簡(jiǎn)介：JOURNALOFMATERIALSPROCESSINGTECHNOLOGY142200320–28DELTAFERRITEPREDICTIONINSTAINLESSSTEELWELDSUSINGNEURALNETWORKANALYSISANDCOMPARISONWITHOTHERPREDICTIONMETHODSMVASUDEVANA,?,AKBHADURIA,BALDEVRAJA,KPRASADRAOBAMETALLURGYANDMATERIALSGROUP,INDIRAGANDHICENTREFORATOMICRESEARCH,KALPAKKAM,INDIABDEPARTMENTOFMETALLURGY,INDIANINSTITUTEOFTECHNOLOGY,CHENNAI,INDIARECEIVED2MAY2002RECEIVEDINREVISEDFORM11DECEMBER2002ACCEPTED17FEBRUARY2003ABSTRACTTHEABILITYTOPREDICTTHEDELTAFERRITECONTENTINSTAINLESSSTEELWELDSISIMPORTANTFORMANYREASONSDEPENDINGONTHESERVICEREQUIREMENT,MANUFACTURERSANDCONSUMERSOFTENSPECIFYDELTAFERRITECONTENTASANALLOYSPECIFICATIONTOENSURETHATWELDCONTAINSADESIREDMINIMUMORMAXIMUMFERRITELEVELRECENTRESEARCHACTIVITIESHAVEBEENFOCUSEDONSTUDYINGTHEEFFECTOFVARIOUSALLOYINGELEMENTSONTHEDELTAFERRITECONTENTANDCONTROLLINGDELTAFERRITECONTENTBYMODIFYINGTHEWELDMETALCOMPOSITIONSOVERTHEYEARS,ANUMBEROFMETHODSINCLUDINGCONSTITUTIONDIAGRAMS,FUNCTIONFITMODEL,FEEDFORWARDBACKPROPAGATIONNEURALNETWORKMODELHAVEBEENPUTFORWARDFORPREDICTINGTHEDELTAFERRITECONTENTINSTAINLESSSTEELWELDSAMONGALLTHEMETHODS,NEURALNETWORKMETHODWASREPORTEDTOBEMOREACCURATECOMPAREDTOOTHERMETHODSAPOTENTIALRISKASSOCIATEDWITHNEURALNETWORKANALYSISISOVERFITTINGOFTHETRAININGDATATOAVOIDOVERFITTING,MACKAYHASDEVELOPEDABAYESIANFRAMEWORKTOCONTROLTHECOMPLEXITYOFTHENEURALNETWORKMAINADVANTAGESOFTHISMETHODARETHATITPROVIDESMEANINGFULERRORBARSFORTHEMODELPREDICTIONSANDALSOITISPOSSIBLETOIDENTIFYAUTOMATICALLYTHEINPUTVARIABLESWHICHAREIMPORTANTINTHENONLINEARREGRESSIONINTHEPRESENTWORK,BAYESIANNEURALNETWORKBNNMODELFORPREDICTIONOFDELTAFERRITECONTENTINSTAINLESSSTEELWELDHASBEENDEVELOPEDTHEEFFECTOFVARYINGCONCENTRATIONOFTHEELEMENTSONTHEDELTAFERRITECONTENTHASBEENQUANTIFIEDFORTYPE309AUSTENITICSTAINLESSSTEELANDTHEDUPLEXSTAINLESSSTEELALLOY2205THEBNNMODELISFOUNDTOBEMOREACCURATECOMPAREDTOTHATOFTHEOTHERMETHODSFORPREDICTINGDELTAFERRITECONTENTINSTAINLESSSTEELWELDS?2003ELSEVIERSCIENCEBVALLRIGHTSRESERVEDKEYWORDSNEURALNETWORKANALYSISDELTAFERRITECONTENTAUSTENITICSTAINLESSSTEELDUPLEXSTAINLESSSTEEL1INTRODUCTIONTHEABILITYTOESTIMATETHEDELTAFERRITECONTENTACCURATELYHASPROVENVERYUSEFULINPREDICTINGTHEVARIOUSPROPERTIESOFAUSTENITICSSWELDSAMINIMUMDELTAFERRITECONTENTISNECESSARYTOENSUREHOTCRACKINGRESISTANCEINTHESEWELDS1,2,WHILEANUPPERLIMITONTHEDELTAFERRITECONTENTDETERMINESTHEPROPENSITYTOEMBRITTLEMENTDUETOSECONDARYPHASES,EGSIGMAPHASE,ETC,FORMEDDURINGELEVATEDTEMPERATURESERVICE3ATCRYOGENICTEMPERATURES,THETOUGHNESSOFTHEAUSTENITICSSWELDSISSTRONGLYINFLUENCEDBYTHEDELTAFERRITECONTENT4INDUPLEXSTAINLESSSTEELWELDMETALS,ALOWERFERRITELIMITISSPECIFIEDFORSTRESSCORROSIONCRACKINGRESISTANCEWHILETHEUPPERLIMITISSPECIFIEDTOENSUREADEQUATEDUCTILITYANDTOUGHNESS5HENCE,DEPENDINGONTHESERVICEREQUIREMENTALOWERLIMITAND/ORANUPPERLIMITONDELTAFERRITECONTENTISGENERALLYSPECIFIEDDURINGTHESELEC?CORRESPONDINGAUTHORTEL91411480232FAX91411440381EMAILADDRESSDEVIGCARERNETINMVASUDEVANTIONOFFILLERMETALCOMPOSITION,THEMOSTACCURATEDIAGRAMTODATEWRC1992ISUSEDGENERALLYTOESTIMATETHE?FERRITECONTENT6THECREQANDNIEQFORMULAEUSEDFORGENERATINGTHEWRC1992CONSTITUTIONDIAGRAMISGIVENBYCREQCRMO07NBANDNIEQNI35C20N025CUTHELIMITATIONOFTHESEEQUATIONSISTHATVALUESOFTHECOEFFICIENTSFORTHEDIFFERENTELEMENTSREMAINUNCHANGEDIRRESPECTIVEOFTHECHANGEINTHEBASECOMPOSITIONOFTHEWELDHOWEVER,THERELATIVEINFLUENCEOFEACHALLOYINGADDITIONGIVENBYTHEELEMENTALCOEFFICIENTSINTHECREQANDNIEQEXPRESSIONSISLIKELYTOCHANGEOVERTHEFULLCOMPOSITIONRANGEFURTHERMORE,THESEEXPRESSIONSIGNORETHEINTERACTIONBETWEENTHEELEMENTSALSO,THEREAREANUMBEROFALLOYINGELEMENTSTHATHAVENOTBEENCONSIDEREDINTHEWRC1992DIAGRAMELEMENTSLIKESI,TI,WHAVENOTBEENGIVENDUETOCONSIDERATIONS,THOUGHTHEYAREKNOWNTOINFLUENCETHEDELTAFERRITECONTENTHENCE,THEDELTAFERRITECONTENTESTIMATEDUSINGTHEWRC1992DIAGRAMWOULDALWAYSBELESSACCURATEANDMAYNEVERBECLOSETOTHEACTUALMEASUREDVALUEINTHEFUNCTIONFITMODEL7FORESTIMATINGFERRITE,THEDIFFERENCEINFREEENERGYBETWEENTHEFERRITEANDTHEAUSTENITEWASCALCULATED09240136/–SEEFRONTMATTER?2003ELSEVIERSCIENCEBVALLRIGHTSRESERVEDDOI101016/S092401360300430822MVASUDEVANETAL/JOURNALOFMATERIALSPROCESSINGTECHNOLOGY142200320–28FIG1SCHEMATICDIAGRAMOFTHENETWORKSTRUCTURESHOWINGTHEINPUTNODES,HIDDENUNITSANDTHEOUTPUTNODEFUNCTIONSOTHATEACHINPUTCONTRIBUTESTOEVERYHIDDENUNIT,WHERENISTHETOTALNUMBEROFINPUTVARIABLESHITANH