Fault detection using multiscale PCA-based moving window GLRT

The presence of measurement errors (noise) in the data and mode l uncertainties degrade the performance quality of fault detection (FD) techniques. Therefore, an objective of this paper is to enhance the quality of FD by suppressing the effect of these errors using wavelet-based multiscale representation of data, which is a powerful feature extraction tool. Multiscale representation of data has been used to improve the FD abilities of principal component analysis. Thus, combining the advantages of multiscale representation with those of hypothesis testing should provide further improvements in FD. To do that, a moving window generalized likelihood ratio test (MW-GLRT) method based on multiscale principal component analysis (MSPCA) is proposed for FD. The dynamical multiscale representation is proposed to extract the deterministic features and decorrelate autocorrelated measurements. An extension of the popular hypothesis testing GLRT method is applied on the residuals from the MSPCA model, in order to further enhance the fault detection performance. In the proposed MW-GLRT method, the detection statistic equals the norm of the residuals in that window, which is equivalent to applying a mean filter on the squares of the residuals. This means that a proper moving window length needs to be selected, which is similar to estimating the mean filter length in data filtering. The fault detection performance of the MSPCA-based MW-GLRT chart is illustrated through two examples, one using synthetic data, and the other using simulated Tennessee Eastman Process (TEP) data. The results demonstrate the effectiveness of the MSPCA-based MW-GLRT method over the conventional PCA-based and MSPCA-based GLRT methods, and both of them provide better performance results when compared with the conventional PCA and MSPCA methods, through their respective charts T² and Q charts.     

Multiscale fuzzy Kalman filtering

Measured data are usually contaminated with errors which sometimes mask their important features. Therefore, data filtering is needed for effective utilization of such measurements. For nonlinear systems which can be described by a Takagi–Sugeno (TS) fuzzy model, several fuzzy Kalman (FK) filtering algorithms have been developed to extend Kalman filtering to such systems. Also, multiscale representation of data is a powerful data analysis tool, which has been successfully used to solve several data filtering problems. In this paper, a multiscale fuzzy Kalman (MSFK) filtering algorithm, in which multiscale representation is utilized to improve the performance of fuzzy Kalman filtering, is developed. The idea is to apply FK filtering at multiple scales to combine the advantages of the FK filter with those of the low pass filters used in multiscale data representation. Starting with a fuzzy model in the time domain, a similar fuzzy model is derived at each scale using the scaled signal approximation of the data obtained by stationary wavelet transform (SWT). These multiscale fuzzy models are then used in FK filtering, and the FK filter with the least cross validation mean square error among all scales is selected as the optimum filter. Also, theoretically, it has been shown that applying FK filtering at a coarser scale than the time domain is equivalent to using a time-averaged FK filter. Finally, the performance of the developed MSFK filtering algorithm is illustrated through a simulated example.     

SAR图像无监督分割的空间变化混合MAR模型方法

提出SAR（Synthetic Aperture Radar）图像的空间变化混合多尺度自回归（Spatially Variant Mixture Multiscale Auto Regressive,SVMMAR）模型方法,该模型不仅能刻画SAR图像的空间变化性,而且利用了SAR图像多尺度序列的统计特性;采用的分类器是像素标号的极大似然估计,细化的同时简化了传统Bayes分类器;该模型无需预先抑制斑点噪声,就能获得精确分割结果;并且理沧上证明了在图像粗尺度确定分类个数的合理性,在此基础上提出一种在粗尺度确定分类个数的新方法,大大减少了运算量。     

空洞卷积的多尺度语义分割网络

计算机硬件的发展极大程度地促进了计算机视觉的发展,卷积神经网络在语义分割中取得了令人瞩目的成就,但多卷积层叠加难免造成图像中目标边界信息的丢失。为了尽可能保留边界信息,提高图像分割精度,提出一种多尺度空洞卷积神经网络模型。该模型利用多尺度池化适应图像中不同尺度目标,并利用空洞卷积学习目标特征,在更加准确识别目标的同时,提高目标边界的识别精度,在ISPRS Vaihingen数据集上的实验结果表明,提出的多尺度空洞卷积神经网络对于目标边界的拟合结果较为理想。     

