小波分析在虚拟检测系统中的应用研究

针对研究的机电设备状态虚拟检测系统的特点，分析了小波分析在虚拟检测系统中的应用及实现方法，研究其在信号处理过程中的动态性，使小波分析能适应不同特点的机电设备及测试信号的处理要求；提出了基于小波能量的状态检测方法，该检测方法在设备故障检测中得到很好的应用。     

二维小波变换及其在机械碰摩故障诊断中的应用

以机械碰摩信号为例，采用具有“时频局部化”性能和“方向极化”性能的二维小波变换，成功地分辨出故障信号与正常信号，避免了由于多信号转化成单信号带来的信息冗余以及能量的转化，同时能方便地分辨出故障信号的相位角，为解决机械故障诊断领域多信号、多特征的分析问题提出新的思路。     

A Novel Approach Using a Two-Dimensional Wavelet Transform in Ball Grid Array (BGA) Substrate Conducting Path Inspections

This paper presents a novel approach for localising open and short boundary defect candidates on ball grid array (BGA) substrate conducting paths by using a 2D wavelet transform (2D WT). Once the potential defects are identified, traditional printed circuit board (PCB) inspection algorithms can focus on these candidates for further analysis to identify true open and short defects. The defect-detecting scope and the inspection effort are thereby significantly reduced. The binary BGA substrate image is processed. It shows only the boundaries of BGA substrate conducting paths, which are further decomposed directly by 2D WT. Since most of the wavelet energy is clustered at the edges of image objects, the wavelet transform modulus sum (WTMS) of each edge pixel on BGA substrate conducting path boundaries is initially collected. By comparing the WTMS of an edge pixel on decomposition level j with its WTMS on an adjacent decomposition level j + 1, an across-level ratio can be estimated to verify the irregularity of an edge pixel. That is, an edge pixel is classified as strongly irregular (e.g. potential open or short defects) if its across-level ratio reaches a predefined threshold. The proposed approach is template-free and easy to implement, so it is suitable for small-batch production. Real BGA substrates with synthetic boundary defects are used as test samples to evaluate the performance of the proposed approach. Experimental results show that the proposed method is capable of capturing all the open and short defects on BGA substrate conducting paths without missing any errors by using a selected across-level ratio threshold and appropriate decomposition level.     

基于虚拟仪器的轨道机车轮轴故障诊断系统

虚拟仪器相对于传统仪器具有开放性、易用性、性价比高的特点．目前已经在检测系统中得到广泛应用。本文采用NI公司的LabVIEW软件开发平台，设计了一种关于机车轮轴故障的检测系统。     

Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data

Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram （EEG） signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study, a computational method for estimating the spectral exponent based on wavelet transform was examined. A series of Daubeehies wavelet bases with various numbers of vanishing moments were applied to analyze tile self-similar characteristics of intracranial EEG data corresponding to different pathological states of the brain, i.e., ictal and interictal states, in patients with epilepsy. The computational results show that the spectral exponents of intracranial EEG signals obtained during epileptic seizure activity tend to be higher than those obtained during non-seizure periods. This suggests that the intracranial EEG signals obtained during epileptic seizure activity tend to be more self-similar than those obtained during non-seizure periods. The computational results obtained using the wavelet-based approach were validated by comparison with results obtained using the power spectrum method.     

Unique visualization of multiply oriented lattice structures using a continuous wavelet transform

The application of continuous wavelet transformation in the phase field crystal model has yielded excellent results for the crystal lattice orientation and grain boundaries with different misorientation angles [H.M. Singer, I. Singer, Phys. Rev. E 74 (2006) 031103]. However, we show here that the orientation map from this simple method cannot distinguish symmetric orientations using a single convolution template. By introducing additional rotational templates, the grain orientation can be uniquely visualized in two and three dimensions.     

可解释化、结构化、多模态化的深度神经网络

深度学习方法依赖于大规模的标签数据,通过端到端的监督训练,在计算机视觉、自然语言处理领域都取得优异性能.但是,现有方法通常针对单一模态数据,忽视数据的内在结构,缺乏理论支撑.针对上述问题,文中从基于小波核学习的深度滤波器组网络设计、基于结构化学习的深度学习、基于多模态学习的深度学习3个角度阐述结合深度学习方法与小波理论、结构化预测的潜在方法,以及其拓展到多模态数据的可行机制.     

Effective compressed sensing MRI reconstruction via hybrid GSGWO algorithm

In a bio-imaging context, the main issues which obstruct the CS (Compressed sensing) application are image reconstruction time and computational cost. This paper presents an effective compressed sensing-based MRI reconstruction through a hybrid optimization algorithm. Initially, the preprocessing stage is performed using Cross guided bilateral filter. Then the K-space is generated by the Fourier transform. The hybrid Walsh Hadamard Transform and Discrete Wavelet Transform (HWHDWT) is utilized for the compressive sensing of the images. Finally, the Hybrid Galactic Swarm Optimization and Grey Wolf Optimization (HGSGWO) algorithm are developed for MRI reconstruction. The dataset collected from a hospital which contains MRI images both in JPEG and DICOM format. The performance of SSIM (Structural Similarity Index), PSNR (Peak Signal to Noise Ratio), MSE (mean square error) and reconstruction time are evaluated for images and it is compared with the existing methods.     

Linear and synchrosqueezed time–frequency representations revisited: Overview,standards of use,resolution, reconstruction,concentration, and algorithms

Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects of their use, reconsidering some conventions and introducing new concepts and procedures to advance their applicability and value. Furthermore, a detailed numerical and theoretical study of three specific questions is provided, relevant to the application of these methods, namely: the effects of the window/wavelet parameters on the resultant TFR; the relative performance of different approaches for estimating parameters of the components present in the signal from its TFR; and the advantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for calculating (S)WFT and (S)WT, are freely available for download.     

Analysis and validation of wavelet transform based DC fault detection in HVDC system

Fault detection plays an important role in both conventional AC and upcoming DC power systems. This paper aims to study the application of discrete wavelet transform (WT) for detecting the DC fault in the high voltage DC (HVDC) system. The methods of choosing the mother wavelet suited for DC fault is presented, based on degree of correlation to the fault pattern and the time delay. The wavelet analysis is performed on a multi-terminal HVDC system, built in PSCAD/EMTDC software. Its performance is judged for critical parameter like the fault location, resistance and distance. The analysis is further extended to validation using results from experiment, which is obtained from a lab-scale DC hardware setup. Load change, one of the transient disturbances in power system, is carried out to understand the effectiveness of the wavelet transform to differentiate it from the DC fault. The noise in the experimental result gives rise to non-zero wavelet coefficient during the steady-state. This can be improved by removing the unwanted noise using right filter while still retaining the fault-induced transient. The wavelet transform is compared with short-time Fourier transform to highlight the issue with window size and noise.