Search results for "Support"

showing 10 items of 2310 documents

H2 Transformations on Graphene Supported Palladium Cluster: DFT-MD Simulations and NEB Calculations

2020

Molecular dynamics simulations based on density functional theory were employed to investigate the fate of a hydrogen molecule shot with different kinetic energy toward a hydrogenated palladium cluster anchored on the vacant site of a defective graphene sheet. Hits resulting in H2 adsorption occur until the cluster is fully saturated. The influence of H content over Pd with respect to atomic hydrogen spillover onto graphene was investigated. Calculated energy barriers of ca. 1.6 eV for H-spillover suggest that the investigated Pd/graphene system is a good candidate for hydrogen storage.

Materials sciencespilloverhydrogen reactionchemistry.chemical_elementsupported metal catalysts02 engineering and technology010402 general chemistryKinetic energylcsh:Chemical technology01 natural sciencesDFTCatalysislaw.inventionlcsh:ChemistryMolecular dynamicsHydrogen storagelawCluster (physics)lcsh:TP1-1185Physical and Theoretical Chemistryhydrogenation elementary eventsGraphene021001 nanoscience & nanotechnology0104 chemical scienceschemistrylcsh:QD1-999Chemical physicsDensity functional theoryHydrogen spillover0210 nano-technologyPalladiumCatalysts
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A new preference handling technique for interactive multiobjective optimization without trading-off

2015

Because the purpose of multiobjective optimization methods is to optimize conflicting objectives simultaneously, they mainly focus on Pareto optimal solutions, where improvement with respect to some objective is only possible by allowing some other objective(s) to impair. Bringing this idea into practice requires the decision maker to think in terms of trading-off, which may limit the ability of effective problem solving. We outline some drawbacks of this and exploit another idea emphasizing the possibility of simultaneous improvement of all objectives. Based on this idea, we propose a technique for handling decision maker’s preferences, which eliminates the necessity to think in terms of t…

Mathematical optimizationControl and OptimizationExploitComputer scienceApplied Mathematicsmedia_common.quotation_subjectpreference informationPreference handlinginteractive methodsManagement Science and Operations ResearchDecision makerMulti-objective optimizationnegotiation supportComputer Science ApplicationsPareto optimalNegotiationmultiple objectivesNAUTILUS methodLimit (mathematics)Focus (optics)media_commonJournal of Global Optimization
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SMAA - Stochastic multiobjective acceptability analysis

1998

Stochastic multiobjective acceptability analysis (SMAA) is a multicriteria decision support technique for multiple decision makers based on exploring the weight space. Inaccurate or uncertain input data can be represented as probability distributions. In SMAA the decision makers need not express their preferences explicitly or implicitly; instead the technique analyses what kind of valuations would make each alternative the preferred one. The method produces for each alternative an acceptability index measuring the variety of different valuations that support that alternative, a central weight vector representing the typical valuations resulting in that decision, and a confidence factor mea…

Mathematical optimizationDecision support systemInformation Systems and ManagementGeneral Computer ScienceStochastic modellingDecision theoryConfidence factorWeight spaceManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringVariety (cybernetics)Modeling and SimulationProbability distributionWeightMathematical economicsMathematicsEuropean Journal of Operational Research
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Incorporating preference information in interactive reference point methods for multiobjective optimization

2009

In this paper, we introduce new ways of utilizing preference information specified by the decision maker in interactive reference point based methods. A reference point consists of desirable values for each objective function. The idea is to take the desires of the decision maker into account more closely when projecting the reference point onto the set of nondominated solutions. In this way we can support the decision maker in finding the most satisfactory solutions faster. In practice, we adjust the weights in the achievement scalarizing function that projects the reference point. We identify different cases depending on the amount of additional information available and demonstrate the c…

Mathematical optimizationDecision support systemInformation Systems and ManagementInteractive programmingStrategy and Managementmedia_common.quotation_subjectManagement Science and Operations ResearchDecision makerMulti-objective optimizationPreferenceSet (abstract data type)Decision-makingFunction (engineering)media_commonMathematicsOmega
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Prospect theory and stochastic multicriteria acceptability analysis (SMAA)