??N?JW1IJXJΘ1I??3HERETHEBIASISDESIGNATEDASΘANDISANALOGOUSTOTHECONSTANTINLINEARREGRESSIONTHETRANSFERFROMTHEHIDDENUNITSTOTHEOUTPUTISLINEAR,ANDISGIVENBYYN?IW2IHIΘ24THEOUTPUTYISTHEREFOREANONLINEARFUNCTIONOFXJ,WITHTHEFUNCTIONUSUALLYSELECTEDFORFLEXIBILITYBEINGTHEHYPERBOLICTANGENTTHUS,THENETWORKISCOMPLETELYDESCRIBEDIFTHENUMBEROFINPUTNODES,OUTPUTNODESANDTHEHIDDENUNITSAREKNOWNALONGWITHALLTHEWEIGHTSWIJANDBIASESΘITHESEWEIGHTSWIJAREDETERMINEDBYTRAININGTHENETWORKANDINVOLVESMINIMIZATIONOFAREGULARIZEDSUMOFSQUAREDERRORSTHEBNNANALYSISOFMACKAY10ALLOWSTHECALCULATIONOFERRORBARSWITHTWOCOMPONENTSONEREPRESENTINGTHEPERCEIVEDLEVELOFNOISEΣVINTHEOUTPUTANDTHEOTHERINDICATINGTHEUNCERTAINTYINTHEDATAFITTINGTHISLATTERCOMPONENTOFTHEERRORBARSEMANATINGFROMTHEBAYESIANFRAMEWORKALLOWSTHERELATIVEPROBABILITIESOFTHEMODELSWITHDIFFERENTCOMPLEXITYTOBEASSESSEDTHISENABLESESTIMATIONOFQUANTITATIVEERRORBARS,WHICHVARYWITHTHEPOSITIONINTHEINPUTSPACEDEPENDINGONTHEUNCERTAINTYINFITTINGTHEFUNCTIONINTHATSPACEHENCE,INSTEADOFCALCULATINGAUNIQUESETOFWEIGHTS,APROBABILITYDISTRIBUTIONOFWEIGHTSISUSEDTODEFINETHEUNCERTAINTYINFITTINGTHEREFORE,THESEERRORBARSBECOMELARGEWHENDATAARESPARSEORLOCALLYNOISYINTHISCONTEXT,AVERYUSEFULMEASUREOFTHEERRORISTHELOGARITHMOFTHEPREDICTIVEERRORLPEGIVENBYTHEFOLLOWINGLPE?N12?TN?YN2ΣN2YLOG2ΠΣNY1/2?5WHERETISTHETARGETFORTHESETOFINPUTSX,WHILEYTHECORRESPONDINGNETWORKOUTPUTΣYISRELATEDTOTHEUNCERTAINTYOFFITTINGFORTHESETOFINPUTSXBYUSINGLPE,THEPENALTYFORMAKINGAWILDPREDICTIONISREDUCEDIFTHATPREDICTIONISACCOMPANIEDBYANAPPROPRIATELYLARGEERRORBAR,WITHALARGERVALUEOFTHELPEIMPLYINGABETTERMODELFURTHER,BYTHISMETHODITISALSOPOSSIBLETOAUTOMATICALLYIDENTIFYTHEINPUTVARIABLESTHATARESIGNIFICANTININFLUENCINGTHEOUTPUTVARIABLE,ASTHEINPUTVARIABLESTHATARELESSSIGNIFICANTAREDOWNWEIGHTEDINTHEREGRESSIONANALYSIS31OVERFITTINGPROBLEMINBNNANALYSIS,TWOSOLUTIONSAREIMPLEMENTEDWHICHCONTRIBUTETOAVOIDOVERFITTINGTHEFIRSTISCONTAINEDINTHEALGORITHMDUETOMACKAY12THECOMPLEXITYPARAMETERSΑANDΒAREINFERREDFROMTHEDATA,THEREFOREALLOWINGAUTOMATICCONTROLOFTHEMODELCOMPLEXITYTHESECONDRESIDESINTHETRAININGMETHODTHEDATABASEISEQUALLYDIVIDEDINTOATRAININGSETANDATESTINGSETTOBUILDAMODEL,ABOUT80NETWORKSARETRAINEDWITHDIFFERENTNUMBEROFHIDDENUNITSANDSEEDS,USINGTHETRAININGSETTHEYARETHENUSEDTOMAKEPREDICTIONSONTHEUNSEENTESTINGSETANDARERANKEDBYLPE32COMMITTEEMODELTHENETWORKSWITHDIFFERENTNUMBEROFHIDDENUNITSWILLGIVEDIFFERENTPREDICTIONSBUTPREDICTIONSWILLALSODEPENDONTHEINITIALGUESSMADEFORTHEPROBABILITYDISTRIBUTIONOFTHEWEIGHTSTHEPRIOROPTIMUMPREDICTIONSAREOFTENMADEUSINGMORETHANONEMODEL,BYBUILDINGACOMMITTEETHEPREDICTIONˉYOFACOMMITTEEOFNETWORKSISTHEAVERAGEPREDICTIONOFITSMEMBERS,ANDTHEASSOCIATEDERRORBARISCALCULATEDACCORDINGTOEQ6ˉY1L?LYLΣ21L?LΣL2Y1L?LYL?ˉY26WHERELISTHENUMBEROFNETWORKSINACOMMITTEENOTETHATWENOWCONSIDERTHEPREDICTIONSFORAGIVENSINGLESETOFINPUTSANDTHATEXPONENTLREFERSTOTHEMODELUSEDTOPRODUCETHECORRESPONDINGPREDICTIONYLINPRACTICE,ANINCREASINGNUMBEROFNETWORKSAREINCLUDEDINACOMMITTEEANDTHEIR

簡(jiǎn)介：MIXEDDSP/FPGAIMPLEMENTATIONOFANERRORRESILIENTIMAGETRANSMISSIONSYSTEMBASEDONJPEG2000MARCOGRANGETTO,ENRICOMAGLI,MAURIZIOMARTINA,FABRIZIOVACCACERCOMCENTERFORWIRELESSMULTIMEDIACOMMUNICATIONSDIPARTIMENTODIELETTRONICAPOLITECNICODITORINOCORSODUCADEGLIABRUZZI2410129TORINOITALYGRANGETTOMAGLIPOLITOITMARTINAVACCAVLSILABOLPOLITOITPH390115644195FAX39011564099ABSTRACTTHISPAPERDESCRIBESADEMONSTRATOROFANERRORRESILIENTIMAGECOMMUNICATIONSYSTEMOVERWIRELESSPACKETNETWORKS,BASEDONTHENOVELJPEG2000STANDARDINPARTICULAR,THEDECODERIMPLEMENTATIONISADDRESSED,WHICHISTHEMOSTCRITICALTASKINTERMSOFCOMPLEXITYANDPOWERCONSUMPTION,INVIEWOFUSEONAWIRELESSPORTABLETERMINALFORCELLULARAPPLICATIONSTHESYSTEMIMPLEMENTATIONISBASEDONAMIXEDDSP/FPGAARCHITECTURE,WHICHALLOWSTOPARALLELIZESOMECOMPUTATIONALTASKS,THUSLEADINGTOEFICIENTSYSTEMOPERATION1INTRODUCTION’NOWADAYS,THEREISAGROWINGINTERESTINTHEENDTOENDTRANSMISSIONOFIMAGES,ESPECIALLYMOTIVATEDBYTHESHORTTERMDEPLOYMENTOFNEXTGENERATIONMOBILECOMMUNICATIONSERVICESUMTSIMT2000HOWEVER,TRANSMISSIONINANETWORKED,TETHERLESSENVIRONMENTPROVIDESBOTHOPPORTUNITIESANDCHALLENGESTHEWIRELESSCONTEXTIMPLIESTHATTHEDATAMAYUNDERGOBITERRORSANDPACKETLOSSES,MAKINGITNECESSARYTOFORESEEERRORRECOVERYMODALITIESITISTHEREBYNECESSARYTHATIMAGECOMMUNICATIONTECHNIQUESAREPROVIDEDWITHTHEABILITYTORECOVER,ORATLEASTCONCEAL,THEEFFECTOFSUCHLOSSESTHEFORTHCOMINGJPEG2000IMAGECOMPRESSIONSTANDARDHASBEENDESIGNEDTOMATCHTHESEREQUIREMENTS,ANDEMBEDSSOMEERRORDETECTIONANDCONCEALMENTTOOLSTHISPAPERADDRESSESTHEDEVELOPMENTOFADEMONSTRATOROFANERRORRESILIENTJPEG2000111DECODERIMPLEMENTATIONFORIMAGECOMMUNICATIONOVERALOSSYPACKETNETWORKTHEROBUSTNESSTOPACKETERASURESISACHIEVEDBYCOMBININGTHEFLEXIBILITYOFTHEJPEG2000FRAMEWORKWITHTHEPOWERFULNESSOFSOURCECHANNEL078037147X/01/100002001IEEEADAPTIVE,OPTIMIZEDREEDSOLOMONCODESTHEDECODERIMPLEMENTATIONISPARTICULARLYSIGNIFICANTINTHECONTEXTOFWIRELESSPORTABLETERMINALSFORNEXTGENERATIONCELLULARSYSTEMS,WHERETHELIMITEDPOWERBUDGETANDAVAILABLEDIMENSIONSIMPOSESEVERECONSTRAINTSONTHEDESIGNOFAMULTIMEDIAPROCESSINGSYSTEM2SYSTEMOVERVIEWTHEFUNCTIONALUNITSOFTHEIMPLEMENTEDSYSTEM21JPEG2000IMAGECOMPRESSIONJPEG2000ISTHENOVELIS0STANDARDFORSTILLIMAGECODING,ANDISINTENDEDTOPROVIDEINNOVATIVESOLUTIONSACCORDINGTOTHENEWTRENDSINMULTIMEDIATECHNOLOGIESATTHETIMEOFTHISWRITING,THESTANDARDISINADVANCEDPUBLICATIONSTAGETHEFINALCOMMITTEEDRAFTLISTHEMOSTRECENTJPEG2000DESCRIPTIONPUBLICLYAVAILABLE,WHICHOURIMPLEMENTATIONCONFORMSTOJPEG2000NOTONLYYIELDSSUPERIORPERFORMANCEWITHRESPECTTOEXISTINGSTANDARDSINTERMSOFCOMPRESSIONCAPABILITYANDSUBJECTIVEQUALITY,BUTALSONUMEROUSADDITIONALFUNCTIONALITIES,SUCHASIOSSLESSANDLOSSYCOMPRESSION,PROGRESSIVETRANSMISSION,ANDERRORRESILIENCETHEARCHITECTUREOFTHEJPEG2000ISBASEDONTHETRANSFORMCODINGAPPROACHANIMAGEMAYBEDIVIDEDINTOSEVERALSUBIMAGESTILES,TOREDUCEMEMORYANDCOMPUTINGREQUIREMENTSABIORTHOGONALDISCRETEWAVELETTRANSFORMDWTISFIRSTAPPLIEDTOEACHTILE,WHOSEOUTPUTISASERIESOFVERSIONSOFTHETILEATDIFFERENTRESOLUTIONLEVELSSUBBANDSTHEN,THETRANSFORMCOEFFICIENTSAREQUANTIZED,INDEPENDENTLYFOREACHSUBBAND,WITHANEMBEDDEDDEADZONEQUANTIZEREACHSUBBANDOFTHEWAVELETDECOMPOSITIONISDIVIDEDINTORECTANGULARBLOCKSCODEBLOCKS,WHICHAREININTHEFOLLOWINGWEPROVIDEABRIEFDESCRIPTIONOF1330CODETHERECEIVEDBITSTREAMMAKESTHEUSEOFAREEDSOLOMONFPGAIMPLEMENTATIONVERYATTRACTIVETHEJPEG2000DECODERMODULE,ENTIRELYIMPLEMENTEDONDSP,ISCOMPOSEDBYFOURMAINBLOCKSSYNTAXPARSER,ENTROPYDECODEREBCOT,UNIFORMSCALARDEQUANTIZER,ANDINVERSEWAVELETTRANSFORMMOREOVER,TWOADDITIONALTASKS,DEVOTEDTOCOMMUNICATIONMANAGEMENTBETWEENDSP,FPGAANDAPERSONALCOMPUTER,HAVEBEENINTRODUCED31SYNTAXPARSERTHEPARSERISTHEFUNCTIONALBLOCKTHATINTERFACESTHEJPEG2000DECODERWITHTHERSDECODERITRETRIEVESRSDECODEDPACKETS,ANDEXTRACTSFROMTHECOMPRESSEDJPEG2000BITSTREAMALLTHERELEVANTINFORMATIONNEEDEDTOPERFORMIMAGERECONSTRUCTIONFIRSTLY,THEBITSTREAMMAINHEADERISREAD,WHICHCONTAINSINFORMATIONONTHEPARAMETERSUSEDDURINGTHEENCODINGPROCESSEGIMAGESIZE,WAVELETFILTERUSED,NUMBEROFDECOMPOSITIONLEVELS,QUANTIZATIONTHRESHOLDS,ANDSOONAFTERTHAT,TILEHEADERSAREREAD,WHICHPROVIDEINFORMATIONSPECIFICTOEACHIMAGETILEFINALLY,EACHPACKETCONTAINEDINTHEBITSTREAMISREAD,ANDTHEDATAANDPARAMETERSOFEACHCODEBLOCKAREEXTRACTED,ANDFEDASINPUTSTOTHEEBCOTDECODER32EBCOTRIGHTAFTERTHEBITSTREAMSYNTAXPARSER,THESUBSEQUENTSTAGEINTHEJPEG2000DECOMPRESSIONCHAINISTHEENTROPYDECODEREBCOTFROMANALGORITHMICPOINTOFVIEW,EBCOTISABLOCKBASEDBITPLANEENCODERFOLLOWEDBYAREDUCEDCOMPLEXITYARITHMETICCODERMQITSUBDIVIDESEACHWAVELETSUBBANDINTOADISJOINTSETOFRECTANGULARBLOCKS,CALLEDCODEBLOCKSTHENTHECOMPRESSIONALGORITHMISINDEPENDENTLYAPPLIEDTOEVERYCODEBLOCKTHESAMPLESOFEVERYCODEBLOCKAREARRANGEDINTOSOCALLEDBITPLANESTODECODEACODEBLOCK,EBCOTALWAYSSTARTSFROMTHEMOSTSIGNIFICANTBITPLANES,ANDTHENMOVESTOWARDSTHELEASTSIGNIFICANTONESTHECOMPRESSEDINFORMATIONOFEVERYCODEBLOCKISTHENARRANGEDINSEVERALQUALITYLAYERS,TOCREATEASCALABLECOMPRESSEDBITSTREAMCONCEPTUALLY,EACHQUALITYLAYERMONOTONICALLYINCREASESTHEKNOWLEDGEOFSAMPLESMAGNITUDES,IEINCREASESTHEQUALITYOFTHERECONSTRUCTEDIMAGEFORMALLY,EBCOTISMADEOFTHREEMAINSTEPS,NAMELYSIGNIFICANCEPROPAGATIONSP,MAGNITUDEREFINEMENTMR,ANDCLEANUPCLEACHOFTHEABOVESTEPSCANRESORTTOFOURDECODINGPRIMITIVES,NAMELYZEROCODING,SIGNCODING,MAGNITUDEREFINEMENTCODING,ANDRUNLENGTHCODINGTHEBITPLANEVISITINGORDERFOLLOWSTHESEQUENCESPMRCLITISWORTHNOTICINGTHATEVERYSAMPLEOFAGIVENCODEBLOCKISPROCESSEDINJUSTONEOFTHETHREESTEPSASFARASCOMPUTATIONALCOMPLEXITYISCONCERNED,CLDEMANDSTHELARGESTEFFORTDURINGTHEDECODINGOFTHEMOSTSIGNIFICANTBITPLANESASSPSTEPSAREAPPLIED,ANINCREASINGNUMBEROFSAMPLESBECOMESIGNIFICANT,ANDAREINSERTEDINALISTOFMRREADYSAMPLESPROGRESSIVELY,THELOADREQUIREDBYMRSTEPSGROWS,MAKINGTHEDECODEREFFICIENCYDIRECTLYDEPENDENTONTHEMRANDCLOPTIMIZATIONLEVELDURINGTHEDEVELOPMENTOFTHEEBCOTDECODERBLOCK,PARTICULARCAREHASBEENPOSEDONTHEDESIGNOFAGILEDATASTRUCTURES,PARTICULARLYSUITEDTODSPOPTIMIZEDCCODEOFMRANDCLSTEPS33UNIFORMSCALARDEQUANTIZERACCORDINGTOL,THEQUANTIZATIONMETHODSUPPORTEDBYJPEG2000ISCALLEDSCALARUNIFORMUNIFORMSCALARDEQUANTIZATIONCANBESIMPLYACCOMPLISHEDBYMEANSOFASINGLEMULTIPLICATIONFOREACHWAVELETCOEFFICIENT34INVERSEWAVELETTRANSFORMTHEDISCRETEWAVELETTRANSFORMCANBEEVALUATEDBYMEANSOFACONVOLUTIONBASEDKERNEL,ORALIFTINGBASEDKERNEL,THISLATTERBEINGTHEDEFAULTTRANSFORMKERNELEMPLOYEDINJPEG2000ITHASBEENDEMONSTRATED4THATTHELIFTINGSCHEMEMAYRUNUPTOTWICEASFASTASCONVOLUTIONTHEWAVELETTRANSFORMHASTOBEPERFORMEDONBOTHIMAGEROWSANDCOLUMNS,INORDERTOOBTAINASEPARABLETWODIMENSIONALSUBBANDDECOMPOSITIONJPEG2000PERFORMSFIRSTTHECOLUMNWISE,ANDTHENTHEROWWISEFILTERINGTHEDEFAULTFILTERUSEDFORLOSSYCOMPRESSIONISTHEWELLKNOWNDB9,7SINCEITDOESNOTHAVERATIONALCOEFFICIENTS,PARTICULARCAREOUGHTTOBEPOSEDTOTHEEFFECTSOFFINITEPRECISIONREPRESENTATION5DUETOTHEUSEOFAFIXEDPOINTTITMS320C6201DSP,ADETAILEDSTUDYOFINTERNALDATAREPRESENTATIONHASBEENPERFORMEDEXPERIMENTALRESULTSSHOWSTHATEXCELLENTPERCEPTIVEQUALITYCANBEACHIEVEDRECURRINGTO9FRACTIONALBITSFORFILTERCOEFFICIENTSINORDERTOOPTIMIZETHEDYNAMICRANGEAROUNDZERO,ADCSHIFTISFORESEENBYTHESTANDARD,ASTHEDCCOMPONENTCOULDLEADTOANEXCESSIVEGROWTHOFTHEDYNAMICRANGEOFLOWPASSSUBBANDCOEFFICIENTSMOREOVER,THELOWPASSFILTERSCANKEEPTHESAMPLESINAFIXEDRANGE,PROVIDEDTHATAUNITARYDCFILTERGAINISGUARANTEEDTHEJOINTEFFECTOFDCCOMPONENTSUPPRESSIONANDUNITARYGAINENSURESTHATRANGEREQUIREMENTSAREFULFILLEDDURINGTHEWHOLEWAVELETTRANSFORM35ADAPTIVEREEDSOLOMONPACKETDETHEDEINTERLEAVINGRSDECODERHASBEENMAPPEDONTHEFPGADEVICEITISSPLITINTOTWOFUNCTIONALSUBBLOCKSTHEFIRSTISTHEDEINTERLEAVER,THESECONDISTHERSDECODERTHEFORMERCOLLECTSPACKETSRECEIVEDFROMCODING1332

簡(jiǎn)介：THEPREPARATIONANDWIDEFREQUENCYMICROWAVEABSORBINGPROPERTIESOFTRISUBSTITUTEDBISPHTHALONITRILE/FE3O4MAGNETICHYBRIDMICROSPHERESSHIHUADONG,MINGZHENXU,JUNJIWEI,XULINYANG,XIAOBOLIUNRESEARCHBRANCHOFFUNCTIONALMATERIALS,INSTITUTEOFMICROELECTRONICTHEMAGNETICLOSSWASENHANCEDOBVIOUSLYWITHTHEINCREASINGAMOUNTOFTPHADDITIONALLY,THEMICROWAVEABSORPTIONPROPERTIESWEREDETECTED,ANDTHERESULTSDEMONSTRATEDTHATTHESTRONGREFLECTIONLOSSRLANDMULTIMODALBANDSAPPEAREDINTHEWIDEFREQUENCYFORTHETPH/FE3O4MAGNETICHYBRIDMICROSPHERESWHENTHEMATCHINGTHICKNESSWAS50MM,THEEFFECTIVERLVALUESOF?31DB,?33DBAND?37DBWEREOBTAINEDINTHEWIDEMICROWAVEFREQUENCYRANGEFROM2GHZTO16GHZBYADJUSTINGTHECONTENTOFTPHTHEWIDEFREQUENCYMICROWAVEABSORPTIONPROPERTIES,TOGETHERWITHTHEREGULARSPHEREANDEXCELLENTMAGNETISM,ENABLETPH/FE3O4MAGNETICHYBRIDMICROSPHERESTOBEFURTHERAPPLIEDINTHEAREASOFFUNCTIONALMATERIALS2013ELSEVIERBVALLRIGHTSRESERVED1INTRODUCTIONMAGNETICHYBRIDMICROSPHERESAREANEWCLASSOFFUNCTIONALMATERIALSMADEOFMACROMOLECULARORGANICMATTERANDMAGNETITEFE3O4,WHICHPOSSESSTHEMAGNETICPROPERTIESASWELLASPROPERTIESOFORGANICMATTERITSNEWFUNCTIONSSHOWNBYTHECOMBINATIONOFTHEORGANICANDINORGANICMATTERSHAVEATTRACTEDMOREANDMOREATTENTIONWIDELYRESEARCHESANDAPPLICATIONHAVEBEENIMPLEMENTEDINTHEFIELDSOFBIOTECHNOLOGY,CHEMICAL,MILITARYSTEALTHMATERIAL,ENVIRONMENTALREMEDIATIONANDSOON1–4INRECENTYEARS,VARIOUSMETHODSHAVEBEENSUCCESSFULLYADOPTEDINPREPARINGFE3O4MICROSPHERES,SUCHASCORE–SHELLPROCESSES,EMULSIONPOLYMERIZATION,MINIEMULSIONPOLYMERIZATION,DISPERSIONPOLYMERIZATION,SUSPENSIONPOLYMERIZATION,ANDCROSSLINKING,ETCHOWEVER,OFALLTHESEMETHODS,THESTRUCTUREANDPERFORMANCEOFTHEPREPAREDMAGNETICHYBRIDMICROSPHERESSHOWITSPROSANDCONS5–9THEPROCESSOFTEDIOUSANDTIMECONSUMINGPREPARATION,BROADSIZEDISTRIBUTIONANDNOUNIFORMMAGNETITEFRACTIONARENOWTHENEWCHALLENGESTODEVELOPMAGNETICCOMPOSITESINADDITION,TOOBTAINIDEALABSORBINGMATERIALSWITHTHINTHICKNESSANDLOWDENSITYASWELLASBROADBANDMICROWAVEABSORPTION,FURTHERRESEARCHESARESTILLESSENTIALRECENTLY,AFACILEONESTEPSOLVENTTHERMALCRYSTALLIZATIONROUTETOPREPAREFE3O4MICROSPHERESHASBEENDEVELOPEDINOURLAB10,11THERESULTSINDICATEDTHATTHEFE3O4HYBRIDMICROSPHERESEXHIBITEDUNIFORMPARTICLESIZE,STABLEMAGNETISMANDSPECIALSTRONGMICROWAVEABSORPTIONPROPERTIES,WHICHEFFICIENTLYSOLVEDTHEABOVEPROBLEMSANDWASWIDELYAPPLIEDINMULTIFUNCTIONALMAGNETICMATERIALSACCORDINGTOTHELITERATURE,INTHEFORMATIONPROCESSOFPOLYMER/FE3O4MAGNETICHYBRIDMICROSPHERES,THEMICROSPHERESPROPERTIESDEPENDONTHETYPEANDMOLECULARSTRUCTUREOFTHEORGANICSPECIESDIFFERENTKINDSOFTHEPOLYMERCOMBINEDWITHFE3O4WILLPOSSESSDIFFERENTSTRUCTUREANDMORPHOLOGY,RESULTINGINTHEDISTINGUISHABLEMICROWAVEABSORPTIONPROPERTIESASREPORTED,THEPHTHALOCYANINEBASEDPOLYMER/FE3O4MAGNETICHYBRIDSCANCONTENTSLISTSAVAILABLEATSCIENCEDIRECTJOURNALHOMEPAGEWWWELSEVIERCOM/LOCATE/JMMMJOURNALOFMAGNETISMANDMAGNETICMATERIALS03048853/SEEFRONTMATTER2013ELSEVIERBVALLRIGHTSRESERVEDHTTP//DXDOIORG/101016/JJMMM201308038NCORRESPONDINGAUTHORTEL/FAXT8602883207326EMAILADDRESSLIUXBUESTCEDUCNXLIUJOURNALOFMAGNETISMANDMAGNETICMATERIALS349201415–2032MAGNETICPROPERTIESTHEHYSTERESISLOOPSFORTPH/FE3O4MAGNETICHYBRIDMICROSPHERESWITHVARIOUSCONTENTOFTPHAREINVESTIGATEDATROOMTEMPERATUREUSINGAVIBRATINGSAMPLEMAGNETOMETERVSM,ASSHOWNINFIG4THESATURATIONMAGNETIZATIONANDTHECOERCIVITYVALUESOFTHETHREESAMPLESARESUMMARIZEDINTABLE1THESAMPLESDEMONSTRATEBETTERMAGNETICPROPERTIESTHANTHOSEREPORTEDINLITERATURES17THESATURATIONMAGNETIZATIONVALUESDECREASEALONGWITHTHEINCREASINGAMOUNTOFTPH,BUTSTILLREACH5816EMUG?1THEREASONSHOULDBETHEEXISTENCEOFTHEORGANICMATTERINTHEFE3O4PARTICLESANDTHELOWERCRYSTALLIZATIONOFMAGNETITEINTHEHYBRIDMICROSPHERES17ANDS3HASLOWERSATURATIONMAGNETIZATIONTHANTHEOTHERTWOSAMPLESS1,S2,WHICHISGIVENRISETOTHEBIGGESTSPINUNORDEREDDEGREEANDTHEINCREASINGSURFACEAREAOFTHEMAGNETICHYBRIDMICROSPHERESITCANBESEENFROMTHETABLETHATTHECOERCIVITYINCREASESALONGWITHTHEINCREASINGAMOUNTOFTPHS1HASTHELOWESTCOERCIVITY,WHICHCANBEATTRIBUTEDTOTHEINCREASEDINTERPARTICLEDIPOLARINTERACTIONFROMTHEDECREASEDNANOPARTICLESPACEDISTANCEFORTHESINGLEDOMAINNANOPARTICLES1433MICROWAVEELECTROMAGNETICPROPERTIESITISWELLKNOWNTHATDIELECTRICCONSTANTANDPERMEABILITYARETHEINTRINSICPARAMETERSOFELECTROMAGNETICPROPERTYFORWAVEABSORBINGMATERIALBOTHDIELECTRICCONSTANTANDPERMEABILITYCANBEUSEDINTHEPLURALUNDERALTERNATINGMAGNETIC18INFIG5A,THEDIELECTRICLOSSCANBEEXPRESSEDASTHERATIOOFTHEIMAGINARYPARTΕ″ANDTHEREALPARTΕ′OFCOMPLEXPERMITTIVITYTHERANGEOFTHEDIELECTRICLOSSISBETWEEN001AND016WHENTHEFREQUENCYISLOWERTHAN7GHZ,THEDIELECTRICLOSSCURVESOFTHESAMPLESAREALLSMALLANDSIMILARANDTHEDIELECTRICLOSSMAXIMUMSOFS1,S2ANDS3APPEARATABOUT17GHZ,FIG2FTIRSPECTRAOFS1,S2ANDS3FIG1SEMIMAGESOFAPUREFE3O4,BS1,CS2ANDDS3SDONGETAL/JOURNALOFMAGNETISMANDMAGNETICMATERIALS349201415–2017

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簡(jiǎn)介：JOURNALOFCONTROLTHEORYANDAPPLICATIONS20075183–88DOI101007/S1176800552586NEUROFUZZYGENERALIZEDPREDICTIVECONTROLOFBOILERSTEAMTEMPERATUREXIANGJIELIU1,JIZHENLIU1,PINGGUAN21DEPARTMENTOFAUTOMATION,NORTHCHINAELECTRICPOWERUNIVERSITY,BEIJING102206,CHINA2DEPARTMENTOFAUTOMATION,BEIJINGINSTITUTEOFMACHINERY,BEIJING100085,CHINAABSTRACTPOWERPLANTSARENONLINEARANDUNCERTAINCOMPLEXSYSTEMSRELIABLECONTROLOFSUPERHEATEDSTEAMTEMPERATUREISNECESSARYTOENSUREHIGHEFFICIENCYANDHIGHLOADFOLLOWINGCAPABILITYINTHEOPERATIONOFMODERNPOWERPLANTANONLINEARGENERALIZEDPREDICTIVECONTROLLERBASEDONNEUROFUZZYNETWORKNFGPCISPROPOSEDINTHISPAPERTHEPROPOSEDNONLINEARCONTROLLERISAPPLIEDTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREOFA200MWPOWERPLANTFROMTHEEXPERIMENTSONTHEPLANTANDTHESIMULATIONOFTHEPLANT,MUCHBETTERPERFORMANCETHANTHETRADITIONALCONTROLLERISOBTAINEDKEYWORDSNEUROFUZZYNETWORKSGENERALIZEDPREDICTIVECONTROLSUPERHEATEDSTEAMTEMPERATURE1INTRODUCTIONCONTINUOUSPROCESSINPOWERPLANTANDPOWERSTATIONARECOMPLEXSYSTEMSCHARACTERIZEDBYNONLINEARITY,UNCERTAINTYANDLOADDISTURBANCE1,2THESUPERHEATERISANIMPORTANTPARTOFTHESTEAMGENERATIONPROCESSINTHEBOILERTURBINESYSTEM,WHERESTEAMISSUPERHEATEDBEFOREENTERINGTHETURBINETHATDRIVESTHEGENERATORCONTROLLINGSUPERHEATEDSTEAMTEMPERATUREISNOTONLYTECHNICALLYCHALLENGING,BUTALSOECONOMICALLYIMPORTANT3FROMFIG1,THESTEAMGENERATEDFROMTHEBOILERDRUMPASSESTHROUGHTHELOWTEMPERATURESUPERHEATERBEFOREITENTERSTHERADIANTTYPEPLATENSUPERHEATERWATERISSPRAYEDONTOTHESTEAMTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREINBOTHTHELOWANDHIGHTEMPERATURESUPERHEATERSPROPERCONTROLOFTHESUPERHEATEDSTEAMTEMPERATUREISEXTREMELYIMPORTANTTOENSURETHEOVERALLEFFICIENCYANDSAFETYOFTHEPOWERPLANTITISUNDESIRABLETHATTHESTEAMTEMPERATUREISTOOHIGH,ASITCANDAMAGETHESUPERHEATERANDTHEHIGHPRESSURETURBINE,ORTOOLOW,ASITWILLLOWERTHEEFFICIENCYOFTHEPOWERPLANTITISALSOIMPORTANTTOREDUCETHETEMPERATUREFLUCTUATIONSINSIDETHESUPERHEATER,ASITHELPSTOMINIMIZEMECHANICALSTRESSTHATCAUSESMICROCRACKSINTHEUNIT,INORDERTOPROLONGTHELIFEOFTHEUNITANDTOREDUCEMAINTENANCECOSTSASTHEGPCISDERIVEDBYMINIMIZINGTHESEFLUCTUATIONS,ITISAMONGSTTHECONTROLLERSTHATAREMOSTSUITABLEFORACHIEVINGTHISGOALTHEMULTIVARIABLEMULTISTEPADAPTIVEREGULATORHASBEENAPPLIEDTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREINA150T/HBOILER3,ANDGENERALIZEDPREDICTIVECONTROLWASPROPOSEDTOCONTROLTHESTEAMTEMPERATURE4ANONLINEARLONGRANGEPREDICTIVECONTROLLERBASEDONNEURALNETWORKSISDEVELOPEDIN5TOCONTROLTHEMAINSTEAMTEMPERATUREANDPRESSURE,ANDTHEREHEATEDSTEAMTEMPERATUREATSEVERALOPERATINGLEVELSTHECONTROLOFTHEMAINSTEAMPRESSUREANDTEMPERATUREBASEDONANONLINEARMODELTHATCONSISTSOFNONLINEARSTATICCONSTANTSANDLINEARDYNAMICSISPRESENTEDIN6FIG1THEBOILERANDSUPERHEATERSTEAMGENERATIONPROCESSFUZZYLOGICISCAPABLEOFINCORPORATINGHUMANEXPERIENCESVIATHEFUZZYRULESNEVERTHELESS,THEDESIGNOFFUZZYLOGICCONTROLLERSISSOMEHOWTIMECONSUMING,ASTHEFUZZYRULESAREOFTENOBTAINEDBYTRIALSANDERRORSINCONTRAST,NEURALNETWORKSNOTONLYHAVETHEABILITYTOAPPROXIMATENONLINEARFUNCTIONSWITHARBITRARYACCURACY,THEYCANALSOBETRAINEDFROMEXPERIMENTALDATATHENEUROFUZZYNETWORKSNFNSDEVELOPEDRECENTLYHAVETHEADVANTAGESOFMODELTRANSPARENCYOFFUZZYLOGIC,ANDLEARNINGCAPABILITYOFNEURALNETWORKS7THENFNSHAVEBEENUSEDTODEVELOPSELFRECEIVED14OCTOBER2005REVISED14OCTOBER2006THISWORKWASSUPPORTEDBYTHENATURALSCIENCEFOUNDATIONOFBEIJINGNO4062030,NATIONALNATURALSCIENCEFOUNDATIONOFCHINANO50576022,69804003,SCIENTIFICRESEARCHCOMMONPROGRAMOFBEIJINGMUNICIPALCOMMISSIONOFEDUCATIONKM200611232007XLIUETAL/JOURNALOFCONTROLTHEORYANDAPPLICATIONS20075183–88853NEUROFUZZYNETWORKGENERALIZEDPREDICTIVECONTROLTHEGPCISOBTAINEDBYMINIMIZINGTHEFOLLOWINGCOSTFUNCTION10,JEN?JDQJ?YTJ?YRTJ2M?J1ΛJΔUTJ?12,7WHEREQJANDΛJARERESPECTIVELYTHEWEIGHTINGFACTORSFORTHEPREDICTIONERRORANDTHECONTROL,YRTJISTHEJTHSTEPAHEADREFERENCETRAJECTORY,DISTHEMINIMUMCOSTINGHORIZON,NANDMARERESPECTIVELYTHEMAXIMUMCOSTINGHORIZONFORTHEPREDICTIONERRORANDTHECONTROLTHECONTROLCOMPUTEDFROMTHENFGPCISTHEWEIGHTEDSUMOFTHECONTROLOBTAINEDFROMPLOCALGPCCONTROLLERSΔUTP?I1ΑIΔUIT,8WHEREΔUITISTHECONTROLINTHEITHREGION,ΑIXISDEFINEDPREVIOUSLYIN4NOTETHATTHEWEIGHTSINTHENFGPCAREIDENTICALTOTHATINTHENFNTHATMODELSTHEPROCESSSINCESWITCHINGBETWEENLOCALGPCCONTROLLERSINTHENFGPCINVOLVESFUZZYLOGICS,ITCANBEINTERPRETEDNOTONLYASAFUZZYCONTROLLER,BUTALSOASAFUZZYSUPERVISORTHECONTROLCANBESMOOTHIFTHEWEIGHTSΑIXARESUITABLYSELECTEDFROMTHENFN6ANDTHECONTROL8,JGIVENBY7CANBEREWRITTENASJEN?JDQJP?I1ΑI?YITJ?YRTJ2M?J1ΛJP?I1ΑIΔUITJ?129THECOSTFUNCTIONISSIMPLIFIEDFIRSTUSINGTHECAUCHYINEQUALITYSINCEP?I1ΑI?YITJ?YRTJ2?PP?I1ΑI?YITJ?YRTJ2,HENCEP?I1ΑIΔUITJ?12?PP?I1ΑIΔUITJ?1210EQUATION10IMPLIESTHATTHESUMOFTHEWEIGHTEDSQUAREDERRORSCANBEANUPPERBOUNDOFTHECOSTFUNCTIONJREWRITING9GIVESEN?JDP?I1QJΑI?YTJ?YRTJ2M?J1P?I1ΛJΑIΔUITJ?12EP?I1ΑI2N?JDQJ?YITJ?YRTJ2P?I1ΑI2M?J1ΛJΔUITJ?12P?I1ΑI2JI,11WHEREJIEN?JDQJ?YITJ?YRTJ2M?J1ΛJΔUITJ?1212EQUATION11SHOWSTHATMINIMIZINGJIISESSENTIALLYTHESAMEASTHATOFMINIMIZINGJFROM12,ASETOFLOCALGENERALIZEDPREDICTIVECONTROLLERSISOBTAINED,WHICHFORMSPARTOFTHENFGPCTHELOCALGPC10ISGIVENBY,ΔUITGTIQIGIΛI(xiàn)?1GTIQIYRT1?FIΔUIT?1?SIZ?1YIT,13WHEREYRT1?YRT1,?YRT2,,?YRTNT,ΔUITΔUIT,ΔUIT1,,ΔUITM?1T,ΔUIT?1ΔUIT?NB,ΔUIT?NB1,,ΔUIT?1T,SIZ?1SI1Z?1,SI2Z?1,,SINZ?1TSIZ?1ANDRIZ?1SATISFYTHEDIOPHANTINEEQUATION1ˉAIZ?1RIJZ?1Z?JSIJZ?1,14ANDGIJZ?1BIZ?1RIJZ?1GIJ,0GIJ,1Z?1GIJ,NBJ?1Z?NBJ?1,15AQIDIAGQI1,QI2,,QIN,15BΛI(xiàn)DIAGΛI(xiàn)1,ΛI(xiàn)2,,ΛI(xiàn)M,15CGTI???????GI1,0GI2,1GIN,N?1GI1,0GIN?1,N?20GIN?M1,N?M???????,15DFI???????GI1,NBGI1,NB?1GI1,2GI1,1GI2,NB1GI2,NBGI2,3GI2,2GIN,NBN?1GIN,NBN?2GIN,N1GIN,N???????15ETHEOPTIMIZEDMSTEPSAHEADCONTROLISCOMPUTED,ANDONLYTHEFIRSTSTEPAHEADCONTROLISIMPLEMENTED,USINGARECEDINGHORIZONPRINCIPLE10,GIVINGΔUITDTI1YRT1?FIΔUIT?1?SIZ?1YIT,16

