Supervisory control of families of linear set-point controllers. 2.Robustness

A simply structured high-level controller, called a “supervisor”, has recently been proposed in part I of this article (ibid., vol.41, 1996) for the purpose of orchestrating the switching of a sequence of candidate set-point controllers into feedback with an imprecisely modeled SISO process so as to cause the output of the process to approach and track a constant reference input. The process is assumed to be modeled by an SISO linear system whose transfer function is in the union of a number of subclasses, each subclass being small enough so that one of the candidate controllers would solve the set-point tracking problem, if the process transfer function was to be one of the subclass members. This paper proves that without any further modification, the same supervisor described in Part I can also perform this function in the face of norm-bounded unmodeled dynamics, and moreover that none of the signals within the overall system can grow without bound in response to bounded disturbance and noise inputs, whether they are constant or not     

VS.NET中Crystal Reports的使用

介绍了Crystal Reports功能、结构和特点,通过在VS.NET创建具体的Windows应用演示了Crystal Repots的强大功能.     

Efficient training of RBF neural networks for pattern recognition.

The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R(n) is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns. In this paper, taking into account the specific feature of classification problems, where the goal is to obtain that the network outputs take values above or below a fixed threshold, we propose an approach alternative to the classical one that makes use of the least-squares error function. In particular, the problem is formulated in terms of a system of nonlinear inequalities, and a suitable error function, which depends only on the violated inequalities, is defined. Then, a training algorithm based on this formulation is presented. Finally, the results obtained by applying the algorithm to two test problems are compared with those derived by adopting the commonly used least-squares error function. The results show the effectiveness of the proposed approach in RBF network training for pattern recognition, mainly in terms of computational time saving.     

On improvement of classification accuracy for stochastic discrimination.

Stochastic discrimination (SD) depends on a discriminant function for classification. An improved SD is introduced to reduce the error rate of the standard SD in the context of a two-class classification problem. The learning procedure of the improved SD consists of two stages. Initially a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. Then the standard SD is modified by 1) restricting sampling in the important space, and 2) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but with a smaller variance than that of the standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples are provided to demonstrate the effectiveness of the proposed improved SD.     

Application of the CVBEM to non-uniform St. Venant torsion

The complex-variable boundary-element method, or CVBEM, is used to approximate the stress distribution associated with non-uniform St. Venant torsion problems. By specifying either the normal- or tangential-force equilibrium equation in terms of the warping function or its conjugate, a Laplace equation results which is immediately tractable by using the CVBEM. A comparison of modeling results to known solutions indicates that the modeling technique is a useful approach for the estimation of interior stresses as well as for the evaluation of modeling error by means of an approximate boundary determined by the CVBEM approximation function.     

Visual Studio．Net中的分布式处理

在当今Web应用程序开发中,分布式处理正得到广泛应用,其作用和地位越发显得重要。Visual Studio．Net开发工具中的WebService,是开发分布式Web应用程序的简单易用工具。利用Web Service,可以把复杂的处理运算分布到不同的Web服务器上,减轻单个服务器的负担,提高效率;利用Web Service,可以使不同地区、不同部门之间共享数据和功能服务,使得Web应用程序系统的功能更强大、更灵活。下面以VisualStudio．Net2005为版本,论述如何利用其中的Visual Basic．Net语言工具,来开发及部署Web Service,并在其它Web应用程序中如何实现对其调用。     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

Learning to design synergetic computers with an extended symmetric diffusion network.

This article proposes an extended symmetric diffusion network that is applied to the design of synergetic computers. The state of a synergetic computer is translated to that of order parameters whose dynamics is described by a stochastic differential equation. The order parameter converges to the Boltzmann distribution, under some condition on the drift term, derived by the Fokker-Planck equation. The network can learn the dynamics of the order parameters from a nonlinear potential. This property is necessary to design the coefficient values of the synergetic computer. We propose a searching function for the image processing executed by the synergetic computer. It is shown that the image processing with the searching function is superior to the usual image-associative function of synergetic computation. The proposed network can be related, as a special case, to the discrete-state Boltzmann machine by some transformation. Finally, the extended symmetric diffusion network is applied to the estimation problem of an entire density function, as well as the proposed searching function for the image processing.     

