software on generalpurpose available multiprocessor systems.docx

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softwareongeneralpurposeavailablemultiprocessorsystems

101

Real-TimeImplementationofanEfficientSpeechEnhancementAlgorithmforDigitalHearingAids  OriginalResearchArticle

TsinghuaScience&Technology,Volume11,Issue4,August2006,Pages475-480

JieGao,HuiZhang,GuangshuHu

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102

Astudyonthepredictionofthemechanicalpropertiesofaceramictoolbasedonanartificialneuralnetwork  OriginalResearchArticle

JournalofMaterialsProcessingTechnology,Volume129,Issues1-3,11October2002,Pages399-402

C.Z.Huang,L.Zhang,L.He,J.Sun,B.Fang,B.Zou,Z.Q.Li,X.Ai

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103

Hardware/softwaresupportforadaptivework-stealinginon-chipmultiprocessor  OriginalResearchArticle

JournalofSystemsArchitecture,Volume56,Issue8,August2010,Pages392-406

QuentinMeunier,FrédéricPétrot,Jean-LouisRoch

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104

Optimizationofcomminutioncircuitsimulationsbasedongeneticalgorithmssearchmethod  OriginalResearchArticle

MineralsEngineering,Volume22,Issues7-8,June-July2009,Pages719-726

A.Farzanegan,S.M.Vahidipour

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Comminutionsimulatorsareextensivelyusedbymineralprocessingengineersforplantdesignandoptimizationpurposes.Recently,therehadbeenagreatprogressindevelopingnewandmorepowerfuloptimizationmethodssuchasGeneticAlgorithms（GA）,AntColonyOptimization（ACO）,ParticleSwarmOptimization（PSO）andTabuSearchAlgorithm（TSA）.Simulationoptimizationisrequiredifonewantstofindthebeststeady-statevaluesofimportantprocessvariables.Inthispaper,theauthorsinvestigatedtheintegrationofGAoptimizationalgorithmwithapre-existinggrindingcircuitsimulatorcalledBallMillingCircuitsSimulator（BMCS）inMATLAB™environment.TheBMCScodehasbeenwritteninANSIClanguageandhasbeenvalidatedagainstrealindustrialgrindingcircuitdatasets.VariousCmodulesoftheBMCSgrindingsoftwarewererestructuredunderanewsinglesourcecodefilesothatitcanbeimportedintoMATLAB.Then,anumberofinputsimulationdatawereidentifiedandselectedaspossibleprocessvariables（e.g.,solidsflowrate,wateradditionrate,andnumberofoperatingcyclones）whichmustbeoptimizedinordertoachieveapre-definedprocessobjective（e.g.,aspecificd80ofcircuitoutput）.TheobtainedresultsshowthatBMCSsimulationtrialscanbesuccessfullyoptimizedbyapplyingevolutionaryalgorithmsviaMATLABtoolboxes.Thisallowsthemineralprocessortoperformautomaticrepetitivesimulationstofindthepossiblesolutionsoftheproblemathandquickly.

ArticleOutline

1.Introduction

2.BallMillingCircuitSimulator（BMCS）

2.1.Ballmillmodule

2.2.Cyclonemodule

2.3.Circuit,junction,splitandconvergencemodules

3.Simulationoptimizationandsearchmethods

3.1.Geneticalgorithms

3.2.Geneticalgorithmoperators

4.BMCSunderMATLAB

5.Fitnessfunction

6.GAexecution

7.Closedcircuitsimulationoptimization

8.Conclusion

Acknowledgements

References

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105

Developmentofaworkforcemanagementsystemforacustomerhotlineservice  OriginalResearchArticle

Computers&OperationsResearch,Volume27,Issue10,September2000,Pages987-1004

C.K.Y.Lin,K.F.Lai,S.L.Hung

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Abstract

Anintegratedworkforcemanagementsystemwasdevelopedfora24-hhotlineservice.Itfirstapplieseitheraregressionmodelorsimulationmodeltorelatethetargetabandonmentrateswiththerequiredhourlystaffinglevel,whichisthenconvertedintothedailyrequirementsofseniorandjuniorofficersbyuseofamixedintegerprogrammingapproach.ThelastphaseinvolvesextendingBurnsandCarteralgorithmtoschedulethemonthlyrosterwithemphasisonbothemployeeequityandmanagementspecifications.ImplementationisunderMicrosoftOffice95ona586PC.Comparisonwiththemanualscheduleshowssignificantsavingsintimeandahigherdegreeofconstraintsatisfaction.

