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Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis  [PDF]
Luigi Portinale
Computer Science , 2013,
Abstract: Although the notion of diagnostic problem has been extensively investigated in the context of static systems, in most practical applications the behavior of the modeled system is significantly variable during time. The goal of the paper is to propose a novel approach to the modeling of uncertainty about temporal evolutions of time-varying systems and a characterization of model-based temporal diagnosis. Since in most real world cases knowledge about the temporal evolution of the system to be diagnosed is uncertain, we consider the case when probabilistic temporal knowledge is available for each component of the system and we choose to model it by means of Markov chains. In fact, we aim at exploiting the statistical assumptions underlying reliability theory in the context of the diagnosis of timevarying systems. We finally show how to exploit Markov chain theory in order to discard, in the diagnostic process, very unlikely diagnoses.
动态故障诊断中的立体因果建模与不确定性推理方法
Cubic causality modeling and uncertain inference method for dynamic fault diagnosis
 [PDF]

董春玲,赵越,张勤
- , 2018, DOI: 10.16511/j.cnki.qhdxxb.2018.26.029
Abstract: 为满足复杂系统的动态、实时和高可靠性的故障诊断需求,克服动态不确定因果图(dynamic uncertain causality graph,DUCG)及其他概率图模型的局限,该文在DUCG理论的基础上扩展其时序因果表达与推理方法,建立了立体DUCG (Cubic DUCG)理论模型。采用动态的手段处理动态问题,以"逐步生长"的立体因果建模取消了时序模型中常见的Markov假设限制,以穿越式因果连接准确地表达动态系统下故障的产生、演变和发展;直观地刻画和处理动态负反馈等复杂故障逻辑因果关系;给出了严谨、高效的动态推理算法。宁德核电站1号机组CPR1000模拟机二回路系统上的故障实验结果表明:Cubic DUCG诊断推理准确、高效,能有效处理负反馈等复杂动态情形。
Abstract:Complex systems need dynamic, real-time, reliable fault diagnostics but current methods have some shortcomings. This paper expands the dynamic uncertain causality graph method (DUCG) for temporal causality modeling and reasoning theory to correct the limits of the DUCG method and other probabilistic graphical models. A Cubic DUCG is developed that is characterized by a true dynamic model of dynamic problems. The cubic causality graph abandons the restriction of the Markov assumption usually used in temporal models with the fault formation, evolution, and development in dynamic systems represented by allowing causal connections to penetrate among any number of time-slices. The negative feedback dynamics is modelled intuitively combined with a reliable dynamic inference algorithm. Fault tests on the secondary loop of Ningde Nuclear Power Plant Unit 1 (CPR1000) simulator show that Cubic DUCG is accurate, efficient, and capable of dealing with the complex dynamics including negative feedback.
Modeling uncertain and vague knowledge in possibility and evidence theories  [PDF]
Didier Dubois,Henri Prade
Computer Science , 2013,
Abstract: This paper advocates the usefulness of new theories of uncertainty for the purpose of modeling some facets of uncertain knowledge, especially vagueness, in AI. It can be viewed as a partial reply to Cheeseman's (among others) defense of probability.
The R Package bgmm : Mixture Modeling with Uncertain Knowledge  [PDF]
Przemys law Biecek,Ewa Szczurek,Martin Vingron,Jerzy Tiuryn
Journal of Statistical Software , 2012,
Abstract: Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subsetof observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package calledbgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model.
Modeling, optimizing and simulating robot calibration with accuracy improvement
Motta, José Maurício S. T.;Mcmaster, R. S.;
Journal of the Brazilian Society of Mechanical Sciences , 1999, DOI: 10.1590/S0100-73861999000300002
Abstract: this work describes techniques for modeling, optimizing and simulating calibration processes of robots using off-line programming. the identification of geometric parameters of the nominal kinematic model is optimized using techniques of numerical optimization of the mathematical model. the simulation of the actual robot and the measurement system is achieved by introducing random errors representing their physical behavior and its statistical repeatability. an evaluation of the corrected nominal kinematic model brings about a clear perception of the influence of distinct variables involved in the process for a suitable planning, and indicates a considerable accuracy improvement when the optimized model is compared to the non-optimized one.
