简介:Theanalysisofthesolutionoffluidnetworkmodeliscarriedouttomatchtheneedofgraphicallymodularauto-modellingforpowerplantsimulatorsbecauseofthesymmetryandsparsityofthelinearsystemofequations,anewmethodofimprovedGausseliminationispresentedforthesolutionoflargescalematrices.ComparisonofthenewmethodwiththeclassicalGausseliminationmethod,theGauss-Seideliterativemethodaregiven.Theresultsshowthatthealgorithmprovidedisbetterthantheothersandissuitableforauto-modellingoffluidnetowrksofpowerplants.
简介:<正>China’sautomobileindustryhadagoodstartintheyearof2014.Lastyear,thosemajorautoenterprisesransmoothly,whoseleadingeco-nomicindicatorsremainedasteadygrowth.AccordingtoChinaAssociationofAutomobileManufacturers(CAAM),inJanuary,theproductionandsalesofautomobilesinChinakepta
简介:The1994ChinaInternationalAutoExpositionattractedsomeoftheworld’sfamouscarmakerssuchasGermany’sBenz,theSwedishSAAD,Japan’sMazdaandKorea’sHyundai.TheonlyChineseenterpriserivalingtheseinternationalgiantsistheShanghaiVolkswagenAutoCo.,Ltd.whoseluxuryShanghaiSantanacararousedgreatinterestamongitscounterpartsathomeandabroad.SituatedintheAntingIndustrial
简介:TangShuangning,ViceChairmanofChinaBankingRegulatoryCommissioninspeechinSino-JapanEconomyForumofHighLevelrevealedthatuntilMaythisyear,61foreignfinancialinstitutionsandenterprisesfrom19countriesandregionshasestablished199operationalfinancialinstitutionsin21citiesinChinaincludingthreeforeignautomobilefinancialcompanies.
简介:Sprayauto-ignitionexperimentswerecarriedoutinaconstantvolumecombustionchamberforsomepurealkanes(n-paraffinswithdifferentchainlength,cyclohexane,n-butylcyclohexane,andisooctane)andblendsofn-decanewithStandardBlendedFuel(isooctane/n-heptane)andproductgasoline.Testresultsshowedthatthereactionactivityofn-paraffinswasrelativelyhigh.Meanwhile,theauto-ignitioncharacteristicsdifferedsignificantlywiththemolecularstructuresofalkanes.Addingdifferentvolumefractionsofn-decanetoStandardBlendedFuelandproductgasolinecouldimprovethefuelreactionactivityatvaryingdegree.Finally,functionalgroupseffectswereusedtosimulatetherelationshipbetweenthemoleculartopologyandtheauto-ignitionquality.
简介:Inthispaper,firstly,amathematicalmodelforaspecifickindofweltedbifurcationisestablished,theparametricequationfortheintersectingcurveisresultedin.Secondly,amethodforpartitioningfiniteelementmeshesoftheweltedbifurcationisputforward,itsmainideaisthatdevelopingthemainpipesurfaceandthebranchpipesurfacerespectively,dividingmeshesoneachdevelopingplaneandobtainingmeshespoints,thentransformingtheirplanecoordinatesintospacecoordinates.Finally,anappliedprogramforfiniteelementmeshesauto-generationissimplyintroduced,whichadoptObjectARXtechniqueanditsrunningresultcanbeshowninAutoCAD.ThemeshesgeneratedinAutoCADcanbeexportedconvenientlytomostoffiniteelementanalysissoftwares,andthefiniteelementcomputingresultcansatisfytheengineeringprecisionrequirement.
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简介:Sentimentanalysis,ahotresearchtopic,presentsnewchallengesforunderstandingusers'opinionsandjudg-mentsexpressedonline.Theyaimtoclassifythesubjectivetextsbyassigningthemapolaritylabel.Inthispaper,weintroduceanovelmachinelearningframeworkusingauto-encodersnetworktopredictthesentimentpolaritylabelatthewordlevelandthesentencelevel.Inspiredbythedimensionalityreductionandthefeatureextractioncapabilitiesoftheauto-encoders,weproposeanewmodelfordistributedwordvectorrepresentation"PMI-SA"usingasinputpointwise-mutual-information"PMI"wordvectors.Theresultedcontinuouswordvectorsarecombinedtorepresentasentence.Anunsupervisedsentenceembeddingmethod,calledContextualRecursiveAuto-Encoders"CoRAE",isalsodevelopedforlearningsentencerepresentation.Indeed,CoRAEfollowsthebasicideaoftherecursiveauto-encoderstodeeplycomposethevectorsofwordsconstitutingthesentence,butwithoutrelyingonanysyntacticparsetree.TheCoRAEmodelconsistsincombiningrecursivelyeachwordwithitscontextwords(neighbors'words:previousandnext)byconsideringthewordorder.Asupportvectormachineclassifierwithfine-tuningtechniqueisalsousedtoshowthatourdeepcompositionalrepresentationmodelCoRAEimprovessignificantlytheaccuracyofsentimentanalysistask.Experimentalresultsdemon-stratethatCoRAEremarkablyoutperformsseveralcompetitivebaselinemethodsontwodatabases,namely,SanderstwittercorpusandFacebookcommentscorpus.TheCoRAEmodelachievesanefficiencyof83.28%withtheFacebookdatasetand97.57%withtheSandersdataset.