簡(jiǎn)介：HIGHPERFORMANCECONTROLOFABOOSTACDCPWMRECTIFIERINDUCTIONGENERATORSYSTEMJYOTISASTRY,OLORUNFEMIOJO,ZHIQIAOWUDEPARTMENTOFELECTRICALANDCOMPUTERENGINEERING/CENTERFORENERGYSYSTEMSRESEARCHLABORATORYFORELECTRICMACHINESANDPOWERELECTRONICSTENNESSEETECHNOLOGICALUNIVERSITYCOOKEVILLE,TN38505,USAPHONE9313723869,FAX9313723436,EMAILJOJOTNTECHEDUABSTRACTTHISPAPERPRESENTSACONTROLMETHODOLOGYFORTHEDCVOLTAGEREGULATIONOFANINDUCTIONGENERATORACDCBOOSTRECTIFIERSYSTEMINWHICHTHECOPPERLOSSOFTHEGENERATORISMINIMIZEDWITHTHEAIDOFANINPUTOUTPUTLINEARIZATIONTECHNIQUE,WHICHLINEARIZESANDDECOUPLESTHEMODELEQUATIONSINTHESYNCHRONOUSREFERENCEFRAME,AROTORFLUXVECTORCONTROLTYPEHIGHPERFORMANCEISACHIEVEDSTEADYSTATEANALYSISPROVIDESSOMEINSIGHTSINTOTHEOPERABILITYREGIMEOFTHEGENERATORTHEEFFECTIVENESSOFTHECONTROLSCHEMEUNDERDIFFERENTLOADCONDITIONSASWELLASVARYINGROTORSPEEDSHASBEENDEMONSTRATEDBYCOMPUTERSIMULATIONSSOMEEXPERIMENTALRESULTSAREINCLUDEDINDEXWORDSINDUCTIONGENERATOR,ACDCPWMRECTIFIER,EXCITATION,INPUTOUTPUTLINEARIZATION,BUTTERWORTHPOLYNOMIALS,MAGNETIZINGFLUXSATURATIONIINTRODUCTIONGROWINGRESEARCHANDREALAPPLICATIONINTERESTSINALTERNATIVEENERGYSYSTEMSSUCHASWINDENERGYHASINCREASEDTHEUSEOFTHEINDUCTIONMACHINEASAGENERATORBECAUSEOFINHERENTADVANTAGESOFSUCHASLOWCOST,REDUCEDMAINTENANCE,RUGGEDANDSIMPLECONSTRUCTION,BRUSHLESSROTORSQUIRRELCAGEANDSOONTHEOPERATIONOFANINDUCTIONMACHINEASAMOTORORGENERATORISDETERMINEDBYTHEOPERATINGSLIPOFTHEMACHINEAPOSITIVEOPERATIONALSLIPWOULDINDICATETHEOPERATIONOFTHEMACHINEASAMOTOR,ANDANEGATIVESLIPWOULDINDICATETHEGENERATINGMODEOFTHEMACHINEITISWELLKNOWNTHATANINDUCTIONMACHINECANBEMADETOWORKASASELFEXCITEDGENERATOR,IETHEGENERATORCANBEEXCITEDBYTHEACONNECTIONOFTHREECAPACITORSATTHESTATORTERMINALSOFTHEMACHINEBBYUSINGANINVERTER/RECTIFIERSYSTEM1INTHECASEOFTHEINVERTER/RECTIFIERSYSTEM,THEDCSIDECAPACITORAPPEARSLIKETHREEPHASECAPACITORSDUETOTHESWITCHINGSIGNALSOFTHEINVERTER,ANDTHESINGLEDCCAPACITOROFTHERECTIFIERPROVIDESTHEREQUIREDEXCITATIONFORTHEINDUCTIONGENERATORANEXTENSIVEOVERVIEWILLUSTRATINGTHEVASTAMOUNTOFWORKDONEINDIFFERENTAREASOVERTHELAST25YEARSSUCHASSELFEXCITATION,VOLTAGEBUILDUPMODELING,STEADYSTATEANALYSISOFANINDUCTIONMACHINEHASBEENPRESENTEDIN2ALSO,INPREVIOUSLYPUBLISHEDWORK,THEVECTORCONTROLOFTHEINDUCTIONGENERATORRECTIFIERSYSTEMTOPRODUCEDCPOWERINWHICHTHERECTIFIERALSOPROVIDESTHEEXCITATIONHASBEENREPORTED3THESYSTEMHASBEENSTUDIEDSPECIFICALLYFORAPPLICATIONSRELATEDTOWINDENERGY,THEREBYSTUDYINGTHECONTROLLERRESPONSEFORVARYINGROTORSPEEDSALSOTHESTABILITYOFANINDUCTIONGENERATORRECTIFIERUNDERFIELDORIENTATIONCONTROLHASBEENSTUDIEDIN4,HIGHLIGHTINGTHEPOSSIBLEINSTABILITYOFANINDUCTIONGENERATORUSEDINHIGHSPEEDAPPLICATIONSTHECONTROLMETHODADOPTEDINTHISPAPERHASBEENLAIDOUTINDETAIL,ILLUSTRATINGTHEINPUTOUTPUTLINEARIZATIONMETHODUSEDINSEPARATINGTHELINEARFROMTHENONLINEARTERMSINTHESYSTEMMODELEQUATIONSTHEPROPOSEDCONTROLSCHEMEHASBEENTESTEDFORITSEFFECTIVENESSBYVARYINGLOADCONDITIONSASWELLASVARYINGTHEROTORSPEEDOFTHEMACHINETHEMACHINEHASBEENOPERATEDATACONDITIONOFMINIMUMCOPPERLOSS5THECONDITIONOFMINIMUMLOSSISACHIEVEDBYREGULATINGTHECOMMANDROTORFLUXUSINGALOSSMINIMIZATIONFUNCTIONTHESTEADYSTATEANALYSISDEALSWITHTHEOPERATIONOFTHESELFEXCITEDGENERATORUNDERCONDITIONSOFSATURATION6THEINDUCTIONMACHINEHASBEENSTUDIEDFORITSOUTPUTPOWERCAPABILITYANDTHEEFFECTOFTHEPARAMETERSOFTHEMACHINEONTHEOPERATIONOFTHEMACHINEUNDERDIFFERENTLOADCONDITIONSTHEANALYSISINTHISPAPERAIMSATHIGHLIGHTINGTHEEFFECTOFTHEMAGNETIZINGFLUXONTHEEXCITATIONREQUIREMENTSOFTHESYSTEMWITHTHEMAGNITUDEOFTHEMODULATIONSIGNALASAMEASUREOFTHEREQUIREDEXCITATIONBYFIXINGTHEMAGNETIZINGFLUXLINKAGETHEREQUIREDMODULATIONINDEXFOREXCITATIONOFTHEMACHINECANBEDETERMINEDALONGWITHTHEEFFECTOFSATURATION,THESYSTEMHASBEENSTUDIEDUNDERACONDITIONOFMINIMUMCOPPERLOSSTHEORGANIZATIONOFTHISPAPERISASFOLLOWSSECTIONSIIANDIIIDETAILTHEMODELSOFTHETHREEPHASEBOOSTRECTIFIERANDINDUCTIONGENERATORRESPECTIVELYTHEMODELOFTHECOMBINEDSYSTEMHASALSOBEENINCLUDEDINSECTIONIIISECTIONIVDEALSWITHTHESTEADYSTATEANALYSISOFTHEIAS200510070780392086/05/2000?2005IEEEIAS200510070780392086/05/2000?2005IEEETHEEFFECTOFMAGNETICAIRGAPFLUXLINKAGESATURATIONISTAKENINTOACCOUNT6THEANALYSISAIMSATDETERMININGTHEVALUEOFTHEMAGNITUDEOFTHEMODULATIONINDEXREQUIREDFORTHEEXCITATIONOFTHEGENERATOR,TAKINGINTOACCOUNTTHEEFFECTOFSATURATIONANDACONDITIONOFMINIMUMTOTALCOPPERLOSSINTHEMACHINEUNDERSATURATEDCONDITIONS,THEMAGNETIZINGINDUCTANCEVARIESWITHTHEMAGNETIZINGFLUXASSHOWNINFIGURE2FORA2HPINDUCTIONMACHINETHEREFERENCEFRAMETRANSFORMATIONANGLEOFTHEVOLTAGEEQUATIONSASSUMESTHEALIGNMENTOFTHEQAXISWITHTHEMAGNETIZINGFLUXLINKAGEHENCE,THEDAXISMAGNETIZINGFLUXISASSUMEDTOBEZERO,ANDTHEDAXISMAGNETIZINGINDUCTANCEISCONSTANTHOWEVERTHEQAXISMAGNETIZINGINDUCTANCEISAFUNCTIONOFTHEMAGNETIZINGFLUX,WHICHISAPPROXIMATEDBYAPOLYNOMIALGIVENIN1326871154754181MMMQLΛΛ?13QDRSQDSSSDCQDSBPJTVMΛΛΩ??214QDSRQDRSRBPJTΛΛΩ?015LDCQSQDSDCRVIMALCPV?RE2316WHEREΩSISTHESLIPFREQUENCYDEFINEDASRESΩΩΩ?DLRTRSSDLRBMRR?DLRTSRRDLRBMSS?2MRSLLLD?FIG2VARIATIONOFTHERECIPROCALOFTHEMAGNETIZINGINDUCTANCEWITHTHEMAGNETIZINGFLUXDURINGSTEADYSTATE,THEDERIVATIVES,PIN1416AREZEROALONGWITHACCOUNTINGFORMAGNETICSATURAT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簡(jiǎn)介：關(guān)于SBS改性瀝青配合比設(shè)計(jì)及施工技術(shù)的探討摘要在良好的設(shè)計(jì)配合比和施工條件下，SBS瀝青能使瀝青路面的耐久性和高溫穩(wěn)定性明顯提高。