分层离散事件系统的不透明性

针对离散事件系统(DES)的不透明性问题,结合具有分层(hierarchical)结构的自动机模型,提出了分层离散事件系统的不透明性.对分层离散事件系统进行标准化,给出了分层离散事件系统不透明性和K延迟不透明性两个概念.为了对分层离散事件系统的两种不透明性进行验证,分别构造了两种相应的不透明性验证器,得到了关于不透明性和K延迟不透明性的充分必要条件,并对构造不透明性验证器的复杂度进行了分析.     

离散事件系统不透明性监督控制研究综述

离散事件系统不透明性是指外部观察者无法分辨系统的一系列行为是否为系统所发生的.而离散事件不透明性监督控制则是构建监督器控制系统行为,使系统满足不透明性的一种方法.离散事件系统不透明性与信息安全有着紧密的联系,并得到了广泛的应用.首先对离散事件系统做了简要的概述,然后介绍了不透明性监督控制算法的研究现状,最后进行了总结和展望.     

不完备离散事件系统的可诊断性

在离散事件系统的建模过程中,由于系统行为的复杂,存在物理系统向逻辑系统映射的不完全性,因此产生了不完备模型的概念.提出在模型不完备的前提下,判断模型可诊断性的方法.提出可诊断性的在线判定方法,同时将不完备的行为加入模型,使模型完备.用经典的双树方法判断离线可诊断性,根据观测序列的时序及语言的前缀判断并处理不完备行为.提出判定不完备行为的方法,向模型中添加不完备行为,并根据不完备行为增量地在双树中判定在线可诊断性.通过在线的可诊断性判定,当前系统能够得到在有限观测内唯一判定故障发生与否的结论.该方法适用于具有离散性质的系统.     

基于粗糙集理论的离散事件系统不透明性的验证算法

近年来,离散事件系统的不透明性研究引起了国内外众多学者的广泛关注.本文针对离散事件系统的不透明性,提出了一种将粗糙集理论作为知识提取工具来处理离散事件系统不透明性验证的方法.先对离散事件系统的不透明性进行形式化,再利用粗糙集理论对离散事件系统以信息表及决策表的形式进行表示,得到一个关于离散事件系统不透明性的充分必要条件.在此基础上,给出一个验证离散事件系统不透明性算法.与现有方法相比,该验证算法既适用于对强不透明性的验证,又适用于对弱不透明性的验证,并且在时间复杂度上也有较明显改进.     

基于扩展窗口的时序不完备诊断方法研究

离散事件系统诊断中,由于系统复杂度较高,对系统建模时要获得系统的完备行为非常困难。传统的诊断方法往往基于模型完备的假设,在模型不完备时会出现得不到诊断解释的问题。针对模型定义不完备中的一种情况——事件顺序定义不完备,提出了一种基于扩展窗口的时序不完备诊断方法,该方法利用相关事件无序信息,在增量诊断时通过动态改变观测窗口大小,结合两个观测窗口的观测序列,在一定程度上解决了不完备的诊断问题。该方法不仅扩展了模型完备条件的约束,得到了合理的诊断结果,而且改进了观测延迟导致的观测乱序情况,扩大了模型诊断的适用范围。最后,通过算法分析和实验结果证明该诊断方法在复杂度较低的情况下能够得到合理的诊断结果。     

不完备模型下的离散事件系统诊断方法

提出不完备模型的两种不完备性:模型定义不完备和因果关系不完备.在模型定义不完备条件下,用在线观测与模型共同约束的方法处理观测乱序及未定义事件,得到可行的诊断轨迹.相对于基于完备模型假设下不能诊断的结论,该方法扩展了诊断方法的适用范围,放松了对模型的约束要求,在因果不完备条件下,提出用因果图联系部件,解决分布式诊断中由于部件独立建模而导致的不彻底诊断,提高了诊断的精确性.通过实验验证,两种条件下的诊断方法均能在相应的不完备模型中得到预期诊断结果,并对模型进行局部修订,提高模型的完备性.     

离散事件系统的学习控制初探

本文建立了包含状态持续时间的离散事件系统的自动机模型，然后引入学习算法用以确定描述闭环系统进行了语言K,从而当控制指标是以时间形式给出时，仍能利用监控理论来设计散事件系统。     

赋时离散事件系统的安全诊断

针对一类计时或非计时自动机模型,研究其赋时离散事件系统(TDES)故障诊断的安全性问题.首先对TDES的安全可诊断性进行形式化;然后通过构造一个非法语言识别器对被禁止危险操作序列进行识别,在此基础上构建一个安全诊断器,提出一种基于安全诊断器的安全诊断方法,并得到一个关于TDES安全可诊断性的充分必要条件,从而实现TDES的安全故障诊断.     

基于动态观测的随机离散事件系统故障诊断

近年来,离散事件系统故障诊断研究引起国内外学者广泛关注.鉴于此,研究动态观测下随机离散事件系统的故障诊断.首先引入一种动态观测,使事件的可观测性随着系统的运行而动态变化;然后分别对基于动态观测的随机离散事件系统的单故障可诊断性和模式故障可诊断性进行形式化;最后通过构造相应的诊断器,分别得到关于单故障可诊断性和模式故障可...     

