多维广义次成分提取准则及自适应算法

针对广义次成分在信号处理中没有与之相对应的信息准则,而且只能提取单维广义次成分,提出一种多维广义次成分提取准则,并通过矩阵微分法证明了所提出信息准则只有唯一的全局极大值.基于该信息准则,采用梯度上升法导出一种多维广义次成分提取算法,并采用李雅普诺夫函数法对所提出算法的全局收敛性进行证明.与其他现有算法不同,所提出算法可以并行提取多维次成分,而并不需要模值归一化.仿真实验表明,所提出算法相比一些现有算法具有收敛速度快和估计精度高的优点.     

一个多维次成分并行提取算法及其收敛性分析

次成分分析是信号处理领域内一项重要的分析工具.目前,多维次成分并行提取算法数量稀少,而且现有的算法在应用时还存在很多限制条件.针对上述问题,在分析研究OJAm次子空间跟踪算法的基础上,采用加权矩阵法提出了一种多维次成分提取算法,并采用递归最小二乘法对所提算法进行了简化,最后采用李雅普诺夫函数法确定了所提算法的全局收敛域.相比现有算法,所提算法对信号的特征值大小没有要求,也不需要在迭代过程中进行模值归一化操作,同时算法具有较低的计算复杂度.仿真实验表明：所提算法能够并行提取多维次成分,而且收敛速度要优于现有同类型算法.     

DRM multidomain mass conservative interpolation approach for the BEM solution of the two-dimensional navier-stokes equations

The dual reciprocity method (DRM) is a technique to transform the domain integrals that appear in the boundary element method into equivalent boundary integrals. In this approach, the nonlinear terms are usually approximated by an interpolation applied to the convective terms of the Navier-Stokes equations. In this paper, we introduce a radial basis function interpolation scheme for the velocity field, that satisfies the continuity equation (mass conservative). The proposed method performs better than the classical interpolation used in the DRM approach to represent such a field. The new scheme together with a subdomain variation of the dual reciprocity method allows better approximation of the nonlinear terms in the Navier-Stokes equations.     

Implicit learner assessment based on semantic relevance of tags

In this paper, a novel method based on tagging technology is proposed for assessing learners which is named TagAssessment. In this method, in order to assess learners, the semantic relationship between educational contents and learner's tags is computed using WordNet ontology. An automatic keyword extraction component is also proposed to facilitate using this method for each educational content. To evaluate this method, an e-learning system is developed. The obtained results show that learner's assessment with the proposed method was too close to the multiple choice question method. Also, the results of the automatic keyword extraction component were very similar to standard tags which were gained by experts.     

非线性多变量零阶接近有界系统的多模型自适应控制

针对一类多变量非线性离散时间系统,提出一种新的基于神经网络的多模型自适应控制方法.为了将非线性系统的高阶非线性项的限制条件放宽到零阶接近有界,该方法引入了一种新的非线性模型.该模型在传统线性回归模型基础上增加了非线性补偿项,使模型的估计误差有界.一个神经网络模型与非线性模型同时被用来对系统进行辨识.基于性能指标的切换机构选择性能较好的模型对应的控制器 对系统进行控制. 理论分析证明了零阶接近有界多模型自适应控制系统的有界输 入和有界输出稳定性. 仿真实验说明了提出的多模型自适应控制方法的有效性.     

Three-dimensional eigenvalue analysis of the Helmholtz equation by multiple reciprocity boundary element method

The eigenvalue of the three-dimensional Helmholtz equation is determined efficiently by extending the previously developed method for the two-dimensional problem. Boundary integral equation is formulated in the realm of the multiple reciprocity method, using higher order fundamental solutions for the Laplace equation; yielding polynomial coefficient matrices in terms of unknown wavenumber (eigenvalue). The Newton iteration method with the help of LU decomposition is employed to search eigenvalue, which can reduce the computational task significantly.     

Distributed power adjustment based on control theory for cognitive radio networks

In this paper, we propose two power adjustment methods for cognitive radio networks. In the first algorithm, the transmitter derives the transmission power with PID control in order to satisfy the QoS constraints in secondary networks. The derived transmission power is compared with a constraint condition in order to avoid the interference with primary networks, and then the actual transmission power is decided. Because the constraint condition affects the performance of our proposed method significantly, we propose an effective update algorithm. On the other hand, the second algorithm is based on model predictive control (MPC). In this method, the decision of transmission power is formulated as quadratic programming (QP) problem and the transmission power is derived directly with the constraint condition. We evaluate the performances of our proposed methods with simulation and compare the proposed methods with the distributed power control (DPC) method. In numerical examples, we show that our proposed methods are more effective than the existing method in some situations. We also prove analytically that the interference with primary networks can be avoided with probability one by using our proposed method if each transmitter has the information about every channel gain.     

