Search results for "state space"

showing 10 items of 49 documents

Modelling and Analysis of Flow Rate and Pressure Head in Pipelines

2019

Currently, various approaches with several utilities are proposed to identify damage in the pipeline. The pipeline system is modeled in the form of a distributed parameter system, such that the state space related to the distributed parameter system contains infinite dimension. In this paper, a novel technique is proposed to analyze and model the flow in the pipeline. Important theorems are proposed for testing the observability as well as controllability of the proposed model.

010504 meteorology & atmospheric sciencesComputer sciencePipeline (computing)05 social sciences01 natural sciencesControllabilityPipeline transportPressure headFlow (mathematics)Distributed parameter systemControl theory0502 economics and businessState spaceObservability050207 economics0105 earth and related environmental sciences2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
researchProduct

Pipeline Monitoring Architecture Based on Observability and Controllability Analysis

2019

Recently many techniques with different applicability have been developed for damage detection in the pipeline. The pipeline system is designed as a distributed parameter system, where the state space of the distributed parameter system has infinite dimension. This paper is dedicated to the problem of observability as well as controllability analysis in the pipeline systems. Some theorems are presented in order to test the observability and controllability of the system. Computing the rank of the controllability and observability matrix is carried out using Matlab.

0209 industrial biotechnologyRank (linear algebra)Computer sciencePipeline (computing)020208 electrical & electronic engineering02 engineering and technologyPipeline transportControllability020901 industrial engineering & automationControl theoryDistributed parameter system0202 electrical engineering electronic engineering information engineeringState spaceObservabilityMATLABcomputercomputer.programming_language2019 IEEE International Conference on Mechatronics (ICM)
researchProduct

Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension

2016

We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For th…

0209 industrial biotechnologyStochastic stabilityMathematical optimizationCollective behaviorTechnologyComputer sciencePopulationcontrol designcrowd-averse robust mean-field games state space extension dynamic agents linear stochastic differential equation Brownian motion adversarial disturbance cost functional cross-coupling mean-field term collective behavior stock market application production engineering example dynamic demand management problem robust mean-field game approximation error stochastic stability microscopic dynamics macroscopic dynamicscontrol engineering02 engineering and technology01 natural sciencesStochastic differential equationoptimal control020901 industrial engineering & automationQuadratic equationAutomation & Control SystemsEngineeringClosed loop systemsSettore ING-INF/04 - AutomaticaApproximation errorRobustness (computer science)Control theory0102 Applied MathematicsState space0101 mathematicsElectrical and Electronic EngineeringeducationBrownian motioneducation.field_of_studyScience & TechnologyStochastic process010102 general mathematicsRelaxation (iterative method)Engineering Electrical & ElectronicOptimal controlComputer Science Applications0906 Electrical and Electronic EngineeringIndustrial Engineering & AutomationMean field theoryControl and Systems EngineeringSettore MAT/09 - Ricerca Operativa0913 Mechanical Engineering
researchProduct

Exact solution of the soft-clustered vehicle-routing problem

2020

Abstract The soft-clustered vehicle-routing problem (SoftCluVRP) extends the classical capacitated vehicle-routing problem by one additional constraint: The customers are partitioned into clusters and feasible routes must respect the soft-cluster constraint, that is, all customers of the same cluster must be served by the same vehicle. In this article, we design and analyze different branch-and-price algorithms for the exact solution of the SoftCluVRP. The algorithms differ in the way the column-generation subproblem, a variant of the shortest-path problem with resource constraints (SPPRC), is solved. The standard approach for SPPRCs is based on dynamic-programming labeling algorithms. We s…

050210 logistics & transportationMathematical optimization021103 operations researchInformation Systems and ManagementGeneral Computer ScienceComputer science05 social sciences0211 other engineering and technologies02 engineering and technologyManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringConstraint (information theory)Exact solutions in general relativityModeling and Simulation0502 economics and businessVehicle routing problemCluster (physics)State spaceRelaxation (approximation)Integer programmingEuropean Journal of Operational Research
researchProduct

