基于.NET平台多层架构eHR系统的研究

本文结合分布式对象技术和软件多层架构技术的发展状况,对Microsoft公司推出的分布式应用系统.Net平台架构进行研究,深入讨论了基于.Net平台的分布式部署,阐述了构建一个基于Web的分布式应用程序所涉及到的相关技术及工作原理,开发了基于.NetFramework的分布式管理系统,充分体现了.Net平台在开发基于Web的分布式应用程序上的优越性。     

基于时间窗的随机时变交通网络信号相位协调

交通网络是随机时变网络,用周期性时间窗模拟各路口信号灯控制,建立交通网络中路口相位差协调控制模型。时间窗的设定使只有规定行驶方向的车辆可以通行路口,其他车辆不可通行。为得到车辆在路口前等待状况,定义时间窗函数,该函数采用协调交通网络路口信号相位差的方法求得随机时变网络的最短期望路径。结合改进的SDOT算法和穷举法及遗传算法设计一种混合算法。对一个四路口小型交通网络进行了仿真研究,结果验证了求解算法的有效性。     

基于Kalman滤波的MIMO时变信道估计

研究了一种基于Kalman滤波的MIMO时变信道估计与跟踪问题。利用衰落信道功率谱统计特性的先验信息,将信道冲击响应近似为一个低阶自回归滑动平均过程,通过信道传输函数逼近信道功率谱的幅频特性,建立时变衰落单径信道的状态方程,导出MIMO信道状态模型参数,并通过Kalman滤波跟踪信道的时变特性。理论分析和仿真试验表明,该算法在时变信道下具有较好的性能,和传统信道估计方法相比,接收机性能有了较大的改进。     

Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems, in this paper, a new iterative adaptive dynamic programming algorithm, which is the discrete-time time-varying policy iteration (DTTV) algorithm, is developed. The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance. The admissibility of the iterative control law is analyzed. The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution. To implement the algorithm, neural networks are employed and a new implementation structure is established, which avoids solving the generalized Bellman equation in each iteration. Finally, the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm, where the mass and pendulum bar length are permitted to be time-varying parameters. The effectiveness of the developed method is illustrated by numerical results and comparisons.      

Pulse-width predictive control for LTV systems with application to spacecraft rendezvous

This work presents a Model Predictive Controller (MPC) that is able to handle Linear Time-Varying (LTV) plants with Pulse-Width Modulated (PWM) control. The MPC is based on a planner that employs a Pulse-Amplitude Modulated (PAM) or impulsive approximation as a hot-start and then uses explicit linearization around successive PWM solutions for rapidly improving the solution by means of quadratic programming. As an example, the problem of rendezvous of spacecraft for eccentric target orbits is considered. The problem is modeled by the LTV Tschauner–Hempel equations, whose state transition matrix is explicit; this is exploited by the algorithm for rapid convergence. The efficacy of the method is shown in a simulation study.     

A review of the Expectation Maximization algorithm in data-driven process identification

The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided.     

Model-based iterative learning control strategies for precise trajectory tracking in gasoline engines

In this paper trajectory tracking algorithms for gasoline engines are devised. Specifically, precise reference tracking in engine speed and air-to-fuel ratio is enabled while satisfying initial and final conditions on the center of combustion. Such a tracking of multiple reference trajectories requires a coordinated control action for the air path, the fuel path, and the ignition timing actuators. Combining a dedicated feedforward and feedback controller structure and multivariable model-based norm-optimal parallel iterative learning control strategies, feedforward control trajectories are generated that enable a precise tracking of desired reference trajectories. Experimental results focusing on the termination of the catalyst heating mode show the effectiveness of the proposed methodology, resulting in a control error reduction above 85%.     

基于多模板隐马尔可夫模型的文本信息抽取算法

针对训练数据来源的多样化，提出了基于多模板隐马尔可夫模型的广本信息抽取算法。该算法利用形式的聚类方法将训练数据聚成几个类，每个类代表一个模板，在聚类的基础上利用隐马尔可丈模型进行文本的信息抽取。实验结果表明，新算法具有较高的精确度和召回率。     

一种基于目标属性特征的多假设关联算法

低时间分辨率遥感成像观测使得合适的目标运动模型难以建立,目标的运动状态变量无法准确估计,造成基于Kalman滤波等运动状态估计算法的经典多目标关联算法不再适用。该文针对该问题提出了一种基于目标属性特征的多假设关联算法,以目标的属性特征作为关联的基本参量,衡量观测到航迹的匹配程度。仿真实验结果证明,该算法对基于低时间分辨率遥感图像的机动目标关联问题具有适用性。     

基于辅助决策支持的虚拟经济性运行系统的设计

火力发电厂机组经济性运行辅助决策系统采用了多元非线性回归数学模型和模型优化技术建立虚拟经济性运行机组,实时动态的优化机组的经济性运行参数,该设计思想在决策分析方面的研究和应用中有一定的特色。在分析、设计阶段中,使用了统一建模语言UML来简单描述其过程。