基于成对约束的主动半监督文本聚类

在主动选取成对约束方法的基础上,提出一种基于成对约束的主动半监督文本聚类方法.利用潜在语义索引方法对文本特征空间进行降维,在聚类过程中,采用构造的约束选取方法主动地选取成对约束信息,并利用选取的成对约束信息指导文本聚类.实验结果表明,该方法能利用少量的监督信息提高文本聚类的分类准确率.     

基于故障树分析的监测点选取

监测点的选取对监测诊断系统的设计至关重要.在雷达设备监测与诊断系统实现中,提出了基于故障树分析的监测点选取方法,并通过实例验证了该方法的有效性.用该方法进行监测点的选取,可以减小监测系统的复杂性,避免诊断推理过程的盲目性,提高故障诊断的效率.     

用辩论刻画含约束的诊断空间

在含约束的极小诊断故障部件的每个超集并非总能构成一个含约束的诊断。作者把基于模型的含约束的诊断比喻成一个辩论过程;一些部件可以指责某些部件出了故障;反过来这些部件也可以为自己辩护。在作者所建立的辩论框架下含约束的诊断空间得到了紧致的刻画。与相关工作相比,这种方法具有计算上的优势,它也可以解决极小诊断假设问题。     

面向远程诊断的AI-ESTATE智能诊断系统研究动态

通过封装服务、规范标准服务,AI-EATATE为诊断系统的客户端和应用对象的扩展、测试与诊断的分离提供了条件,使其更适合远程控制的诊断系统的构建。论文分析了武器装备远程故障诊断对知识重用与共享、软件互操作的需求,提出了面向远程诊断的AI-ESTTAE智能诊断系统构建的方法;研究了AI-ESTATE智能诊断系统的基本结构和框架,讨论了AI-ESTATE在发展过程中其服务的变化对开放、远程智能诊断系统构建的影响;最新的AI-ESTATE标准规范的服务更加强了对诊断过程的控制和效果评估,论文采用状态图的方式分析了最新的AI-ESTTAE服务在远程故障诊断过程的作用,为符合AI-ESTATE标准的远程故障诊断系统的开发提供了方法。     

基于约束放松的自适应Web服务选取方法的研究

已有服务选取都是把问题规划为针对某个模型寻求最优解或者次优解的过程,不考虑用户约束对Web服务选取的影响,而在SLA模型中,用户对服务的选取是有约束的,如果约束不满足,选取算法直接结束,要求用户重新给出约束定义,增加了用户与选取过程的交互.为了减少用户的操作次数,本文为用户的约束定义了约束放松模型,对于约束不能满足的情况下,自动地对已有模型进行条件放松,定义了基于分布式约束满足问题求解的服务选取过程,将约束放松模型应用于选取过程,进行自适应的选取.通过与已有的选取算法进行实验比较,本文提出的自适应组合服务选取算法,考虑了用户的约束条件,在选取失败时,基于放松模型进行自动约束条件的更新,进行服务的重新选取,减少了与用户交互次数,有效提高了有约束的组合服务选取的执行效率.     

基于定性定量知识的故障诊断

提出一种将检测中的定量信息定性化的方法,并应用QSIM的算法约束的概念。结合区间代数的计算规则,设计了定性与定量结合的诊断系统,缩小诊断空间,增大定性仿真在故障诊断中的应用范围。通过不断扩大先验故障模型,使系统具备一定的学习能力,并以压缩制冷系统为例进行了诊断,验证了系统的正确性。     

基于特征参数空间的交互约束自动求解方法*

提出了一种新的约束求解方法求解特征交互过程中两个特征形状在空间上发生重叠的约束。这个约束求解器在特征模型的参数空间进行取样的基础之上,利用蒙特卡罗技术来减少样本数据量。该方法不但可以产生一组正确的几何约束,而且提高了效率约束转换的效率。     

多智能体的分布式智能故障诊断

基于分布式人工智能的思想 ,将多Agent技术引入复杂故障诊断领域 ,分析了基于MAS的分布式智能故障诊断方法和过程 ;讨论了基于模式聚类的故障求解机制及对诊断问题任务辨识、分解 ;研究了多Agent宏观上的约束和关联 ;设计了应用Agent工作状态的表达机制 ;确定了应用Agent间的工作状态影响关系及多Agent间的交互、协作和通讯 ;构建了多Agent模糊关联模型 ;给出了多Agent诊断系统局部诊断决策与全局诊断决策的集成描述结构 ;建立了一种分布式Agent诊断系统结构及其原型系统 .在某电力企     

远程诊断系统中两个关键问题的代数建模

基于网络的远程监测诊断系统是一个复杂的系统,其实施需要进行理性的建模分析.针对基于网络的远程监测诊断系统的实现和分析问题,利用了π演算以及δπ演算这一高度抽象的适于移动分布计算模式的代数建模工具,完成了基于网络的远程监测诊断系统的两个诊断特征问题：移动诊断和群组诊断的建模.建模结果表明δπ演算能很好地表达基于网络的远程监测诊断系统的这两个特征问题.     