本文將根據(jù)南二路的施工試驗(yàn)情況，簡(jiǎn)要講述SBS改性瀝青的施工技術(shù)要求。一、SBS改性瀝青概述SBS改性瀝青是在原有基質(zhì)瀝青（AH70）的基礎(chǔ)上，摻加25、30、40％的SBS改性劑，改性后的瀝青，與原瀝青相比，其高溫粘度增大，軟化點(diǎn)升高。在良好的設(shè)計(jì)配合比和施工條件下，瀝青路面的耐久性和高溫穩(wěn)定性明顯提高。在SBS改性瀝青生產(chǎn)過(guò)程中進(jìn)行了大量的室內(nèi)試驗(yàn)，生產(chǎn)后對(duì)其技術(shù)指標(biāo)進(jìn)行了現(xiàn)場(chǎng)實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明，外摻30SBS的改性瀝青，軟化點(diǎn)、針入度等指標(biāo)均滿足改性瀝青規(guī)范要求，可用SBS改性瀝青做瀝青混合料的配合比設(shè)計(jì)。二、SBS瀝青混合料的配合比設(shè)計(jì)為了使設(shè)計(jì)的混合料能夠達(dá)到實(shí)施效果，需要從材料要求、施工工藝、質(zhì)量控制標(biāo)準(zhǔn)和質(zhì)量控制方法等諸多方面提出以下要求，希望能夠引起注意。（一）、原材料要求11粗集料用于改性瀝青混合料面層的粗集料，宜采用碎石或碎礫石，其粒徑規(guī)格和質(zhì)量要求均應(yīng)符合公路瀝青路面施工技術(shù)規(guī)范（JTJ03294）的規(guī)定1粗集料應(yīng)潔凈、干燥、無(wú)風(fēng)化、無(wú)有害雜質(zhì)，且具有一定硬度和強(qiáng)度。2粗集料應(yīng)具有良好的顆粒形狀，破碎礫石用于高速公路、一級(jí)公路時(shí)，應(yīng)采用大礫石破碎，并至少應(yīng)有兩個(gè)以上的破碎面。3、對(duì)于抗滑表層粗集料應(yīng)選擇硬質(zhì)巖（中性或基性火成巖）。由于硬質(zhì)巖石與瀝青的粘接力存在著較大差異，粗集料與瀝青的粘附性應(yīng)不小于4級(jí)。對(duì)于3－5MM石屑部分由于含量較低，并且該部分對(duì)瀝青混合料形成嵌接結(jié)構(gòu)有一定的作用，建議用硬質(zhì)巖石屑（玄武巖）。12細(xì)集料細(xì)集料包括人工砂、天然砂。瀝青路面面層宜采用人工砂作為細(xì)集料，細(xì)集料應(yīng)潔凈、干燥、無(wú)風(fēng)化、無(wú)有害雜質(zhì)，有適當(dāng)?shù)念w粒組成，并與改性瀝青有良好的粘附性，天然砂由于質(zhì)量變化大（大部分為中粗砂），形狀較圓滑，與瀝青的粘附性差，對(duì)瀝青混合料影響較大。對(duì)于高速公路、一級(jí)公路瀝青混合料，天然砂的含量不宜超過(guò)20％，可用03MM的石屑粉代替天然砂。13填充料施1、采用“瀝青混合料馬歇爾穩(wěn)定度試驗(yàn)”方法測(cè)定的48H浸水馬歇爾穩(wěn)定度試驗(yàn)殘留穩(wěn)定度不應(yīng)小于80。（2）、采用“瀝青混合料凍融劈裂試驗(yàn)”方法測(cè)定的劈裂強(qiáng)度比不應(yīng)小于80。三、SBS瀝青混合料的施工SBS瀝青是一種以彈性塑膠類改性瀝青，正確使用可以顯著提高瀝青面層的抗車轍性能，增加耐久性，增加抗老化能力，延長(zhǎng)公路的壽命。與AH70基質(zhì)瀝青相比，SBS瀝青的粘度和軟化點(diǎn)顯著增加，SBS瀝青的運(yùn)輸儲(chǔ)存和路面面層施工有一些與基質(zhì)瀝青不同的要求，只有正確使用才能達(dá)到預(yù)期效果。1、拌合、運(yùn)輸?shù)募夹g(shù)要求為保證瀝青混合料的質(zhì)量更穩(wěn)定，瀝青用量更準(zhǔn)確，宜采用間隙式拌和機(jī)拌和。拌和必須均勻，只有SBS瀝青改性劑完全分散在瀝青中，才能充分發(fā)揮其效能，對(duì)于密級(jí)配（AC13I）混合料，應(yīng)做到拌合后的混合料均勻一致，無(wú)細(xì)料和粗料分離及花白、結(jié)成團(tuán)塊的現(xiàn)象。由于SBS改性瀝青混合料的施工溫度要求較高，建議拌合溫度控制在160℃,運(yùn)輸車必須加蓋篷布或其它保溫材料，防止結(jié)合料表面結(jié)硬，為確保攤鋪連續(xù)以及平整度大小符合技術(shù)規(guī)范要求，必須保證攤鋪機(jī)前至少兩輛車等待卸料，決不能出現(xiàn)攤鋪機(jī)等車的現(xiàn)象。其余要求應(yīng)滿足改性瀝青路面施工技術(shù)規(guī)范的技術(shù)要求。2、攤鋪的技術(shù)要求SBS瀝青混合料在攤鋪時(shí)應(yīng)盡量連續(xù)不斷的施工，以減少攤鋪機(jī)和壓路機(jī)的停頓，應(yīng)盡量減少橫縫，提高其面層平整度。為提高路面的平整度，表面層宜采用攤鋪前后保持相同高差的雪橇式攤鋪厚度控制方式。由于SBS瀝青粘度較大，粘附力強(qiáng)，用部分?jǐn)備仚C(jī)的后雪橇是膠輪式結(jié)構(gòu)，膠輪易粘附混合料細(xì)顆粒，影響平整度，所以攤鋪機(jī)后雪橇是膠輪式結(jié)構(gòu)的必須改成鋼滑靴式結(jié)構(gòu)。攤鋪速度應(yīng)控制在2米/分鐘，做到緩慢、均勻、連續(xù)不間斷地?cái)備?，禁止隨意變換速度或中途停頓。提高攤鋪過(guò)程中的預(yù)壓密實(shí)度。改性瀝青SBS混合料在高溫狀態(tài)下主要是靠粗集料的嵌擠作用，可適當(dāng)提高夯錘振搗頻率，使剩余壓實(shí)系數(shù)減少，初壓的痕跡也極小，進(jìn)而確保路面的最終平整度。3、碾壓的技術(shù)要求對(duì)于密級(jí)配型混合料，其適宜的碾壓溫度范圍是130℃－150℃，其最終碾壓溫度不低于110℃。SBS瀝青混合料的壓實(shí)工藝本著以下原則進(jìn)行按照“緊跟、慢壓、高頻、低幅”“碾壓八字方針進(jìn)行碾壓，壓路機(jī)必須緊跟攤鋪機(jī)的后面，只有在高溫條件下碾壓才能取得更好的效果，壓實(shí)速度控制在4－5KM/H。碾壓速度均衡，倒退時(shí)關(guān)閉振動(dòng)，方向要逐漸地改變，不許擰著彎行走，對(duì)每一道碾壓起點(diǎn)或終點(diǎn)可稍微扭彎碾壓，消除碾壓接頭輪跡。決不允許

簡(jiǎn)介：中文中文4480字畢業(yè)設(shè)計(jì)外文資料翻譯畢業(yè)設(shè)計(jì)外文資料翻譯題目環(huán)氧樹脂的力學(xué)性能學(xué)院材料科學(xué)與工程專業(yè)復(fù)合材料與工程班級(jí)學(xué)生學(xué)號(hào)20060103027指導(dǎo)教師指導(dǎo)教師二〇一〇年三月二十日2脂塑性變形的后固化溫度對(duì)塑性變形的影響，并且解釋了各個(gè)理論的各個(gè)條款，發(fā)展到能解釋玻璃狀聚合物的塑性變形。在第二部分我們展示了在這些材料中的裂紋擴(kuò)展是受樹脂的塑性變形行為控制。2實(shí)驗(yàn)過(guò)程21成型和制樣目前此研究用的樹脂是一個(gè)商用雙酚A二縮水甘油醚（DGEBA），與EPIKOTE828（殼牌）的三乙烯四胺（TETA），經(jīng)過(guò)初級(jí)混合得到二級(jí)胺。成型條件的詳細(xì)內(nèi)容被記載到以前的出版物上1113。固化劑使用的四種不同的數(shù)量是74,98,123和147每份。對(duì)于所有的初始硬化成型反應(yīng)可以在不同時(shí)期高溫固化前在室溫下反應(yīng)24小時(shí)。鑄造樹脂在形式上有兩種不同的厚度39和63RAM。22楊氏模量的測(cè)定把39MM的模壓樹脂片切成30MM70RAM的矩形片。樹脂的系數(shù)由三點(diǎn)彎曲確定，用一款英斯特機(jī)械測(cè)試機(jī)作用在不同的跨桿頭速度下得到。圖解說(shuō)明如圖1A。載荷P和試樣中心位移Y的關(guān)系體現(xiàn)在圖1B。而且P與Y的比值通過(guò)方程與樹脂的楊氏模量有關(guān)14，式中的B和H分別是試樣矩形截面的寬度和厚度。兩個(gè)支撐點(diǎn)之間的距離是L樹脂的模量是E。位移Y取決于跨頭的運(yùn)動(dòng)，同時(shí)要考慮到機(jī)器的柔韌性。圖1楊氏模量的確定A三點(diǎn)彎曲測(cè)試B載荷位移曲線。23屈服應(yīng)力的測(cè)量用63毫米的樹脂板加工的長(zhǎng)10毫米直徑5毫米的圓柱樣本，他們?cè)诃h(huán)境室的英斯特朗測(cè)試機(jī)的拋光潤(rùn)滑板之間發(fā)生單軸壓縮變形如圖2A。公稱應(yīng)變E取決于

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