基于概率假设密度滤波的多目标雷达空间误差配准算法

针对数据关联关系不确定的多目标场景下的雷达空间误差配准问题,提出一种基于概率假设密度(PHD)滤波的雷达空间误差估计方法.该方法在地心地固(ECEF)坐标系下建立雷达空间误差及其观测的随机有限集合(RFS)描述形式,结合PHD滤波算法避免多个雷达观测的数据关联问题,利用高斯混合概率假设密度(GM-PHD)滤波器实现多目标场景下的雷达系统误差递推融合估计.Monte Carlo仿真实验结果表明,所提出的基于PHD滤波的雷达空间误差配准算法能够有效地实现数据关联关系不确定情况下的多目标雷达空间误差融合估计,且估计精度及稳定性优于结合数据关联算法的空间误差配准方法.     

基于多不动点约束的网格模型局部编辑

传统的自由变形方法通过计算控制晶格来变形,不适用于精确变形的情况。针对此问题,提出一种基于多不动点约束的网格模型局部编辑算法,该算法通过多个不动点的合理配置设定多种复杂的约束条件,实现网格模型的局部编辑,进而精确的变形模型。实验表明算法计算代价低,可实现精确变形。     

A novel alignment repulsion algorithm for flocking of multi-agent systems based on the number of neighbours per agent

In this paper, an energy-based control methodology is proposed to satisfy the Reynolds three rules in a flock of multiple agents. First, a control law is provided that is directly derived from the passivity theorem. In the next step, the Number of Neighbours Alignment/Repulsion algorithm is introduced for a flock of agents which loses the cohesion ability and uniformly joint connectivity condition. With this method, each agent tries to follow the agents which escape its neighbourhood by considering the velocity of escape time and number of neighbours. It is mathematically proved that the motion of multiple agents converges to a rigid and uncrowded flock if the group is jointly connected just for an instant. Moreover, the conditions for collision avoidance are guaranteed during the entire process. Finally, simulation results are presented to show the effectiveness of the proposed methodology.     

DoS攻击下基于切换Luenberger观测器的冗余控制

研究了DoS攻击下网络化控制系统基于观测器的控制器设计问题.首先,提出了一种具有多个增益的切换Luenberger观测器,实现了无DoS攻击时系统状态的间歇性估计.根据获得的估计值,控制器同时计算了系统当前以及未来一段时域的控制信号,并将其封装在一个数据包中发送给执行器,保证了系统在有无DoS攻击时都有合适的控制输入更新.其次,利用构造的切换系统对DoS攻击下观测器与控制器的不同动态进行了统一的建模,基于多Lyapunov函数方法推导了任意切换律下系统指数稳定的充分条件,并给出了相应的观测器与控制器设计方法.最后,通过网络化倒立摆系统的实验验证了所提方法的有效性.     

A new complex-valued formulation and eigenvalue analysis of the Helmholtz equation by boundary element method

By considering the close relationship between the multiple reciprocity boundary element formulation and that of the fundamental solution of the Helmholtz differential operator, we present a new complex-valued integral equation formulation for the eigenvalue analysis of the scalar-valued Helmholtz equation. Eigenvalues are determined as local minima of the determinant of the coefficient matrix of the discretized equation iteratively by the Newton scheme. The necessary recurrence formula is derived and computed with high efficiency, due to polynomial representation of the matrix components. Some example computations demonstrate the utility of the proposed formulation and eigenvalue determination scheme, and construction of adaptive boundary elements for the eigenvalue determination is attempted.     

Applications of dual MRM for determining the natural frequencies and natural modes of a rod using the singular value decomposition method

In this article, the dual multiple reciprocity method is employed to solve the natural frequencies and natural modes for a rod. The conventional approach using dual MRM is not qualified as a systematic method because of the following two reasons: (1) it needs to distinguish the spurious eigenvalue only after the corresponding eigenmode is obtained; (2) the possible indeterminancy of eigenvector may be encountered when the constraint equations chosen are highly dependent such that the rank of the leading coefficient matrix is insufficient. To construct a systematic way, we propose to consider all constraint equations together instead of using the singular or hypersingular equation alone as the conventional MRM uses. The singular value decomposition method is, then, used to solve the eigenproblem after combining the singular and hypersingular equations. This method can avoid the spurious eigenvalue problem and the possible indeterminancy of boundary eigenvectors at the same time. Three numerical examples are given to verify the validity of the present method.     

Generalizing the definition of the bi-directional reflectance distribution function

The fundamental quantity that characterizes the reflectance property of a surface is the bi-directional reflectance distribution function (BRDF), defined as the ratio of the radiance scattered by a surface into a specified direction to the unidirectional irradiance incident on a surface. Its standard definition was derived under very restrictive conditions and it has only the angles of illumination and measurement as dependent variables. Several recent papers have attempted to generalize the BRDF to include the spatial attributes of illumination and measurement in order to make it applicable to heterogeneous media. The various BRDF definitions proposed by these papers are shown to be special cases of a generalized form of the BRDF derived herein. The spatial attributes of illumination and measurement are included as part of the nomenclature of the generalized BRDF. It is also shown that the generalized BRDF obeys reciprocity when properly weighted by the areas of illumination and measurement.     

