具有扇形执行器的多机电力系统气门开度的时滞无关镇定

本文研究了具有扇形执行器的多机电力系统气门开度的时滞无关分散控制器和鲁棒分散控制器的设计问题.首先利用三角变换把非线性关联函数变换为子系统状态变量的二次有界不等式形式,然后基于Lyapunov稳定性理论,推导出闭环多机电力系统及其参数不确定系统渐近稳定的线性矩阵不等式(LMI)充分条件,最后以两机无穷大母线系统为例进行了仿真分析,验证了所提方法的有效性.     

基于T-H方程的编队卫星碰撞概率与规避策略研究

基于T-H方程,对椭圆轨道下编队卫星碰撞预测以及规避机动进行了研究。通过递推编队卫星的初始状态协方差矩阵,将碰撞概率密度在危险域内积分获得编队卫星的碰撞概率。当碰撞概率大于安全阈值时,采用瞬时校正速度的控制策略对卫星施加最小脉冲速度修正量,在所预测的碰撞点沿碰撞概率梯度产生偏移,到达安全等高线上,从而降低碰撞概率。仿真结果表明,该预测方法有效,规避策略可行。     

机载雷达系统测试性分析与优化

针对机载雷达系统的特点,研究了系统测试性建模问题。根据机载PD雷达的组成和主要功能,结合FMEA分析结果,使用eXpress软件建立了机载PD雷达测距子系统的测试性模型,对模型的测试性参数进行了计算和分析。对测距子系统模型构造出故障信息矩阵,通过逐步分解故障信息矩阵的方法,对模型进行测试点优选。运用故障植入法通过测试设备对雷达进行测试,并计算测试性参数,计算结果表明所采用的测试方法满足系统测试性的要求。     

基于支持向量机和灰度共生矩阵的纹理图像分割方法

图像分割是计算机视觉领域的研究热点。灰度共生矩阵是图像灰度的二阶统计度量,反映了纹理图像灰度在方向、局部邻域和变化幅度的综合信息,以能量、对比度、熵、差方差和差熵作为纹理图像的特征,利用支持向量机(SVM)对这些特征进行训练和分类,以达到纹理图像分割的目的。详细说明了纹理图像的分割过程,同时分析了不同参数情况下对分割精度的影响。针对Brodatz纹理库的实验结果表明:该方法具有较好的分割效果。     

基于图像的异型墙地砖在线分道系统

提出了一种异型墙地砖在线提取和纹理比较的方法。将CCD采集的墙地砖图像进行灰度阈值分割后,经过以矩形模式为特征的轮廓搜索,对原始图像中的砖面区域做标准化仿射变换处理,实现墙地砖的在线提取。依据计算共生矩阵特征向量,采用最小距离分类原理进行纹理比较分类决策,并采用自适应阈值参数加强算法的鲁棒性。基于所提取算法研制了异型墙地砖在线分道系统,并在实际现场运行成功,取得良好的分道效果。     

一类Lurie 时滞广义系统的时滞相关H∞ 控制

研究一类Lurie时滞广义系统的H∞控制问题.以矩阵不等式形式给出了保证闭环系统正则、无脉冲、全局一致渐近稳定且具有给定性能的时滞相关充分条件.进一步,以非凸约束下的线性矩阵不等式(LMIs)的可行解给出了状态反馈控制律设计方法.仿真算例表明了所提出方法的有效性.     

Personalized Learning Course Planner with E-learning DSS using user profile

Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students’ attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students’ initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students’ satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students’ previous test scores and priorities encouraged students to concentrate on the lesson.     

Operability analysis of nonlinear processes based on incremental dissipativity

Process operability can be defined as the ability of a process to reject disturbances at a specified operating point and/or to move quickly and smoothly from one operating point to another operating point using a feedback control system. Unlike linear processes, the properties of nonlinear processes (e.g., stability, minimum phase condition, etc.) are different around different equilibria. Most existing operability analysis for nonlinear systems focuses on one particular operating point of interest. This paper addresses the issues of dynamic process operability at various operating points, including the reachability of all equilibrium points or output trajectories in an operating region, regardless of initial conditions. In this work, a nonlinear analysis approach is developed based on the concept of incremental stability. Conditions for incremental stability are derived based on incremental dissipativity. The links between input and output multiplicities and incremental dissipativity are explored. The dynamic control performance achievable in terms of the speed of the response of the closed-loop system and offset minimization is studied. A method for determination of incremental dissipativity using Linear Differential Inclusion (LDI) is also presented, to facilitate the dissipativity based operability analysis.     

Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays

In this paper, the problem of passivity analysis is investigated for uncertain stochastic fuzzy interval neural networks with time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved by some standard numerical packages. Finally, numerical examples are given to show the effectiveness and merits of the proposed method.     

Delay-dependent robust asymptotic state estimation of Takagi-Sugeno fuzzy Hopfield neural networks with mixed interval time-varying delays

This paper investigates delay-dependent robust asymptotic state estimation of fuzzy neural networks with mixed interval time-varying delay. In this paper, the Takagi-Sugeno (T-S) fuzzy model representation is extended to the robust state estimation of Hopfield neural networks with mixed interval time-varying delays. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally asymptotically stable. Based on the Lyapunov-Krasovskii functional which contains a triple-integral term, delay-dependent robust state estimation for such T-S fuzzy Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally two numerical examples are provided to demonstrate the effectiveness of the proposed method.