基于双尺度约束模型的BN结构自适应学习算法

在无先验信息的情况下, 贝叶斯网络(Bayesian network, BN)结构搜索空间的规模随节点数目增加呈指数级增长, 造成BN结构学习难度急剧增加. 针对该问题, 提出基于双尺度约束模型的BN结构自适应学习算法. 该算法利用最大互信息和条件独立性测试构建大尺度约束模型, 完成BN结构搜索空间的初始化. 在此基础上设计改进遗传算法, 在结构迭代优化过程中引入小尺度约束模型, 实现结构搜索空间小尺度动态缩放. 同时, 在改进遗传算法中构建变异概率自适应调节函数, 以降低结构学习过程陷入局部最优解的概率. 仿真结果表明, 提出的基于双尺度约束模型的BN结构自适应学习算法能够在无先验信息的情况下保证BN结构学习的精度和迭代寻优的收敛速度.     

电传飞行控制系统MBIT故障诊断专家系统设计

针对电传飞控系统的特点和维护性要求,介绍了MBIT故障诊断专家系统的设计思路及实现方法。阐述了MBIT的功能与用途。构建了电传飞控系统MBIT故障诊断专家系统的体系结构,深入研究了如何建立故障知识库、制定推理机制、组建综合数据库和决策解释,并结合某型号,给出了具体的示例。工程实际使用过程中,利用本系统可以全面地对电传飞控系统进行有效的维护。     

VERIFICATION OF QUALITATIVE PROPERTIES OF RULE-BASED EXPERT SYSTEMS

Frequently, expert systems are developed to operate in dynamic environments where they must reason about time-varying information and generate hypotheses, conclusions, and process inputs that can drastically influence the environment within which they operate. For instance, expert systems used for fault diagnosis and fault accommodation in nuclear power plants reason over sensor data and operator inputs, form fault hypotheses, make recommendations pertaining to safe process operation, and in crisis situations, could generate command inputs to the process to help maintain safe operation. Clearly, there is a pressing need to verify and certify that such expert systems are dependable in their operation and can reliably maintain adequate performance levels. In this article we develop a mathematical approach to verifying qualitative properties of rule-based expert systems that operate in dynamic and uncertain environments. First, we provide mathematical models for the expert system (including the knowledge-base and inference engine) and for the mechanisms for interfacing to the user inputs and the dynamic process. Next, using these mathematical models, we show that while the structure and interconnection of information in the knowledge base influence the expert system's ability to react appropriately in a dynamic environment, the qualitative properties of the full knowledge-base/inference engine loop must be considered to fully characterize an expert system's dynamic behavior. To illustrate the verification approach, we show how to model and analyze the qualitative properties of rule-based expert systems that solve a water-jug filling problem and a simple process control problem. Finally, in our concluding remarks, we highlight some limitations of our approach and provide some future directions for research.     

基于故障树的远程故障诊断任务分解和决策方法

远程故障诊断对提高飞机飞行安全、降低诊断和维护成本、建立更好的飞机维护环境具有重要作用,在远程故障诊断过程中,面对复杂的诊断任务和冗余、不确定的决策如何做出合理的任务分解和最优的诊断决策是个重要的研究课题。以提高诊断效率和可靠性,本文提出了一种基于故障树模型的任务分解与决策融合方法。首先,描述了多资源远程诊断任务分解问题;其次,建立了基于故障树最小割集的诊断任务分解机制;最后,提出了基于D-S证据理论的决策级信息融合方法,并通过实际算例验证了任务分解和决策方法的可行性。     

Adaptive fault‐tolerant control of an axially moving system with time‐varying constraints

In this paper, for an axially moving system, with the purpose of suppressing vibration, an adaptive fault‐tolerant control method with time‐varying constraints is investigated. The dynamics of the system are comprised of an ordinary differential equation coupled with a partial differential equation. The method used in our study is known as fault‐tolerant control to process affairs of actuator failures occur. Actual control substitutes for ideal control to regulate the vibration when the actuator occurs undesired failures. Time‐varying constraints are appropriate to cope with the variation range of the performance. Lyapunov function has proven that the adaptive control law is feasible, as well as verifying the exponential stability of the system. Eventually, simulations we have done suggest that the effectiveness of the designed control is feasible.     

Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour

This paper describes an on-line fault diagnosis system which diagnoses faults in a pilot scale mixing process using on-line measurements. Fault detection and fault diagnosis is performed based on a qualitative model of the mixing process. The qualitative model provides a set of constraints for the system being diagnosed. Once it is violated, a particular fault is detected. Since most of the information used by the diagnosis system comes from on-line measurements, it is important to determine whether sensors are working normally or not before considering failures of other components. Sensor failure is mainly diagnosed from heuristic considerations, while the failures of other components are diagnosed from a procedure of hypothesis generation, qualitative simulation, and comparison. Based on a hypothesis, the behaviour of the system being diagnosed is simulated from its qualitative model and is compared with the actual measurements. Depending upon whether they conflict or not, the hypothesis is denied or retained. A new approach for reducing the ambiguity in qualitative simulation is described. Ambiguity is reduced by taking account of the information on the order of magnitude relations between different physical variables.     

标签Petri网的路径信息在故障诊断中的应用

针对一类由标签Petri网模拟的离散事件系统的故障问题,提出一种利用网结构中路径信息的诊断方法.假设标签Petri网模型的网结构是已知的,隐藏在系统传感器或者执行机构中的故障用不可观测的变迁或者可观测且标记不可分辨的变迁模拟.首先,在故障诊断过程前,提出网结构转化规则转化原始Petri网;随后,在转化后Petri网的子...     

Rapid hypothesis generation by combining residual sorting with local constraints

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.     

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible. This paper is a revised and extended version of [3].