Modeling, optimizing and simulating robot calibration with accuracy improvement  [cached]
Motta José Maurício S. T.,Mcmaster R. S.
Journal of the Brazilian Society of Mechanical Sciences , 1999,
Abstract: This work describes techniques for modeling, optimizing and simulating calibration processes of robots using off-line programming. The identification of geometric parameters of the nominal kinematic model is optimized using techniques of numerical optimization of the mathematical model. The simulation of the actual robot and the measurement system is achieved by introducing random errors representing their physical behavior and its statistical repeatability. An evaluation of the corrected nominal kinematic model brings about a clear perception of the influence of distinct variables involved in the process for a suitable planning, and indicates a considerable accuracy improvement when the optimized model is compared to the non-optimized one.
Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments  [PDF]
D. Goulet
Quantitative Biology , 2015,
Abstract: In many chemical and biological applications, systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data. The DEs are typically nonlinear and difficult to analyze, requiring numerical methods to approximate the solutions. Compounding this difficulty are the unknown parameters in the DE system, which must be given specific numerical values in order for simulations to be run. Estrogen receptor protein dimerization is used as an example to demonstrate model construction, reduction, simulation, and parameter estimation. Mathematical, computational, and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling. The process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models.
Modeling and Simulating for Emergency Medical Service System Optimizing Based on Discrete Event System Theory  [PDF]
Tian Xie, Yaoyao Wei, Leilei Pan, Tieli Wang, Hairong Chen
Open Journal of Social Sciences (JSS) , 2016, DOI: 10.4236/jss.2016.47022
Abstract: Due to the complexity and randomness of emergency demands, it is difficult to describe and analyze the Emergency Medical Service Systems (EMSS) just by certain modeling. Therefore, a stochastic modeling and simulating method for EMSS optimizing is proposed based on the Discrete Event System theory. With a fictive and stochastic medical emergency scenario, the relevant simulation model is constructed with the FLEXSIM software quickly and explicitly. And by simulating and analyzing, it is easily to discover the bottlenecks and to optimize the EMSS system.
A Comparison of the Mathematical Modeling Methods in the Inventory Systems under Uncertain Conditions  [PDF]
A. Mirzazadeh
International Journal of Engineering Science and Technology , 2011,
Abstract: The inventory models, generally, are derived with considering two methods: (1) minimizing the average annual cost or (2) minimizing the discounted cost. This paper compares the optimal ordering policies determined by these methods under uncertain inflationary situations. The inventory and shortages behavior have been analyzed with using the differential equations. The numerical examples are used to illustrate the theoretical results. A detailed analysis on the models parameters has been performed and some management insights are presented. The results show that there is a negligible difference between two procedures for wide range values of the parameters.
Behavior Modeling in Uncertain Information Systems by Fuzzy-UML
Ali Haroonabadi,Mohammad Teshnehlab
International Journal of Soft Computing , 2012,
Abstract: Because of the natural essence of requirements, uncertainty in information systems is unavoidable. To develop the systems (analyze, design, implementation, test) object oriented methods describes their concepts, with UML language. UML as a standard object oriented language has ability to investigate the different aspects of behavioral and structural in software engineering. If enter the uncertainty in UML, the extended version named Fuzzy-UML will be produced. This developed version includes both of structure and behavior sights. Because of the lack of UML ability support for evaluation, the necessity of transforming the pragmatic model of (UML) system to the formal model (Petri Nets) is considered (because of being uncertain of the information, the pragmatic model transforms to the Fuzzy Petri Net formal model). We will have the ability of evaluating the complex systems, with preparing the proper model of system.
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