美军技术研究与试验建模体系结构(MATREX)

技术研究与试验建模体系结构(以下简称MATREX)项目是美国陆军最重要的分布式建模与仿真环境,提供了统一的建模仿真架构、辅助工具以及基础设施,使得具有各种分辨率的真实、虚拟和构造的军事应用之间的集成和使用非常方便。首先概括介绍了MATREX项目的一些背景情况和基本原理;接着深入剖析了该项目的核心体系结构、各种构件工具、集成设施服务和系统参考模型。最后简要地介绍了使用MATREX环境二次开发的两个军事应用,分别用在未来战斗系统主系统集成商的系统集成实验室和跨司令部协作项目。     

Annealing adaptive search,cross‐entropy,and stochastic approximation in global optimization

The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于WindowsCE的ARM9处理器专用总线设计方法

针对ARM9处理器专用总线扩展时存在总线竞争以及WindowsCE系统下的应用程序不能直接访问外设物理地址等问题，以S3C2410处理器通过HPI接口扩展TMS320VC5402型DSP为例，提出了一种专用总线设计方法，并对防止总线竞争和WindowsCE系统下总线设备的虚拟地址编程访问进行了详细分析。