Improving the prediction and parsimony of ARX models using multiscale estimation

Multiscale wavelet-based data representation has been shown to be a powerful data analysis tool in various applications. In this paper, the advantages of multiscale representation are utilized to improve the prediction accuracy and parsimony of the auto-regressive with exogenous variable (ARX) model by developing a multiscale ARX (MSARX) modeling algorithm. The idea is to decompose the input–output data at multiple scales, construct an ARX model at each scale using the scaled signal approximations of the data, and then using cross validation, select among all MSARX models the one which best predicts the process response. The MSARX algorithm is shown to improve the parsimony of the estimated models, as ARX models with a fewer number of coefficients are needed at coarser scales. This advantage is attributed to the down-sampling used in multiscale representation. Another important advantage of the MSARX algorithm is that it inherently accounts for the presence of measurement noise through the application of low-pass filters in the multiscale decomposition of the data, which in turn improves the model robustness to measurement noise and enhances its prediction. These prediction and parsimony advantages of MSARX modeling are demonstrated through a simulated second order example, in which the MSARX algorithm outperformed the time-domain one at different noise contents, and the relative improvement of MSARX increased at higher levels of noise.     

Multiscale Active Contours

We propose a new multiscale image segmentation model, based on the active contour/snake model and the Polyakov action. The concept of scale, general issue in physics and signal processing, is introduced in the active contour model, which is a well-known image segmentation model that consists of evolving a contour in images toward the boundaries of objects. The Polyakov action, introduced in image processing by Sochen-Kimmel-Malladi in Sochen et al. (1998), provides an efficient mathematical framework to define a multiscale segmentation model because it generalizes the concept of harmonic maps embedded in higher-dimensional Riemannian manifolds such as multiscale images. Our multiscale segmentation model, unlike classical multiscale segmentations which work scale by scale to speed up the segmentation process, uses all scales simultaneously, i.e. the whole scale space, to introduce the geometry of multiscale images in the segmentation process. The extracted multiscale structures will be useful to efficiently improve the robustness and the performance of standard shape analysis techniques such as shape recognition and shape registration. Another advantage of our method is to use not only the Gaussian scale space but also many other multiscale spaces such as the Perona-Malik scale space, the curvature scale space or the Beltrami scale space. Finally, this multiscale segmentation technique is coupled with a multiscale edge detecting function based on the gradient vector flow model, which is able to extract convex and concave object boundaries independent of the initial condition. We apply our multiscale segmentation model on a synthetic image and a medical image.     

Multiscale SAR image segmentation by combining curvelet transform and GMTRJ algorithms

To integrate multiscale analysis within region-based segmentation framework, this paper presents multiscale SAR image segmentation method. First, curvelet transform is used to obtain a collection of the decomposed SAR images on multiple scales. Their domain is partitioned into a set of blocks by a regular tessellation, in which the number of blocks is assumed to be a random variable with Poisson distribution. On the partitioned domain, the Bayesian paradigm is followed to build the region- and multiscale-based image segmentation model with unknown number of classes. Further, a Generalized Multiple-Try Reversible Jump (GMTRJ) algorithm is designed to simulate the segmentation model. During the procedure of iterative simulation for the segmentation model, the segmentation result on the current scale is regarded as the initial segmentation on next scale. The segmented results corresponding to the finest scale is considered as the optimal image segmentation. The proposed method is validated using SAR images. The Kappa coefficient of the test simulated SAR image is up to 0.998, and the Kappa coefficients of the test real SAR images are up or equal to 0.903. From the test results of the quantitative and qualitative evaluation, it can be found that the proposed method can not only determine the number of classes, but also segment homogeneous regions well.     

扩展的多尺度有限元法基本原理

阐述一种适用于非均质材料力学性能分析的扩展的多尺度有限元法（Extended Multiscale Finite Element Method,EMsFEM）的基本原理．该方法的基本思想是利用数值方法构造能反映胞体单元内部材料非均质影响的多尺度基函数,在此基础上求得粗网格层次的等效单元刚度阵,从而在粗网格尺度上对原问题进行求解,很大程度地减少计算量．以该方法进行的具有周期和随机微观结构的材料计算示例,通过与传统有限元法的结果比较,说明这一方法的有效性．EMsFEM的优势在于,能容易地进行降尺度计算,可较准确地求得单元内部的微观应力应变信息,在非均质材料强度和非线性分析中有很大的应用潜力．     

Multiscale segmentation of unstructured document pages using softdecision integration

We present an algorithm for layout-independent document page segmentation based on document texture using multiscale feature vectors and fuzzy local decision information. Multiscale feature vectors are classified locally using a neural network to allow soft/fuzzy multi-class membership assignments. Segmentation is performed by integrating soft local decision vectors to reduce their “ambiguities”     

Compiling network traffic into rules using soft computing methods for the detection of flooding attacks

The ability to dynamically collect and analyze network traffic and to accurately report the current network status is critical in the face of large-scale intrusions, and enables networks to continually function despite of traffic fluctuations. The paper presents a network traffic model that represents a specific network pattern and a methodology that compiles the network traffic into a set of rules using soft computing methods. This methodology based upon the network traffic model can be used to detect large-scale flooding attacks, for example, a distributed denial-of-service (DDoS) attack. We report experimental results that demonstrate the distinctive and predictive patterns of flooding attacks in simulated network settings, and show the potential of soft computing methods for the successful detection of large-scale flooding attacks.     