Learning Automata-based Misinformation Mitigation via Hawkes Processes

2021

AbstractMitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint ra…

Computer Networks and CommunicationsComputer scienceDistributed computingStochastic optimizationSocial media Misinformation02 engineering and technologyCrisis mitigationArticleTheoretical Computer ScienceLearning automata020204 information systemsConvergence (routing)0202 electrical engineering electronic engineering information engineeringState spaceSocial mediaMisinformationVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Social networkLearning automatabusiness.industryAutomaton020201 artificial intelligence & image processingStochastic optimizationbusinessHawkes processesSoftwareInformation Systems
researchProduct

Multi-agent Reinforcement Learning for Simulating Pedestrian Navigation

2012

In this paper we introduce a Multi-agent system that uses Reinforcement Learning (RL) techniques to learn local navigational behaviors to simulate virtual pedestrian groups. The aim of the paper is to study empirically the validity of RL to learn agent-based navigation controllers and their transfer capabilities when they are used in simulation environments with a higher number of agents than in the learned scenario. Two RL algorithms which use Vector Quantization (VQ) as the generalization method for the space state are presented. Both strategies are focused on obtaining a good vector quantizier that generalizes adequately the state space of the agents. We empirically state the convergence…

Computer scienceGeneralizationbusiness.industryVector quantizationContext (language use)Machine learningcomputer.software_genreDomain (software engineering)Convergence (routing)State spaceReinforcement learningArtificial intelligenceTransfer of learningbusinesscomputer
researchProduct

State Space-Vector Model of Linear Induction Motors Including End-effects and Iron Losses - Part II: Model Identification and Results

2020

This is the second part of an article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost …

Computer scienceidentification techniquelinear 22 induction motor (LIM)020208 electrical & electronic engineeringSystem identification020302 automobile design & engineering02 engineering and technologyFunction (mathematics)Industrial and Manufacturing EngineeringFinite element methodEnd-effectsEnd-effectIdentification (information)0203 mechanical engineeringSettore ING-INF/04 - AutomaticaControl and Systems EngineeringControl theoryLinear induction motor0202 electrical engineering electronic engineering information engineeringState spacelinear induction motor (LIM)State (computer science)Electrical and Electronic Engineeringparameter estimationInduction motor
researchProduct

State Space-Vector Model of Linear Induction Motors including End-Effects and Iron Losses Part I: Theoretical Analysis

2020

This is the first part of the article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses and its offline identification. This first part specifically treats the theoretical formulation of this model, which has been expressed in a state form, so to be, in perspective, suitably adopted for developing novel nonlinear control techniques, nonlinear observers as well as electrical losses minimization techniques. Besides the formulation of the dynamic model in space-vector state form, a steady-state analysis is proposed, highlighting the combined effects …

Computer sciencestate modelend-effectsNonlinear controlIndustrial and Manufacturing EngineeringMagnetic fluxEnd-effectIdentification (information)Nonlinear systemPerspective (geometry)Settore ING-INF/04 - AutomaticaControl and Systems EngineeringControl theoryLinear induction motorspace vectorState spacelinear induction motor (LIM)MinificationElectrical and Electronic Engineering
researchProduct

Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes

2019

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …

Conditional entropyFOS: Computer and information sciencesComputer scienceStochastic processDynamical system01 natural sciencesMeasure (mathematics)010305 fluids & plasmasMethodology (stat.ME)Multiscale Entropy Information Theory ComplexityAutoregressive model0103 physical sciencesState space010306 general physicsRepresentation (mathematics)AlgorithmStatistics - MethodologyParametric statistics
researchProduct

A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes

2021

The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It p…

Constraint (information theory)Transitive relationTheoretical computer scienceLearning automataComputer scienceGreatest common divisorState spaceSpace (commercial competition)Partition (database)Automaton
researchProduct