Scopeandpurpose

Withtheboomingmobiletelephonebusiness,a24-hcustomerhotlineserviceinamobilecommunicationscompanyfacedincreasingchallengesinschedulingitsgrowingworkforcetobedistributedintoeightdailyshifts.Thecomplexconstraintsandgoalssetbythecompanydemandatailor-madeworkforcemanagementsystemwhereexistingpackagescannotbedirectlyapplied.Thispaperreportsonthedevelopmentofadecisionsupportsystemfortheforecastsofhourlydemandcalls,leadingtotheestimationofstaffingrequirementsandschedulingofthemonthlyroster.Fastreschedulingcanbeachievedandmoreschedulingconstraintsaresatisifed.Usefulmanagementinformationisgeneratedtoenablebetterunderstandingofthemanpowersupplyanddemandsituations.Thecomputerizationprocessbringstogetherthemanagementandinterdisciplinaryteamstorealizethetradeoffsbetweenvariousschedulingconstraints,andpossiblemodificationsinanticpationoffuturechanges.

ArticleOutline

1.Introduction

2.Schedulingconstraints

3.Methodology

3.1.PhaseI（hourlyforecasts）

3.2.PhaseII（dailystaffrequirementandmealbreakassignment）

3.3.PhaseIII（monthlyrosterandovertime/excessman-hour）

4.ImplementationonaPC

5.Conclusionsandfuturework

Acknowledgements

References

Vitae

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106

ScalableuncertaintyandreliabilityanalysisbyintegrationofadvancedMonteCarlosimulationandgenericfiniteelementsolvers  OriginalResearchArticle

Computers&Structures,Volume87,Issues13-14,July2009,Pages930-947

M.F.Pellissetti,G.I.Schuëller

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Abstract

Thiscontributiondescribeshowtheuncertaintyassociatedwithstructurescanbemodeledandanalyzed,incontextwithstate-of-the-artFEsoftwareandmoderncomputinginfrastructure.Uncertaintymodelingwithhigh-dimensionalrandomvariablesandrandomfieldsmotivatestheadoptionofadvancedMonteCarlomethodsforreliabilityanalysis.Ontheimplementationside,object-orientationandparallelizationhavebeenembracedtoensureflexibilityandperformance.

Anovel,Matlab-basedtoolkit,COSSAN-X,embodyingthesecharacteristics,ispresented.TheapplicationtoasatelliteunderharmonicexcitationandaturbinebladeundercentrifugalloadingindicatestheimportanceofconsideringspatialfluctuationsandthescalabilitywithrespecttorealisticFEmodels.

ArticleOutline

1.Introduction

2.Methodsofanalysis

2.1.Generalremarks

2.2.Modelingofuncertaintiesinstructuralpropertiesandloading

2.2.1.Stochasticfiniteelementanalysis

2.3.Uncertaintypropagationinstructuralanalysis

2.3.1.MonteCarlosimulation

2.3.2.Accelerateduncertaintypropagationbyapproximateresponserepresentations

2.4.Structuralreliabilityanalysis

2.4.1.Problemdefinition

2.4.2.DirectMonteCarloSimulation（DMCS）

2.4.3.AdvancedMonteCarlosimulationmethods

3.Softwaretechnology

3.1.Generalremarks

3.2.Finiteelementsoftware

3.2.1.IntegrationofCADandFEsoftware

3.3.InteroperabilitybetweenFEcodeanduncertaintyanalysissoftware

3.3.1.Standaloneimplementation

3.3.2.IntegrationintoFEprogram

3.3.3.IntegrationintoFEpre-processor

3.4.Uncertaintypropagation

3.4.1.Uncertaintypropagationwithnon-intrusivemethods

3.4.2.Uncertaintypropagationwithintrusivemethods

3.5.Parallelprocessing

3.5.1.Highlevelofparallelism

3.5.2.Lowlevelparallelismofthedeterministicanalysis

3.5.3.Multi-corearchitecture

3.6.COSSAN–objectorientedtoolkitforcomputationalstochasticstructuralanalysis

3.6.1.Background

3.6.2.Softwareconcept–interoperabilitywithFEsoftware

3.6.3.Uncertaintyanalysisengine

3.6.4.Parallelprocessing

3.6.5.Uncertaintypropagationandanalysismethods

4.Large-scaleapplications

4.1.Satellitestructure

4.1.1.Reliabilityanalysis

4.2.Turbineblade

4.2.1.Modeldescription

4.2.2.Deterministicanalysis

4.2.3.Stochasticmodel

4.2.4.Uncertaintyanalysis

4.2.5.Reliabilityanalysis

5.Conclusions

Acknowledgements

References

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107

Evaluationofintegratedhydrogensystems:

IEATask18  OriginalResearchArticle

InternationalJournalofHydrogenEnergy,Volume35,Issue18,September2010,Pages10031-10037

MariadelPilarArgumosa,EstherChacon,SusanM.Schoenung

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Abstract

TheInternationalEnergyAgency（IEA）HydrogenImplementingAgreement（HIA）establishedin2004atask（Annex18）onthetopicof“IntegratedSystems:

EvaluationofHydrogenDemonstrationProjects”.During6years,dividedintwophases,thisannexhasbeenworkinginordertosearchandprovideinformationonprogressinthehydrogeneconomyaroundtheworld.Spainisoneofthe15membercountriessincethephase1.

Thetaskhasfollowedthreemaintargets:

Todevelopadatabasethatincludesnational