离散事件系统规范DEVS研究

离散事件系统是一类常见的系统,如何对这类系统进行描述与建模是离散事件系统仿真研究的核心内容。离散事件系统规范DEVS是一种离散事件系统形式化描述方法,它具有层次化和模块化的特点,利用该方法可对复杂的离散事件系统进行建模、设计、分析和仿真。该文详细介绍了DEVS基本模型和耦合模型,给出了DEVS在耦合运算下的封闭性构造证明,并提出了一种具有嵌套层次结构的DEVS耦合模型实现算法,该算法对基于DEVS描述的离散事件系统的仿真实现具有一定参考价值。     

LEARNING EVENTS FROM SIMULATION MODEL RUNS: FROM PROCEDURAL MODELSTO RELATIONAL ABSTRACTIONS

Simulation models are largely procedural and are proposed to be used to coordinate the-actions of an autonomous system. The problem, however, is the fact that procedural models lack a well-developed reasoning system. This paper suggests that relational representations, which have a relatively well-developed logical basis for deductive reasoning, be extracted from simulation models and be used as metaknowledge to reason about aspects of simulated behavior generated by procedural model simulation runs.     

On optimal and reasonable control in the presence of adversaries

This paper constitutes a sketch of a unified framework for posing and solving problems of optimal control in the presence of uncontrolled disturbances. After laying down the general framework we look closely at a concrete instance where the controller is a scheduler and the disturbances are related to uncertainties in task durations.     

基于Petri网的故障诊断研究理论的综述

故障诊断是离散事件系统中一项重要的研究内容,对于保障系统安全具有积极意义。基于Petri网的故障诊断相关研究主要分为故障可诊断性研究以及故障诊断器的构造理论研究,故障可诊断性又可以进一步分为一般可诊断性与K-可诊断性,而故障诊断器的设计方法又可以按照适用系统类型进一步分类。综述了故障诊断理论中可诊断性、K可诊断性的各类研究方法和研究结论,介绍了离散Petri网系统、连续Petri网系统和分布式Petri网系统中故障诊断器的设计方法,并对各类方法的特点进行了重点分析。最后,给出了基于Petri网的故障诊断进一步研究的方向与应用难点,其对今后研究有一定的指导意义。     

Synthesis of insertion functions for enforcement of opacity security properties

Opacity is a confidentiality property that characterizes whether a “secret” of a system can be inferred by an outside observer called an “intruder”. In this paper, we consider the problem of enforcing opacity in systems modeled as partially-observed finite-state automata. We propose a novel enforcement mechanism based on the use of insertion functions. An insertion function is a monitoring interface at the output of the system that changes the system’s output behavior by inserting additional observable events. We define the property of “i-enforceability” that an insertion function needs to satisfy in order to enforce opacity. I-enforceability captures an insertion function’s ability to respond to every system’s observed behavior and to output only modified behaviors that look like existing non-secret behaviors. Given an insertion function, we provide an algorithm that verifies whether it is i-enforcing. More generally, given an opacity notion, we determine whether it is i-enforceable or not by constructing a structure called the “All Insertion Structure” (AIS). The AIS enumerates all i-enforcing insertion functions in a compact state transition structure. If a given opacity notion has been verified to be i-enforceable, we show how to use the AIS to synthesize an i-enforcing insertion function.     

Diagnosis of Discrete Event Systems Using Decentralized Architectures

Decentralized diagnosis of discrete event systems has received a lot of attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. This paper casts the problem of decentralized diagnosis in a new hierarchical framework. A key feature is the exploitation of different local decisions together with appropriate rules for their fusion. This includes local diagnosis decisions that can be interpreted as “conditional decisions.” Under this new framework, a series of new decentralized architectures are defined and studied. The properties of their corresponding notions of decentralized diagnosability are characterized and their relationship with existing work described. Corresponding verification algorithms are also presented and on-line diagnosis strategies discussed.

离散事件系统部分可诊断性分析

针对离散事件系统部分可诊断性问题,提出一种量化评价与分析方法。该方法以树状结构的故障模型为基础,引入可诊断度与可诊断深度指标,能够从可诊断故障覆盖程度与精确程度两个方面对系统可诊断性进行评价,其优点是评价结果量化表示,能为部分可诊断系统的进一步评价、分析与对比提供参考。此外,还讨论了故障模型对系统可诊断度与可诊断深度两个评价指标的影响,并给出了故障模型构造的一般原则。实例分析与讨论结果表明,所提出的可诊断度与可诊断深度指标能够准确反映系统在特定故障模型下的部分可诊断状态。所提出的部分可诊断性评价方法能为基于离散事件模型的复杂系统设计与评价提供依据,并能够进一步为智能、自适应和自愈系统的设计提供参考。     

Model-based detection of routing events in discrete flow networks

A theoretical framework and its practical implications for formulating and implementing model-based monitoring of discrete flow networks are discussed. Possible flows of items are described as discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path, visiting tracking locations within the monitored system. To deal with alternative routing, creation of items, flow bifurcations and convergences are allowed. Given the set of possible discrete flows, a possible-behavior model—an interacting set of automata—is constructed, where each automation models the item discrete flow at each tracking location. In this model, which assumes total observability, event labels or symbols contain all the information required to unambiguously distinguish each discrete movement. Within the possible behavior, there is a special sub-behavior whose occurrence is required to be detected. The special behavior may be specified by the occurrence of routing events, such as faults or route violations, for example. These intermittent or non-persistent events may occur repeatedly. An observation mask is then defined, characterizing the observation configuration available for collecting item tracking data. The verification task is to determine whether this observation configuration is capable of detecting the specified special behavior. The assessment is accomplished by evaluating several observability notions, such as detectability and diagnosibility. If the corresponding property is satisfied, associated formal observers are constructed to perform the monitoring task at hand. The synthesis of observation masks may also be conducted to suggest optimal observation configurations (specifying number, type, and tracking locations of observation devices) guaranteeing the detection of special events and to construct associated monitoring agents. The developed framework, modeling methodology, and supporting techniques for defining and implementing discrete flow monitoring of entity movements are presented and illustrated with examples.