火灾模拟实验炉温软测量建模的研究

火灾模拟实验炉是对建筑构件进行模拟测试实验的装置,其升温必须按ISO834国际标准执行,炉温的估计是实验的关键,采用了主元分析和RBF神经网络相结合的模型构成火灾模拟实验炉温软测量;主元分析（PCA）实现输入变量的降维,RBF神经网络采用K-均值聚类算法进行隐层中心和连接权调节的学习,实现快速收敛;该融合模型使炉温估计精度比常规的最小二乘方法拟合情度提高2倍以上,保证了升温过程温度的精确控制。     

Identification of multiscale spatio-temporal dynamical systems using a wavelet multiresolution analysis

In this article, a new algorithm for the multiscale identification of spatio-temporal dynamical systems is derived. It is shown that the input and output observations can be represented in a multiscale manner based on a wavelet multiresolution analysis. The system dynamics at some specific scale of interest can then be identified using an orthogonal forward least-squares algorithm. This model can then be converted between different scales to produce predictions of the system outputs at different scales. The method can be applied to both multiscale and conventional spatio-temporal dynamical systems. For multiscale systems, the method can generate a parsimonious and effective model at a coarser scale while considering the effects from finer scales. Additionally, the proposed method can be used to improve the performance of the identification when the measurements are noisy. Numerical examples are provided to demonstrate the application of the proposed new approach.     

Multiscale Modeling of Concrete

In this paper, a mesoscale model of concrete is presented, which considers particles, matrix material and the interfacial transition zone (ITZ) as separate constituents. Particles are represented as ellipsoides, generated according to a prescribed grading curve and placed randomly into the specimen. Algorithms are proposed to generate realistic particle configurations efficiently. The nonlinear behavior is simulated with a cohesive interface model for the ITZ. For the matrix material, different damage/plasticity models are investigated. The simulation of localization requires to regularize the solution, which is performed by using integral type nonlocal models with strain or displacement averaging. Due to the complexity of a mesoscale model for a realistic structure, a multiscale method to couple the homogeneous macroscale with the heterogeneous mesoscale model in a concurrent embedded approach is proposed. This allows an adaptive transition from a full macroscale model to a multiscale model, where only the relevant parts are resolved on a finer scale. Special emphasis is placed on the investigation of different coupling schemes between the different scales, such as the mortar method and the arlequin method, and a discussion of their advantages and disadvantages within the current context. The applicability of the proposed methodology is illustrated for a variety of examples in tension and compression.     

The biomechanical basis for early care programmes

Musculoskeletal injuries in the workplace are at epidemic proportions. The total cost in terms of disability, suffering and economic loss is enormous. The soft tissues are frequently involved in these injuries. Soft tissues respond to the mechanical forces on them. Without the stimulus from mechanical forces, there are biological changes in the tissues and poorer organization of newly synthesized collagen. The consequence is a softening and loss of mechanical strength. Joint fibrosis and contracture also occur. In contrast, exercise results in increased collage fibril size and increased strength. Treatment of soft tissue injuries remains a balance between the immobilization required for primary healing of the tissues and the motion- and stress-dependent homeostatic response. These concepts are important in the early care of all soft tissues. The concepts can also be used to study treatments frequently used in the most common occupational injury-low back pain. Some studies have shown bedrest to be effective in the short term. However, the evidence is not strong that this is the treatment of choice if patients do not respond quickly. The back school appears to be very effective. It is probably effective because the patient becomes more self-confident, receives important information, and is encouraged to get moving and return to work. Thus the vicious circle of dependency is obviated. Traction or bedrest with traction are also popular treatments. There is little evidence of their efficacy although traction and exercise appears to be a beneficial combination. Mobilization and manipulation have some capacity to hasten the resolution of symptoms in some patients, but the long-term prognosis is unchanged. Corsets and braces are not justified in most cases of acute low back pain. The emphasis must be on returning the patient to function as soon as possible. It was Aristotle who observed that ‘movement is life’.     