A new method of obtaining the priority weights from an interval fuzzy preference relation

In analyzing a multiple criteria decision-making problem, the decision maker may express her/his opinions as an interval fuzzy or multiplicative preference relation. Then it is an interesting and important issue to investigate the consistency of the preference relations and obtain the reliable priority weights. In this paper, a new consistent interval fuzzy preference relation is defined, and the corresponding properties are derived. The transformation formulae between interval fuzzy and multiplicative preference relations are further given, which show that two preference relations, consistent interval fuzzy and multiplicative preference relations, can be transformed into each other. Based on the transformation formula, the definition of acceptably consistent interval fuzzy preference relation is given. Furthermore a new algorithm for obtaining the priority weights from consistent or inconsistent interval fuzzy preference relations is presented. Finally, three numerical examples are carried out to compare the results using the proposed method with those using other existing procedures. The numerical results show that the given procedure is feasible, effective and not requisite to solve any mathematical programing.     

基于多特征信息融合的模拟电路故障预测

为解决利用单一信息进行故障预测的不足以及提高模拟电路故障预测的准确度,提出了一种将信息融合应用到模拟电路故障预测中的方法.提取模拟电路多个测点的多种故障特征量,对其进行时间序列分析,采用ARMA模型研究其预测过程,将得到的预测结果转换为模拟电路故障发生的概率,最后将得到的多个数据进行加权融合,实现了基于多特征信息融合的...     

Individual Kernel Tensor-subspaces for robust face recognition: a computationally efficient tensor framework without requiring mode factorization.

Facial images change appearance due to multiple factors such as different poses, lighting variations, and facial expressions. Tensors are higher order extensions of vectors and matrices, which make it possible to analyze different appearance factors of facial variation. Using higher order tensors, we can construct a multilinear structure and model the multiple factors of face variation. In particular, among the appearance factors, the factor of a person's identity modeled by a tensor structure can be used for face recognition. However, this tensor-based face recognition creates difficulty in factorizing the unknown parameters of a new test image and solving for the person-identity parameter. In this paper, to break this limitation of applying the tensor-based methods to face recognition, we propose a novel tensor approach based on an individual-modeling method and nonlinear mappings. The proposed method does not require the problematic tensor factorization and is more efficient than the traditional TensorFaces method with respect to computation and memory. We set up the problem of solving for the unknown factors as a least squares problem with a quadratic equality constraint and solve it using numerical optimization techniques. We show that an individual-multilinear approach reduces the order of the tensor so that it makes face-recognition tasks computationally efficient as well as analytically simpler. We also show that nonlinear kernel mappings can be applied to this optimization problem and provide more accuracy to face-recognition systems than linear mappings. In this paper, we show that the proposed method, Individual Kernel TensorFaces, produces the better discrimination power for classification. The novelty in our approach as compared to previous work is that the Individual Kernel TensorFaces method does not require estimating any factor of a new test image for face recognition. In addition, we do not need to have any a priori knowledge of or assumption about the factors of a test image when using the proposed method. We can apply Individual Kernel TensorFaces even if the factors of a test image are absent from the training set. Based on various experiments on the Carnegie Mellon University Pose, Illumination, and Expression database, we demonstrate that the proposed method produces reliable results for face recognition.     

A new Wavelet Method for Variable‐Order Fractional Optimal Control Problems

In this paper, a new computational method based on the Legendre wavelets (LWs) is proposed for solving a class of variable‐order fractional optimal control problems (V‐FOCPs). To do this, a new operational matrix of variable‐order fractional integration (OMV‐FI) in the Riemann‐Liouville sense for the LWs is derived and used to obtain an approximate solution for the problem under study. Along the way the hat functions (HFs) are introduced and employed to derive a general procedure to compute this matrix. In the proposed method, the variable‐order fractional dynamical system is transformed to an equivalent variable‐order fractional integro‐differential dynamical system, at first. Then, the highest integer order of the derivative of the state variable and the control variable are expanded by the LWs with unknown coefficients. Next, the OMV‐FI in the the Riemann‐Liouville sense together with some properties of the LWs are employed to achieve a nonlinear algebraic equation in place of the performance index and a nonlinear system of algebraic equations in place of the dynamical system in terms of the unknown coefficients. Finally, the method of constrained extremum is applied which consists of adjoining the constraint equations derived from the given dynamical system to the performance index by a set of undetermined Lagrange multipliers. As a result, the necessary conditions of optimality are derived as a system of algebraic equations in the unknown coefficients of the state variable, control variable and Lagrange multipliers. Furthermore, the efficiency and accuracy of the proposed method are demonstrated for some concrete examples. The obtained results show that the proposed method is very efficient and accurate.     

基于多视图树的多关系分类

提出一种新型的利用多视图树的方法解决多关系分类的问题。在多视图分类方法中,不同视图对分类任务贡献不同,视图间存在互补性。对视图互补性进行研究,提出视图互补性的度量方法。根据视图互补性选择视图进行集成,构造多视图树,用于多关系分类。实验结果表明,多视图树无论在分类效果还是效率上,都优于现有的多关系分类方法。