Multi-scale extension of PLS algorithm for advanced on-line process monitoring

Typical process measurements are usually correlated with each other and compounded with various phenomena occurring at different time and frequency domains. To take into account this multivariate and multi-scale nature of process dynamics, a multi-scale PLS (MSPLS) algorithm combining PLS and wavelet analysis is proposed. The MSPLS first decomposes the process measurements into separated multi-scale components using on-line wavelet transform, and then the resultant multi-scale data blocks are modeled in the framework of multi-block PLS algorithm which can describe the global relationships across the entire scale blocks as well as the localized features within each sub-block at detailed resolutions. To demonstrate the feasibility of the MSPLS method, its process monitoring abilities were tested not only for the simulated data sets containing several fault scenarios but also for a real industrial data set, and compared with the monitoring abilities of the standard PLS method on the quantitative basis. The results clearly showed that the MSPLS was superior to the standard PLS for all cases especially in that it could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability.     

A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

In this paper, we present a framework based on a generic representation, which is able to handle most of the radiometric quantities required by global illumination software. A sparse representation in the wavelet space is built using the separation between the directional and the wavelength dependencies of such radiometric quantities. Particularly, we show how to use this representation for spectral power distribution, spectral reflectance and phase function measurements modeling. Then, we explain how the representation is useful for performing spectral rendering. On the one hand, it speeds up spectral path tracing by importance sampling to generate reflected directions and by avoiding expensive computations usually done on-the-fly. On the other hand, it allows efficient spectral photon mapping, both in terms of memory and speed. We also show how complex light emission from real luminaires can be efficiently sampled to emit photons with our numerical model.     

Wavelet analysis to characterize cluster dynamics in a circulating fluidized bed

A common hydrodynamic feature in heavily loaded circulating fluidized beds is the presence of clusters. The continuous formation and destruction of clusters strongly influences particle hold-up, pressure drop, heat transfer at the wall, and mixing. In this paper fiber optic data is analyzed using discrete wavelet analysis to characterize the dynamic behavior of clusters. Five radial positions at three different axial locations under five different operating conditions spanning three different flow regimes were analyzed using discrete wavelets. Results are summarized with respect to cluster size and frequency.     

小波再分配尺度谱在声发射信号特征提取中的应用

在分析典型声发射(Acoustic emission, AE)信号特征的基础上,根据机械故障或损伤引发的AE信号的故障特征提取原理和特点,首次提出AE信号的小波再分配尺度谱分析法.将小波尺度谱和再分配尺度谱同时用于AE信号的特征提取,再分配尺度谱能提高尺度图的聚集性,减少干扰项,更准确地表征AE信号中的特征信息.通过理论研究和仿真,确定了小波再分配尺度谱基函数及其参数的选择,克服了小波再分配尺度谱的时、频分辨率不能同时达到最好的缺陷.将小波再分配尺度谱用于声发射检测的滚动轴承损伤类型及部件的识别,诊断结果十分直观、清晰、准确.仿真分析和试验研究均表明了小波再分配尺度谱能有效应用于基于声发射技术的状态监测和故障诊断.     

基于小波变换的SAR图像相干斑噪声消除方法研究

本文提出了一种基于小波变换的合成孔径雷达(SAR)图像相干斑消除滤波器.这种滤波器通过在小波细节子图像中减少小波分解系数的幅度来抑制相干斑噪声,同时利用小波细节子图像中提供的边缘信息来检测边缘和纹理细节,并保留其对应的小波分解系数值.实验结果表明,此方法除了对相干斑噪声有很好的抑制作用外,还保留了尽可能多的目标特性和图像细节,有着良好的图像视觉解译效果.     

数字通信中基于双正交子波的波形成形

本文讨论数字调制技术中的波形成形问题,提出利用双正交子波实现波形成形的思想.本文首先证明了利用双正交子波实现波形成形满足Nyquist准则;然后研究了利用双正交子波进行基带成形的具体数字化实现涉及的问题,并在频谱利用率和误码性能两个方面进行了对比分析,给出了计算机模拟结果,得到了有益的结论.     

一种复合型自适应数字水印算法

提出一种同时嵌入鲁棒性水印和易损性水印的复合型数字水印算法。其中鲁棒性水印的算法,利用图像小波系数的零树结构,基于零树小波编码,构造出重要系数小波树,将水印信息自适应地以不同强度嵌入载体图像的合适位置。实验结果表明,本算法在用于版权认证的同时还能对篡改进行有效的检测,具有较好的双重保护性能。     

Discrimination of locally stationary time series using wavelets

Time series are sometimes generated by processes that change suddenly from one stationary regime to another, with no intervening periods of transition of any significant duration. A good example of this is provided by seismic data, namely, waveforms of earthquakes and explosions. In order to classify an unknown event as either an earthquake or an explosion, statistical analysts might be helped by having at their disposal an automatic means of identifying, at any time, which pattern prevails. Several authors have proposed methods to tackle this problem by combining the techniques of spectral analysis with those of discriminant analysis. The goal is to develop a discriminant scheme for locally stationary time series such as earthquake and explosion waveforms, by combining the techniques of wavelet analysis with those of discriminant analysis.     

Sparse BEM for potential theory and Stokes flow using variable order wavelets

Wavelets for the discretization of boundary integral operators usually have fixed order and are constructed in some parameter space of the surface. Here a new approach is presented, where the order is flexible and no parameterizations are needed. The wavelets are restrictions of piecewise polynomial functions in three variables on the boundary manifold. This construction is especially suited for surfaces with complicated geometries. If the polynomial order is suitably adjusted to the level of the wavelet, then the non-standard form of a large class of boundary integral operators can be truncated to contain only O(N) non-vanishing entries while retaining the asymptotic convergence of the full Galerkin scheme. An algorithm which sets up the basis and the non-standard form in O(N) operations will be discussed. The method is applied to problems from potential theory and Stokes flow and compared with the Fast Multipole Method.