RF MEMS引线键合的射频性能和等效电路研究

针对一种用键合线连接的简单封装模型进行射频性能的模拟.用HFSS软件对不同长度、不同高度、不同直径以及不同间距的键合线进行模拟,总结出这些参数对键合线射频性能的影响.提出了由顶盖、CPW和键合线组成的简单封装结构的等效电路,并提取参数值.用Mcrowave Office软件对等效电路进行模拟,其S11在6～8 GHz内与HFSS模拟的模型的S11相差2 dB以内,其S21在1～10 GHz内与模型的S21相差0.1 dB以内.     

面向噪声数据的多尺度粗糙集模型研究

针对Pawlak粗糙集模型处理噪声信息的局限性,借鉴变精度粗糙集模型的思想,引入多尺度变量,建立单维的多尺度粗糙集模型。通过构造尺度变量s与尺度函数(fs)的变化关系,对噪声数据进行多尺度、多角度的动态分析,提高抑制噪声的能力,根据评价指标不断地优化尺度,获取满足用户要求的决策规则。实例说明了该方法的优点及可行性。     

多层次-跨尺度物理中并行DVM-DAC算法

多尺度现象及相关理论方法是复杂物质系统研究中重要的科学问题.单一的量子力学或分子动力学方法无法解释多尺度体系中存在的现象.第一原理离散变分线性标度（DVM-DAC）算法是一种有效的大尺度体系计算方法.它采用分而治之方案,获得了O（n）的计算复杂性.但由于需要求解大量的特征方程,实现中存在严重的计算瓶颈.发展了一种并行DVM-DAC算法并付诸实现,有效地解决了原有算法的计算瓶颈问题.测试结果表明,并行DVM-DAC算法具有很好的可扩展性,并成功完成104碳纳米管原子体系的计算,为多尺度体系研究提供重要工具.     

基于改进多元多尺度熵的人体步态加速度信号分类

传统多元多尺度熵算法在处理有限长时间序列时,会使均值曲线产生较大的波动,并且阈值的选取也会对结果产生较大的影响。因此,在传统多元多尺度熵的基础上首先对传统粗粒化方式进行了改进,改进后的算法采用滑动均值滤波使粗粒化后各尺度上的时间序列与原始时间序列长度一致,减小了所计算多元多尺度熵的离散性。此外,本文算法在保持多元样本熵硬阈值优点的同时,通过定义模糊隶属度函数来统计两复合延迟矢量距离略大于阈值的情况,既降低了传统方法对阈值的依赖性,也很好的解决了传统阈值所导致的不稳定现象。最后用仿真数据对该算法进行了验证,并将其应用于不同人体步态加速度信号的复杂度评价和分类,结果表明改进算法的识别效果明显优于传统多元多尺度熵。     

基于改进遗传算法的小波去噪的阈值优化

根据基因重组原理,定义了新的反向逻辑交叉算子和随机逻辑交叉算子对标准遗传算法进行改进.利用改进的遗传算法求解多尺度小波分解每层系数的最优阈值,通过软阈值法对小渡系数处理后进行小波重构.实验结果表明,利用改进的遗传算法进行小波去噪是可行的,且能够达到较高的信噪比.     

Soft tissue deformation with reaction-diffusion process for surgery simulation

This paper presents a new methodology to conduct modelling and analysis of soft tissue deformation from the physicochemical viewpoint of soft tissues for surgery simulation. The novelty of this methodology is that soft tissue deformation is converted into a reaction-diffusion process coupled with a mechanical load, and thus reaction-diffusion of mechanical load and non-rigid mechanics of motion are combined to govern the dynamics of soft tissue deformation. The mechanical load applied to a soft tissue to cause a deformation is incorporated into the reaction-diffusion system and consequently distributed among mass points of the soft tissue. An improved reaction-diffusion model is developed to describe the distribution of the mechanical load in the tissue. A generic finite difference scheme is presented for construction of the reaction-diffusion model on a 3D tissue surface. A gradient method is established for derivation of internal forces from the distribution of the mechanical load. Real-time interactive deformation of virtual human organs with haptic feedback has been achieved by the proposed methodology for surgery simulation. The proposed methodology not only accommodates isotropic, anisotropic and inhomogeneous materials by simply modifying diffusion coefficients, but also accepts local and large-range deformations simultaneously.