International Journal of Radiation OncologyBiologyPhysics.docx

International Journal of Radiation OncologyBiologyPhysics.docx

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InternationalJournalofRadiationOncologyBiologyPhysics

Multi-objectiveoptimizationofwind-excitedstructures  OriginalResearchArticle

EngineeringStructures,Volume29,Issue6,June2007,Pages983-990

I.Venanzi,A.L.Materazzi

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Abstract

Aprocedurefortheoptimizationofwind-excitedstructuresisproposed.Itisbasedonthesimulatedannealingalgorithmcombinedwiththedynamicanalysisoftheresponseeitherinthefrequencyorinthetimedomain.Whenthestep-by-stepdynamicanalysisisused,itcanhandlethecaseofflexiblestructures,likemastsandlatticetowers,whosegeometricnon-lineareffectscannotbeneglected.Theprocedureallowsformultiplevariablesandobjectives.Theproposedmethodisusedtooptimizetheconfigurationofacable-stayedmastsubjectedtoturbulentwindloading.Theresultsshowedthatthealgorithmisreasonablyindependentofthefirstguessconfigurationandiseffectiveinavoidinglocalminima.Itswidefieldofapplicabilityandeaseofimplementationmaketheproposedalgorithmapowerfuldesigntoolforstructuralengineers.

ArticleOutline

1.Introduction

2.Theoptimizationtechnique

2.1.TheSimulatedAnnealingalgorithm

2.2.Themultipleobjectivefunction

2.3.Implementationoftheprocedure

3.Windloadingandstructuralresponse

3.1.Windloadmodeling

3.2.Frequencydomainanalysis

3.3.Timedomainanalysis

4.Numericalapplication

4.1.Descriptionofthecasestudy

4.2.Theoptimizationproblem

4.3.Results

5.Concludingremarks

Acknowledgements

References

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UFOme:

Anontologymappingsystemwithstrategypredictioncapabilities  OriginalResearchArticle

Data&KnowledgeEngineering,Volume69,Issue5,May2010,Pages444-471

GiuseppePirró,DomenicoTalia

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Abstract

Ontologymapping,ormatching,aimsatidentifyingcorrespondencesamongentitiesindifferentontologies.Severalstrandsofresearchcomeupwithalgorithmsoftencombiningmultiplemappingstrategiestoimprovethemappingaccuracy.However,fewapproacheshavesystematicallyinvestigatedtherequirementsofamappingsystembothfromthefunctional（i.e.,thefeaturesthatarerequired）anduserpointofview（i.e.,howtheusercanexploitthesefeatures）.Thispaperpresentsanontologymappingsoftwareframeworkthathasbeendesignedandimplementedtohelpusers（bothexpertandnon-expert）indesigningand/orexploitingcomprehensivemappingsystems.Itisbasedonalibraryofmappingmodulesimplementingfunctionssuchasdiscoveringmappingsorevaluatingmappingstrategies.Inparticular,thestrategypredictormoduleofthedesignedframework,foreachspecificmappingtask,can“predict”mappingmodulestobeexploitedandparametervalues（e.g.,weightsandthresholds）.Theimplementedsystem,calledUFOme,assistsusersduringthevariousphasesofamappingtaskexecutionbyprovidingauserfriendlyontologymappingenvironment.TheUFOmeimplementationanditspredictioncapabilitiesandaccuracywereevaluatedontheOntologyAlignmentEvaluationInitiativetestswithencouragingresults.

ArticleOutline

1.Introduction

2.Definitionsandproblemstatement

2.1.Ontology

2.1.1.Ontologicalinformation

2.2.Ontologymapping:

motivationandexample

2.3.Ontologymappingrepresentation

2.4.Ontologymappingsystem

2.5.Mappingstrategy

2.6.Similarityfunction

3.Relatedwork

3.1.Usersupportforontologymapping

3.2.Ontologymappingsystemsbasedonmultiplestrategies

3.3.Evaluatingmappingstrategies

3.4.Automaticparameterstuning

3.5.Requirementsofanontologymappingframework

3.6.Whatamappingsystemshouldfeature?

4.Alibraryofmodulesforontologymappingsystems

4.1.Visualization

4.2.Matching

4.2.1.TheLuceneontologymatcher（LOM）

4.2.2.Thewordnetmatcher（WM）

4.2.3.Thestringmatcher（SM）

4.2.4.Thestructuralontologymatcher（SOM）

4.3.Combination

4.4.Strategyprediction:

thestrategypredictormodule

4.4.1.Lexicalaffinitycoefficient

4.4.2.Structuralaffinitycoefficient

4.4.3.Exploitingaffinitycoefficients

4.4.4.Ondeterminingtheoptimalthresholdvalues

4.4.5.Ondeterminingoptimalweightvalues

4.5.Evaluation

5.UFOme:

Acomprehensiveontologymappingsystem

5.1.Areferenceontologymappingframeworkarchitecture

5.2.TheUFOmesystem

5.2.1.TheUFOmetwo-layerarchitecture

5.2.2.Phase1:

Designing

5.2.3.Phase2:

Running

5.2.4.Phase3:

Evaluation

5.2.5.Remark

6.Experimentalevaluation

6.1.Thedatasetandtheevaluationmethodology

6.2.Evaluationofthestrategypredictormodule

6.2.1.Parametertuning

6.3.Effectofthethresholdonlinguisticmappingstrategies

6.4.Effectofthethresholdonstructuralontologymapping

6.5.Evaluatingautomaticweightsassignment

6.5.1.Evaluatingtheeffectsofmappingmodulesselection

6.5.2.ComparingUFOmewithothersystems

7.Discussion

8.Futurework

References

Vitae

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Anefficientdiagnostictechniquefordistributionsystemsbasedonunderfaultvoltagesandcurrents  OriginalResearchArticle

ElectricPowerSystemsResearch,Volume80,Issue10,October2010,Pages1205-1214

A.Campoccia,M.L.DiSilvestre,I.Incontrera,E.RivaSanseverino,G.Spoto

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Abstract

Servicecontinuityisoneofthemajoraspectsinthedefinitionofthequalityoftheelectricalenergy,forthisreasontheresearchinthefieldoffaultsdiagnosticfordistributionsystemsisspreadingevermore.Moreovertheincreasinginterestaroundmoderndistributionsystemsautomationformanagementpurposesgivesfaultsdiagnosticsmoretoolstodetectoutagespreciselyandinshorttimes.Inthispaper,theapplicabilityofanefficientfaultlocationandcharacterizationmethodologywithinacentralizedmonitoringsystemisdiscussed.Themethodology,appropriateforanykindoffault,isbasedontheuseoftheanalyticalmodelofthenetworklinesandusesthefundamentalcomponentsrmsvaluestakenfromthetransientmeasuresoflinecurrentsandvoltagesattheMV/LVsubstations.Thefaultlocationandidentificationalgorithm,proposedbytheauthorsandsuitablyrestated,hasbeenimplementedonamicroprocessor-baseddevicethatcanbeinstalledateachMV/LVsubstation.Thespeedandprecisionofthealgorithmhavebeentestedagainsttheerrorsderivingfromthefundamentalextractionwithintheprescribedfaultclearingtimesandagainsttheinherentprecisionoftheelectronicdeviceusedforcomputation.ThetestshavebeencarriedoutusingMatlabSimulinkforsimulatingthefaultedsystem.

ArticleOutline

1.Introduction

2.Thediagnosticsystem

3.Circuitmodelofonelinespanfordiagnosys

3.1.Unfaultedcondition

3.2.Faultedcondition

4.Faultidentificationandcharacterization

4.1.Solutionalgorithm

4.2.Phaseassessment

5.Compliancetoexistingsystems

6.Applications

6.1.Precisionofthediagnosticalgorithm

6.1.1.Singlephasetoearthfault

6.1.2.Threephasetogroundfault

6.2.Calculationtimes

7.Conclusions

AppendixA.Microprocessorimplementation

References

Vitae

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On-linehandwrittendigitrecognitionbasedontrajectoryandvelocitymodeling  

PatternRecognitionLetters,Volume29,Issue5,1April2008,Pages580-594

MonjiKherallah,LobnaHaddad,AdelM.Alimi,AmarMitiche

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Abstract

Thehandwritingisoneofthemostfamiliarcommunicationmedia.Penbasedinterfacecombinedwithautomatichandwritingrecognitionoffersaveryeasyandnaturalinputmethod.Thehandwrittensignalison-linecollectedviaadigitizingdevice,anditisclassifiedasonepre-specifiedsetofcharacters.Themaintechniquesappliedinourworkincludetwofieldsofresearch.Thefirstoneconsistsofthemodelingsystemofhandwriting.Inthisarea,wedevelopedanovelmethodofthehandwrittentrajectorymodelingbasedonellipticandBetarepresentation.ThesecondpartofourworkshowstheimplementationofaclassifierconsistingoftheMulti-LayersPerceptionofNeuralNetworks（MLPNN）developedinafuzzyconcept.ThetrainingprocessoftherecognitionsystemisbasedonanassociationoftheSelfOrganizationMaps（SOM）withFuzzyK-NearestNeighborAlgorithms（FKNNA）.Totesttheperformanceofoursystemwebuild30,000Arabicdigits.Theglobalrecognitionrateobtainedbyourrecognitionsystemisabout95.08%.

ArticleOutline

1.Introduction

2.TrajectorymodelingbyBeta–ellipticalrepresentation

2.1.Betavelocitymodeling

2.2.Ellipticaltrajectorymodeling

2.3.CombinationbetweenBetaandellipticalmodels

3.On-linerecognitionofhandwrittendigits

3.1.Pre-processingsystem

3.2.Realclassdetectionbyself-organizingmap

3.3.MembershipassignmentofthetrainingdatasettorealclassesbytheFuzzyK-NearestNeighborAlgorithm

3.4.ClassificationbytheMLPNNsystem

3.5.Digitdatabaseformulation

4.Experimentalresultsanddiscussions

5.Conclusion

Acknowledgements

References

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EDICAMfastvideodiagnosticinstallationontheCOMPASStokamak  OriginalResearchArticle

FusionEngineeringandDesign,Volume85,Issues3-