝缘附近温度与实际温度偏差较小,能够为船用机电设备安全稳定运行提供实时监测,具有较高的应用价值。

DT-CWT-LMS自适应降噪模型构建及其在隧道监测工程中的应用

针对隧道工程监测现场应用布里渊光时域反射仪(BOTDR)系统存在的问题,提出基于双树复小波变换(DT-CWT)和改进的LMS算法的组合降噪模型,用于对BOTDR分布式光纤监测信号进行降噪处理。首先,基于双树复小波变换算法分解原始信号;然后,使用样本熵作为目标函数自动选择最优小波分解层数的模型,并基于优化双曲余弦函数改进LMS算法的收敛速度和收敛性;最后,为验证所提出算法的有效性,进行BOTDR温度信号降噪试验。试验结果表明:1)DT-CWT-LMS算法的降噪效果明显优于传统的小波阈值降噪方法,在6个温度梯度上的平均SNR值比WDD、EMT、EMD分别高出43.98%、17.5%、8.4%,平均RMSE值分别降低33.18%、17.14%、9.23%,平均SE值分别降低29.04%、21.17%、20.67%。为验证所提算法在工程现场的有效性,依托北京地铁隧道监测项目,使用DT-CWT-LMS算法对光纤监测信号进行降噪研究,降噪后信号的样本熵平均降低幅为64.03%;2)和常规WDD、EMT、EMD 3种方法相比,DT-CWT-LMS算法在3条光纤上的表现均优于其他3种算法,在1号、2号、3号光纤上的SNR指标比其他3种算法的平均值分别高出22%、38%、27%,说明该算法可作为隧道工程光纤监测信号的一种有效降噪方法。

基于频域解离特征的OCT指纹表征攻击检测

在自动指纹识别系统中,指纹防伪能力的发展至关重要.传统指纹一般由表面成像得到,而这种表面的纹理特征极容易被盗取.基于这种传统指纹的识别系统,检测指纹表征攻击的能力十分有限.因此,现有研究普遍针对具有防伪特征的指纹模态,如具有汗腺特征的高精度指纹和具有指静脉特征的指纹开发表征攻击检测算法.在本篇工作中,为了进一步提高指纹系统的表征攻击检测能力,我们提出一种基于光学相干断层扫描技术(Optical Coherence Tomography,OCT)的频域指纹表征攻击检测方法.与以往方法不同,我们首先利用卷积神经网络和残差结构设计了一个频域特征解离模型,通过该模型可以分别解离出时域中叠加在原始OCT指纹图像上的信息(如区分性特征、无效特征和冗余特征).然后,我们让它学习不同的频域编码,并结合OCT指纹在时域中的重构编码形成相应的潜层编码.利用潜层编码,我们设计了一种用于区分表征攻击指纹和真实指纹的预测模型,用于表征攻击检测.在目前常用的OCT指纹数据集上的实验结果表明,我们的方法可以通过在频域中分离出一些叠加在时域中的无用干扰信息,从而有效地消除干扰.在实例方面,该方法的最小误差(Err.)为0.67%,与已有的基于时域的最优方法相比,最小误差降低了3.03%,性能提高了81.89%.

基于机器视觉技术的新能源汽车零部件表面缺陷检测

新能源汽车零部件表面缺陷种类繁多,依靠人工检测存在错检、漏检等问题。为了提高新能源汽车零部件表面缺陷精度,提出基于机器视觉技术的新能源汽车零部件表面缺陷检测方法。结合Ridegelet变换与小波变换,在不破坏图像细节前提下,对基于机器视觉技术采集的新能源汽车零部件表面缺陷图像展开去噪处理。引入MSR算法,通过信息熵占比权值的自适应计算,实现图像的快速增强。利用数学形态学计算方法,对新能源汽车零部件图像中的缺陷主体展开边缘提取,进而实现新能源汽车零部件表面缺陷的检测。实验结果表明,所提方法检测耗时低于10 ms、缺陷位置分析准确,面对多类型新能源汽车零部件表面缺陷,能够做到精准识别,提高了新能源汽车零部件的缺陷检测效率。

全息激光投影退化图像相位恢复技术研究

全息激光投影退化图像包含噪声、光照不均匀等多种因素,为了优化图像质量,提高图像清晰度,提出全息激光投影退化图像相位恢复技术。首先,采用小波阈值方法去除含噪的全息激光投影退化图像中的噪声;其次,结合Retinex算法与高斯低通滤波器估计全息激光投影退化图像的入射分量,采用分区的局部色条映射算法结合入射分量增强全息激光投影退化图像;最后,基于卡通-纹理模型的相位恢复方法展开退化图像修复,实现全息激光投影退化图像修复。实验结果表明,所提方法图像具有较好的去噪效果和增强效果,同时,相位恢复精度高于97.0%,SSIM值达到0.9,MSE值低于0.9%,峰值信噪比高于7.14 dB,具有较优的全息激光投影退化图像相位恢复效果。