2009

Abstract We consider problems where multiple decision makers (DMs) want to choose their most preferred alternative from a finite set based on multiple criteria. Several approaches to support DMs in such problems have been suggested. Prospect theory has appealed to researchers through its descriptive power, but rare attempts have been made to apply it to support multicriteria decision making. The basic idea of prospect theory is that alternatives are evaluated by a difference function in terms of gains and losses with respect to a reference point. The function is suggested to be concave for gains and convex for losses and steeper for losses than for gains. Stochastic multicriteria acceptabil…

Mathematical optimizationDecision support systemInformation Systems and ManagementStrategy and ManagementManagement Science and Operations ResearchDecision problemGroup decision-makingProspect theoryComplete informationLoss aversionProbability distributionMathematical economicsPreference (economics)MathematicsOmega
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Fully Polynomial Time Approximation Scheme for the Two-Parallel Capacitated Machines Scheduling Problem Under Unavailability Constraint

2010

Abstract Decision Support Systems (DSS) ensure the computer-based support for the conscientious decision-making in solving problems that require a large amount of information processing and complex scenarios. DSS for Transportation (DSST) are intelligent systems that are used at operational and organizational management levels. Operating a DSST in a public transportation web-based monitoring system is presented in this paper.

Mathematical optimizationDecision support systemJob shop schedulingbusiness.industryDistributed computingIntelligent decision support systemInformation processingGeneral MedicinePolynomial-time approximation schemeConstraint (information theory)Public transportUnavailabilitybusinessMathematicsIFAC Proceedings Volumes
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Statistical criteria for early-stopping of support vector machines

2007

This paper proposes the use of statistical criteria for early-stopping support vector machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to fourth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower computational efforts and no significant differences are observed in terms of performance and sparsity.

Mathematical optimizationEarly stoppingStructured support vector machinebusiness.industryCognitive NeuroscienceMachine learningcomputer.software_genreRegressionProbability vectorComputer Science ApplicationsSupport vector machineRelevance vector machineArtificial IntelligenceConvergence (routing)MinificationArtificial intelligencebusinesscomputerMathematicsNeurocomputing
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Kernelizing LSPE(λ)

2007

We propose the use of kernel-based methods as underlying function approximator in the least-squares based policy evaluation framework of LSPE(λ) and LSTD(λ). In particular we present the 'kernelization' of model-free LSPE(λ). The 'kernelization' is computationally made possible by using the subset of regressors approximation, which approximates the kernel using a vastly reduced number of basis functions. The core of our proposed solution is an efficient recursive implementation with automatic supervised selection of the relevant basis functions. The LSPE method is well-suited for optimistic policy iteration and can thus be used in the context of online reinforcement learning. We use the hig…

Mathematical optimizationKernel (statistics)KernelizationLeast squares support vector machineBenchmark (computing)Reinforcement learningContext (language use)Basis functionFunction (mathematics)Mathematics2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning
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Least-Norm Regularization For Weak Two-Level Optimization Problems

1992

In this paper, we consider a regularization for weak two-level optimization problems by adaptation of the method presented by Solohovic (1970). Existence and approximation results are given in the case in which the constraints to the lower level problems are described by a multifunction. Convergence results for the least-norm regularization under perturbations are also presented.

Mathematical optimizationOptimization problemNorm (mathematics)Proximal gradient methods for learningRegularization perspectives on support vector machinesBackus–Gilbert methodRegularization (mathematics)Mathematics
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TOWARD A SOLUTION OF ALLOCATION IN LIFE CYCLE INVENTORIES: THE USE OF LEAST SQUARES TECHNIQUES

2010

Purpose: The matrix method for the solution of the so-called inventory problem in LCA generally determines the inventory vector related to a specific system of processes by solving a system of linear equations. The paper proposes a new approach to deal with systems characterized by a rectangular (and thus non-invertible) coefficients matrix. The approach, based on the application of regression techniques, allows solving the system without using computational expedients such as the allocation procedure. Methods: The regression techniques used in the paper are (besides the ordinary least squares, OLS) total least squares (TLS) and data least squares (DLS). In this paper, the authors present t…

Mathematical optimizationSettore ING-IND/11 - Fisica Tecnica AmbientaleMulti-functional processLCAAllocationGeneralized least squares/dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_productionLeast squaresOverdetermined systemLeast squaresOrthogonal regressionOver-determined systemDiscrepancy vectorNon-linear least squaresOrdinary least squaresLeast squares support vector machineTotal least squaresSDG 12 - Responsible Consumption and ProductionLinear least squaresGeneral Environmental ScienceMathematics
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