基于ASP．NET的主题在网站设计中用户登录界面的应用

主题是ASP．NET的一项强大的应用功能,通过利用主题功能．可以减少需要添加到各个ASP．NET页面的内容量,一次定义控件的外观,将该外观应用于整个Web应用程序。在实例中重点介绍主题在网站设计中用户登录界面的具体应用．使用主题功能可以轻松创建具有一致的并可维护的外观设计的网站。     

超球SVM与证据理论在故障诊断中的应用

针对使用多传感器信息融合技术进行故障诊断时,故障模式较多、基本概率赋值难以确定的问题,提出一种基于超球支持向量机与D-S证据理论相结合的故障诊断方法。该方法使用超球支持向量机针对每一个传感器的故障空间训练分类模型,根据类内隶属度与类-类相似度得到各故障类别的基本概率赋值,利用D-S证据理论进行证据融合,基于信任函数进行故障决策。试验结果表明该方法提高了故障识别能力,有一定实践意义。     

Control engineering applications of V. I. Zubov's construction procedure for Lyapunov functions

The determination of a stability domain of a control system, the motion of which is described by nonlinear differential equations, is often the object of intensive experimental and theoretical attack. This paper, partly tutorial and partly a presentation of new results, describes a method for obtaining a solution to this problem proposed recently by the Russian mathematician, V. I. Zubov. The tutorial part outlines the fundamental principles of V. I. Zubov's procedure for constructing Lyapunov functions for non-linear systems. If the construction problem can be solved, it leads to a Lyapunov function which uniquely defines the exact boundary of the stability region. For the application of the method, several simple examples are treated in which the exact stability region is found in analytic closed form. Since the construction procedure requires the solution of a linear partial differential equation, there are many cases for which an exact analytic solution is not possible. In some of these cases, however, it is possible to construct an approximate series solution which is always at least as good as the usual quadratic form Lyapunov function. The series construction procedure has been programmed (in IBM 7070 FORTRAN language) for a broad class of differential equations of the second order. A simple example solved by the digital computer program is described.     

Compositing. 1. Theory

Associating a pixel's color with its opacity is the basis for a compositing function that is simple, elegant, and general. However, there are more reasons than mere prettiness to store pixels this way. One of the most important anti-aliening tools in computer graphics comes from a generalization of the simple act of storing a pixel into a frame buffer. Several people simultaneously discovered the usefulness of this operation, so it goes by several names: matting, image compositing, alpha blending, overlaying, or lerping. It was most completely codified in a previous paper by Porter and Duff (1984), where they call it the over operator. The author shows a new way to derive the over operator and describes some implementation details that he has found useful     

Ridge polynomial networks.

This paper presents a polynomial connectionist network called ridge polynomial network (RPN) that can uniformly approximate any continuous function on a compact set in multidimensional input space R (d), with arbitrary degree of accuracy. This network provides a more efficient and regular architecture compared to ordinary higher-order feedforward networks while maintaining their fast learning property. The ridge polynomial network is a generalization of the pi-sigma network and uses a special form of ridge polynomials. It is shown that any multivariate polynomial can be represented in this form, and realized by an RPN. Approximation capability of the RPN's is shown by this representation theorem and the Weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental network growth. Simulation results on a surface fitting problem, the classification of high-dimensional data and the realization of a multivariate polynomial function are given to highlight the capability of the network. In particular, a constructive learning algorithm developed for the network is shown to yield smooth generalization and steady learning.     

A Review of: “PROBLEM - SOLVING METHODS IN ARTIFICIAL INTELLIGENCE”, by Nils J. Nilsson. McGraw-Hill,New York, 1971. 255 pp.

After defining systems that can be represented by scators it is shown that the merging of such systems is conveniently modelled by scator addition. It is shown that scator addition although not generally associative will be so if a certain function involved is solution of a functional equation whose general solution is found. Finally after defining scator derivatives it is shown that systems with continuous merging lead to differential equations in scators.     

A new constrained independent component analysis method.

Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.     

Optimal design of nonlinear d.c. circuits

This paper presents a method for optimal design of nonlinear d.c. circuits which minimizes the difference between the desired and actual specifications subject to constraints based on circuit equations and limits on design parameters. All the problem variables are decomposed into two sets, i.e. independent and dependent variables, which reduces the size of the problem. Computation of partial derivatives of the objective function is also done more efficiently and it does not require the adjoint network and its solution. Reliability and efficiency of the method is tested by the authors for large practical circuits.     

Linearization of logical functions defined by a set of orthogonal terms. I. Theoretical aspects

Consideration was given to the linearization of logical functions defined by a set of pairwise orthogonal terms. The linearization is carried out by computing the autocorrelation functions. Proposed was a method consisting of (i) calculation of the autocorrelation function in the space of orthogonal terms, (ii) generation of the corresponding matrix of linear transformation, and (iii) the linear transformation proper of the variables in the space of orthogonal terms. Complexity of the proposed method and its effectiveness were estimated. Effectiveness was verified by a series of experiments with standard benchmarks. The distinctions of the proposed method from other existing methods of linearization were examined.     

Reducing “Structure from Motion”: a general frameworkfor dynamic vision. 1. Modeling

The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of apparently unrelated models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction. The “natural” dynamic model, derived from the rigidity constraint and the projection model, is first reduced by explicitly decoupling structure (depth) from motion. Then, implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for models seen so far in the literature, but we can also derive novel ones