基于小波分析的电力多级冗余数据校验系统

针对电力多级数据体量的增大,冗余数据量随之增大,造成电力大数据应用质量降低的问题,设计基于小波分析的电力多级冗余数据校验系统。预处理电力多级数据后,引入小波分析算法分解处理电力多级数据,提取最大权重系数对应分解部分数据的代表特征。基于流水线策略检测电力多级冗余数据,并通过并行化技术对其进行消除处理,从而实现电力多级冗余数据的校验。实验数据显示,应用设计系统后,冗余数据查全率最大值达到了97.56%,冗余数据校验误差最小值为2%,证实了设计系统具有较好的冗余数据校验效果。

数字技术支持下的海量图像信息多云存储平台设计

为降低海量图像的存储内存占用率,并保证图像的存储安全性,设计数字技术支持下的海量图像信息多云存储平台。该平台包括Google Cloud、Amazon S3、Microsoft Azure等多个云服务,形成多云架构;通过客户端控制高性能的本地服务器,实现本地服务器和企业云存储服务之间的云通信;本地应用模块利用数字水印技术加密海量图像信息后,结合小波变换算法压缩该图像后,通过该通信方式上传压缩加密后海量图像信息至多云存储架构中。测试结果显示,该平台数据流标准方差均在0.27以下,内存占用率在22.6%以下,多云服务器的负载均衡度结果均在94.1%以上,能够保证图像质量不会发生损坏。

声发射技术在水轮机叶片裂纹监测和诊断中的应用

水轮机转轮叶片裂纹严重影响电站运行的安全稳定性。然而,裂纹所产生的噪声信号十分微弱,在实际运行时易被强背景噪声淹没。为了更好地监测叶片裂纹产生的噪声信号,采用声发射(AE)技术进行信号采集监测,同时使用小波阈值降噪方法对信号进行提取分析。研究结果表明:在水轮机停机时,声发射技术监测信号的幅值范围为10~60 mV的断铅信号;AE信号的主要频率在64~128 kHz,噪声信号主要集中在第4和第5个小波系数中;采用AE参数可以准确定位产生裂纹的叶片;当传感器安装在转轮叶片正上方时,AE信号接收所需的距离和时间最短,提取到的结果更精确;采用AE技术可以及时发现叶片裂纹的初期状况,便于尽早采取合理的加固与补修措施。声发射监测技术对电站运行的安全稳定性具有重要的现实意义和经济价值。

基于第二代小波变换技术和双向脉冲交互的架空线路缺陷检测及定位方法

针对传统架空线路缺陷检测方法存在的不足,本文提出了一种基于第二代小波变换技术和双向脉冲交互的架空线路缺陷检测及定位方法,通过第二代小波变换技术克服了原有噪声信号对信号处理及分析的干扰,大幅度提高了检测效率。双向脉冲交互技术通过双边信号中脉冲的极性,可以准确定位缺陷发生的位置,大大提高了缺陷定位的精度。本方法原理简单,实施方便,具有较高的推广前景,为进一步提高供电可靠性、提升人民群众用电“获得感”“幸福感”提供了坚强保障。

基于深度融合模型的气膜密封端面状态识别方法

气膜密封装置是工业领域应用广泛的一种密封技术,其可靠的密封性能对于设备正常运行至关重要。气膜密封装置的动静密封环接触端面相对运动会产生摩擦,摩擦过程会产生复杂的声发射信号,这些信号往往隐含密封端面运行状况的重要信息。采用传统的方法往往难以准确识别和分类这些微弱的特征信号,因此需要开发更高精度的故障诊断方法。针对机械密封动、静环端面摩擦状态难以识别这一问题,以气膜密封装置为研究对象,提出了一种基于深度融合模型的气膜密封端面状态识别方法。首先,采用声发射传感器及采集设备,对密封端面的声发射信号进行了采集;其次,利用小波包变换方法对采集到的信号进行了滤波处理,并提取了时域和频域的微弱特征;然后,将深度随机森林(DRF)作为分类层融入卷积神经网络(CNN)形成了融合模型,对预先处理过的密封装置运行状态的特征信息进行了识别和分类;最后,根据实验的泄漏量,使用混淆矩阵和受试者工作曲线分析了两种模型的特征提取能力。研究结果表明:CNN-DRF融合模型对于密封端面声发射信号的两种特征识别精度分别为96%和98%,与传统的CNN模型相比,其可以充分提取信号特征信息,具有更出色的故障诊断能力。