飞机机电综合管理框架下的水系统建模与仿真

为了提高现行服役客机机载公共设备的综合化管理水平,搭建了以ARINCA29总线互连的分布式机电综合管理数字化仿真平台,建立了该仿真平台的飞机水系统受控模型.模型主要仿真水系统中水体的调度和分配,进行了水系统模型的功能和外特性仿真.仿真结果表明,在机电综合管理数字化仿真平台上,能够有效地实现实际水系统的所有功能特性,验证了机电综合管理数字化仿真平台的有效性,为飞机机电综合管理的最终实现提供了技术基础.     

动态电源管理的随机切换模型与策略优化

提出一种基于连续时间Markov决策过程的动态电源管理策略优化方法.通过建立动态电源管理系统的随机切换模型,将动态电源管理问题转化为带约束的策略优化问题,并给出一种基于矢量合成的策略梯度优化算法.随机切换模型对动态电源管理系统的描述精确,策略优化算法简便有效,既能离线计算,也适用于在线优化.仿真实验验证了该方法的有效性.     

微机多媒体数据库管理系统MObase

本文介绍了我们研制的微机多媒体数据库管理系统ＭＯｂａｓｅ的基本特点。Ｍｏｂａｓｅ是在我们研制的微机面向对象的数据库管理系统－Ｏｂａｓｅ基础上扩展而成的。Ｏｂａｓｅ支持面向对象的数据模型，用户界面就是一个Ｃ＋＋的特殊的类－－Ｏｂｃｌａｓｅ，提供了和Ｃ＋＋的完全无缝连接，系统本身用面向对象的方法实现，易于扩充。ＭＯｂａｓｅ中又新增了二进制串ＬＯＮＧ及图象帧ＰＩＣ ＦＲＡＭＥ等原子类以存储声音，图象等多     

分布式系统管理策略的规格说明与实现模型

由于用户对分布式系统高可靠性、系统管理及管理员的分散化的需求以及系统自身的高度复杂性，因此，自动的、主动的而不是补救式的系统管理，对于分布式系统是十分重要的。自动管理的基础是管理策略的形式化。本文试图用面向对象方法对分布式系统管理策略进行形式化定义。在仔细研究二种管理策略：授权策略和义务策略、策略组成及策略属性的基础上，归纳出了管理策略定义的一般形式，并给出了几个策略模型化和规格说明的例子。最后简单讨论了有关实现方面的问题。本文讨论的技术同样适用于大型计算机网络管理。     

一种基于分布式代理的网络管理重构机制

为了提高网络管理系统自身的可靠性，提出一种基于分布式代理的重构容错机制。该机制在集中式管理域内，设置多个具备管理者部分或全部功能的分布式代理实体作为冗余，保证了在域管理者失效时，能通过分布式代理的故障检测模块触发重构，从而选出负载性能最好的分布式代理来替代原管理者，并重构管理域。此外，还对分布式代理实体的结构进行了说明。     

Embedded software debugging using virtual filesystem abstractions

We present a scalable technique to simplify the software debugging process for embedded systems that is based on the use of distributed filesystem abstractions. In this technique, the principal building blocks within embedded systems comprising “system-on-chip” (SoC) devices export debugging interfaces realized with filesystem abstractions. These filesystem interfaces are composed in a manner consistent with the hardware hierarchy and provide a portable interface to concurrently debug software executing in the various processing elements within the embedded system. We illustrate application of the model to support the various software debugging requirements unique to SoCs, many of which traditional methods are ill-equipped to deal with. We also present an architecture that may be used to realize distributed filesystem abstractions within resource constrained embedded architectures.     

基于因果逻辑关系的CPS分布式控因研究

针对多子系统间存在复杂因果逻辑关系的信息物理系统(cyber-physical systems,CPS),建立带有未知非线性项和不确定耦合项的CPS多因系统模型,并提出基于云控制技术的分布式控因方法.利用非线性解耦观测器对CPS多因系统进行动态前馈线性化,使得CPS多因系统分解为多个无耦合关联的CPS因系统.设计基于非线性解耦观测器的分布式模型预测控制器以及分布式优化算法,对解耦后的CPS因系统实现在线约束优化控制.最后,通过数值仿真表明所提出控制策略和优化算法的有效性.     

Reliability Optimization in the Design of Distributed Systems

The reliability of a distributed system depends on the reliabilities of its communication links and computing elements, as well as on the distribution of its resources, such as programs and data files. A useful measure of reliability in distributed systems is the terminal reliability between a pair of nodes which is the probability that at least one communication path exists between these nodes. An interesting optimization problem is that of maximizing the terminal reliability between a pair of computing elements under a given budget constraint. Analytical techniques to solve this problem are applicable only to special forms of reliability expressions. In this paper, three iterative algorithms for terminal reliability maximization are presented. The first two algorithms require the computation of terminal reliability expressions, and are therefore efficient for only small networks. The third algorithm, which is developed for large distributed systems, does not require the computation of terminal reliability expressions; this algorithm maximizes approximate objective functions and gives accurate results. Several examples are presented to illustrate the approximate optimization algorithm and an estimation of the error involved is also given.     

Backward Inference in Bayesian Networks for Distributed Systems Management

The growing complexity of distributed systems in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for distributed management systems. An efficient distributed management system needs to work effectively even in face of incomplete management information, uncertain situations, and dynamic changes. In this paper, Bayesian networks are proposed to model dependencies between managed objects in distributed systems. The strongest dependency route (SDR) algorithm is developed for backward inference in Bayesian networks. The SDR algorithm can track the strongest causes and trace the strongest routes between particular effects and its causes, the strongest dependency of causes can be also achieved by the algorithm. Thus, the backward inference provides an efficient mechanism in fault locating, and is beneficial for performance management.     

Distributed model predictive control for polytopic uncertain systems subject to actuator saturation

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min–max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm.