Search results for "Markov process"

showing 10 items of 147 documents

Fuzzy Distributed Genetic Approaches for Image Segmentation

2010

This paper presents a new image segmentation algorithm (called FDGA-Seg) based on a combination of fuzzy logic, multiagent systems and genetic algorithms. We propose to use a fuzzy representation of the image site labels by introducing some imprecision in the gray tones values. The distributivity of FDGA-Seg comes from the fact that it is designed around a MultiAgent System (MAS) working with two different architectures based on the master-slave and island models. A rich set of experimental segmentation results given by FDGA-Seg is discussed and compared to the ICM results in the last section.

Markov random fieldGeneral Computer ScienceComputer sciencebusiness.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationMarkov processImage processingImage segmentationFuzzy logicsymbols.namesakeGenetic algorithmsymbolsSegmentationArtificial intelligencebusinessJournal of Computing and Information Technology
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Diferenciālvienādojumu ar Markova impulsu atgriezenisko saiti asimptotiskās analīzes robežteorēmas

1999

Matemātiskā analīzeDifferential equationMarkov processesAsymptotic methods:MATHEMATICS::Algebra geometry and mathematical analysis::Mathematical analysis [Research Subject Categories]DiferenciālvienādojumiAsimptotiskā analīzeMarkova procesi
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Performance modeling of epidemic routing

2006

In this paper, we develop a rigorous, unified framework based on ordinary differential equations (ODEs) to study epidemic routing and its variations. These ODEs can be derived as limits of Markovian models under a natural scaling as the number of nodes increases. While an analytical study of Markovian models is quite complex and numerical solution impractical for large networks, the corresponding ODE models yield closed-form expressions for several performance metrics of interest, and a numerical solution complexity that does not increase with the number of nodes. Using this ODE approach, we investigate how resources such as buffer space and the number of copies made for a packet can be tra…

Mathematical optimizationComputingMethodologies_SIMULATIONANDMODELINGComputer Networks and CommunicationsDifferential equationComputer scienceWireless ad hoc networkNetwork packetNumerical analysisMathematicsofComputing_NUMERICALANALYSISOdeMarkov processMarkov modelsymbols.namesakeOrdinary differential equationMetric (mathematics)symbolsRouting (electronic design automation)ScalingSimulation
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Cross-entropy-based adaptive optimization of simulation parameters for Markovian-driven service systems

2005

Abstract Markov fluid models represent a general description of the process of service request arrivals to service systems. The solution of performance analysis problems incorporating them often calls for a simulation approach, for which a reference methodology is Importance Sampling. However, in this case the appropriate choice of the biasing conditions is a problem in itself. In this paper an iterative method based on the cross-entropy is proposed for this choice. The equations are given that allow to derive the biasing conditions from the simulation itself. The application of the proposed method to three different sample cases, referring to one transient scenario (finite time horizon and…

Mathematical optimizationImportance samplingMarkov chainIterative methodComputer scienceAdaptive optimizationSettore ING-INF/03 - TelecomunicazioniMarkov processSimulation techniquesCross-entropy; Importance sampling; Markov fluid models; Rare event simulation; Simulation techniquesMarkov fluid modelssymbols.namesakeRare event simulationCross entropyHardware and ArchitectureControl theoryModeling and SimulationPath (graph theory)symbolsTransient (computer programming)Cross-entropySoftwareImportance sampling
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Approximate survival probability determination of hysteretic systems with fractional derivative elements

2018

Abstract A Galerkin scheme-based approach is developed for determining the survival probability and first-passage probability of a randomly excited hysteretic systems endowed with fractional derivative elements. Specifically, by employing a combination of statistical linearization and of stochastic averaging, the amplitude of the system response is modeled as one-dimensional Markovian Process. In this manner the corresponding backward Kolmogorov equation which governs the evolution of the survival probability of the system is determined. An approximate solution of this equation is sought by employing a Galerkin scheme in which a convenient set of confluent hypergeometric functions is used a…

Mathematical optimizationMonte Carlo methodAerospace EngineeringBilinear interpolationMarkov processOcean Engineering02 engineering and technology01 natural sciencesHysteretic systemsymbols.namesake0203 mechanical engineering0103 physical sciencesApplied mathematicsHypergeometric functionGalerkin method010301 acousticsCivil and Structural EngineeringMathematicsGalerkin approachMechanical EngineeringStatistical and Nonlinear PhysicsFractional derivativeCondensed Matter PhysicsOrthogonal basisFractional calculus020303 mechanical engineering & transportsAmplitudeNuclear Energy and EngineeringsymbolsSurvival probabilitySettore ICAR/08 - Scienza Delle Costruzioni
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On risk sensitive control of regular step Markov processes

2001

Mathematical optimizationsymbols.namesakeApplied MathematicsStatisticssymbolsMarkov processRisk sensitiveControl (linguistics)Markov modelMathematicsApplicationes Mathematicae
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Stochastic anticipative calculus on the path space over a compact Riemannian manifold

1998

Abstract In this paper, we shall first give another expression for Cruzeiro-Malliavin structure equation, by means of the Skorohod integral. The torsion tensor with respect to the Markovian connection used in [CF] is computed. This is the key step to establish a Stroock-like formula of commutation on the derivative of the Skorohod integral, which enables us to prove an Ito formula. As an application, we shall give a maximal inequality for Skorohod integrals following [AN2].

Mathematics(all)General MathematicsApplied MathematicsMathematical analysisMarkov processDerivativeExpression (computer science)Riemannian manifoldConnection (mathematics)symbols.namesakeTorsion tensorMathematics::ProbabilitysymbolsPath spaceCommutationMathematicsJournal de Mathématiques Pures et Appliquées
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Analysis and modeling of Temporal Dominance of Sensations with stochastic processes

2019

Temporal Dominance of Sensations (TDS) is a technique to measure temporal perception of food product during tasting. For a panelist, it consists in choosing in a list of attributes which one is dominant at any time. This work aims to model TDS data with a stochastic process and proposes to use semi-Markov processes (SMP), a generalization of Markov chains which allows dominance durations to be modeled by any type of distribution. The model can then be used to compare TDS samples based on likelihood ratio. Because probabilities of transition from one attribute to another one can also depend on time, we propose to model TDS by period and we propose a method to select optimally the number of p…

Modèles de mélangeDominance Temporelle des SensationsAnalyse sensorielleProcessus semi-Markoviens[SCCO.COMP] Cognitive science/Computer scienceSemi-Markov processesSensory analysisTemporal Dominance of SensationsMixture models
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Analysis and modeling of Temporal Dominance of Sensations with stochastic processes

2019

Temporal Dominance of Sensations (TDS) is a technique to measure temporal perception of food product during tasting. For a panelist, it consists in choosing in a list of attributes which one is dominant at any time. This work aims to model TDS data with a stochastic process and proposes to use semi-Markov processes (SMP), a generalization of Markov chains which allows dominance durations to be modeled by any type of distribution. The model can then be used to compare TDS samples based on likelihood ratio. Because probabilities of transition from one attribute to another one can also depend on time, we propose to model TDS by period and we propose a method to select optimally the number of p…

Modèles de mélange[SHS.STAT]Humanities and Social Sciences/Methods and statisticsProcessus semi-MarkoviensTemporal Dominance of Sensations (TDS)[SCCO.COMP]Cognitive science/Computer scienceSensory analysis[SDV.AEN] Life Sciences [q-bio]/Food and NutritionDominance Temporelle des Sensations[SCCO.COMP] Cognitive science/Computer scienceAnalyse sensorielle[SHS.STAT] Humanities and Social Sciences/Methods and statisticsSemi-Markov processesMixture modelsTemporal Dominance of Sensations[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
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Ideal and physical barrier problems for non-linear systems driven by normal and Poissonian white noise via path integral method

2016

Abstract In this paper, the probability density evolution of Markov processes is analyzed for a class of barrier problems specified in terms of certain boundary conditions. The standard case of computing the probability density of the response is associated with natural boundary conditions, and the first passage problem is associated with absorbing boundaries. In contrast, herein we consider the more general case of partially reflecting boundaries and the effect of these boundaries on the probability density of the response. In fact, both standard cases can be considered special cases of the general problem. We provide solutions by means of the path integral method for half- and single-degr…

Monte Carlo methodMarkov processProbability density function02 engineering and technologyWhite noise01 natural sciencesBarrier crossingsymbols.namesake0203 mechanical engineeringStructural reliability0103 physical sciencesBoundary value problem010301 acousticsMathematicsApplied MathematicsMechanical EngineeringMathematical analysisFokker-Planck equationWhite noisePath integrationNonlinear system020303 mechanical engineering & transportsMechanics of MaterialsPath integral formulationsymbolsFokker–Planck equationRandom vibration
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