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简介：Thispaperdealswithanimportantsubjectofrejectingtheabnormaldataintelligentlyinthedynamicdatasystem.Basedontheprincipleofnearestneighboroffuz-zymathematics,anapproachofmathematicallyabstractingthehumanthinkingandphys-icalpracticeknowledgeisdiscussed,anewmethodofautomaticrejectionofabnormalda-taisthenproposed.Theexperimentalresultsshowthatthemethodisavailabletothepractice.

简介：2013年11月日本家用空调、热水器出货情况；2013年10月美国供热和制冷设备出货情况；2013年8～11月日本商用空调出货情况；

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简介：2015年第二季度，加拿大市场商用空调出货量为1．7974万台，同比增长26％：家用空调出货量为15．9272万台，同比增长19％；家用壁挂炉出贷量为13．8446万台，同比增长7％；供暖机组出货量为1．6072万台，同比增长8％；无风管空调系统出贷量为5．0242万台，同比增长5％。

简介：数据存取延期成为了高端计算系统的突出的性能瓶颈。在系统设计减少数据存取延期的关键是减少数据货摊时间。存储器地区和并发是影响现代存储器系统的性能的二个必要因素。因为全面存储器系统性能上的存储器并发的影响很好没被理解，然而，存在在利用数据存取并发上在很少减少数据货摊时间学习焦点。在这研究，一双新奇数据货摊时间模型，为地区和并发的联合努力的L-C模型和为数据上的纯失误的效果的下午模型阻止时间，被介绍。模型提供数据存取延期的新理解并且为表演优化提供新方向。基于这些新模型，先进缓存优化的一张概括表格被介绍。当时，被数据并发贡献了，把38个条目仅仅，21个条目由数据地区作出贡献，它显示出数据并发的值。在这研究介绍的L-C和下午模型和他们的联系结果和机会为数据中央的建筑学和算法现代计算系统设计的未来重要、必要。

简介：Inthisagecharacterizedbyrapidgrowthinthevolumeofdata,datadeidentificationtechnologieshavebecomecrucialinfacilitatingtheanalysisofsensitiveinformation.Forinstance,healthcareinformationmustbeprocessedthroughdeidentificationproceduresbeforebeingpassedtodataanalysisagenciesinordertopreventanyexposureofpersonaldetailsthatwouldviolateprivacy.Assuch,privacyprotectionissuesassociatedwiththereleaseofdataanddatamininghavebecomeapopularfieldofstudyinthedomainofbigdata.Asastrictandverifiabledefinitionofprivacy,differentialprivacyhasattractednoteworthyattentionandwidespreadresearchinrecentyears.Inthisstudy,weanalyzetheadvantagesofdifferentialprivacyprotectionmechanismsincomparisontotraditionaldeidentificationdataprotectionmethods.Furthermore,weexamineandanalyzethebasictheoriesofdifferentialprivacyandrelevantstudiesregardingdatareleaseanddatamining.

简介：Thisarticlepresentsadatamanagementsolutionbasedonthedatadistributionservice(DDS)communicationmodel.ThebasicDDScommunicationmodelconsistsofaunidirectionaldataexchangewhereapplicationsthatpublishdata'push'therelevantdata,whichisupdatedtothelocalcachesofco-locatedsubscriberstothedata[1].DDShasnospecifiedcenternodetoforwarddatapacketsandmaintainthecommunicationdata.Thistypeofpublish-subscribe(P/S)modelpresentsintegrityandconsistencychallengesindatamanagement.Unlikepeer-to-peer(P2P)distributedstorage,DDSapplicationshaveahardreal-timeenvironmentandfewerdatafeatures,andthecoreproblemisensuringtheintegrityandconsistencyofdataindistributedsystemsunderthishardreal-timeenvironment.ThisarticlebeginswithabriefintroductionofthecommunicationmodelusedbyDDS,thenanalyzespersistentdatamanagementproblemscausedbysuchmodel,andprovidesanappropriatesolutiontotheseproblems.Thissolutionhasbeenimplementedinaprototypesystemofthereal-timeservicebus(RTSB)ofTsinghuaUniversity.

简介：Theapplicationofdataenvelopmentanalysis(DEA)asamultiplecriteriadecisionmaking(MCDM)techniquehasbeengainingmoreandmoreattentioninrecentresearch.InthepracticeofapplyingDEAapproach,theappearanceofuncertaintiesoninputandoutputdataofdecisionmakingunit(DMU)mightmakethenominalsolutioninfeasibleandleadtotheefficiencyscoresmeaninglessfrompracticalview.ThispaperanalyzestheimpactofdatauncertaintyontheevaluationresultsofDEA,andproposesseveralrobustDEAmodelsbasedontheadaptationofrecentlydevelopedrobustoptimizationapproaches,whichwouldbeimmuneagainstinputandoutputdatauncertainties.TherobustDEAmodelsdevelopedarebasedoninput-orientedandoutputorientedCCRmodel,respectively,whentheuncertaintiesappearinoutputdataandinputdataseparately.Furthermore,therobustDEAmodelscoulddealwithrandomsymmetricuncertaintyandunknown-but-boundeduncertainty,inbothofwhichthedistributionsoftherandomdataentriesarepermittedtobeunknown.TherobustDEAmodelsareimplementedinanumericalexampleandtheefficiencyscoresandrankingsofthesemodelsarecompared.TheresultsindicatethattherobustDEAapproachcouldbeamorereliablemethodforefficiencyevaluationandrankinginMCDMproblems.

简介：TheLHCexperimentsatCERNwillgeneratehugevolumesofdata-severalPBperyearatdataratesbetween100MB/sand1.5GB/s.Thestorageandanalysisofthesedatapresentamajorchallenge.IncollaborationwithothermembersoftheformerRD45project,thecentraldatabasesupportgroupatCERNhasbeenworkingonthisissueforseveralyears,leadingtoproductionuseofapotentialsolution,basedonthecombinationofanObjectDatabaseandMassStoragesystem,botheatCERNandoutside.

简介：Throughanalyzingtheprincipleofdatasharinginthedata-basesystem,thispaperdiscussestheprincipleandmethodforintegratingandsharingGISdatabydataengine,introducesawaytoachievethehighintegrationandsharingofGISdataonthebasisofVCTinVC++,andpro-videsthemethodforunitingVCTintoRDBMSinordertoimplementaspa-tialdatabasewithobject-orienteddatamodel.

简介：[1]BattistiDS.1988.Thedynamicsand

简介：Thefastturnoverofsoftwaretechnologies,inparticularinthedomainofinteractivity(coveringuserinterfaceandvisualisation)makesitdifficultforasmallgroupofpeopletoproducecompleteandpolishedsoftware-toolsbeforetheunderlyingtechnologiesmakethemobsolete.AttheHepVis'99workshop,aworkinggrouphasbeenformedtoimprovetherpoductionofsoftwaretoolsfordataanalysisinHENP.Besidepromotingadistributeddevelopmentorganisation,onegoalofthegroupistosystematicallydesignasetofabstractinterfacesbasedonusingmodernOOanalysisandOOdesigntechniques.Aninitialdomainanalysishascomeupwithseveralcategories(componets)foundintypicaldataanalysistools:historams,Ntuples,Functions,Vectors,Fitter,Plotter,AnalyzerandController,SpecialEmphasiswasputonreducingthecouplingsbetweenthecategoriestoaminimum,thusoptimisingre-useandmaintainabilityofanycomponentindividually.TheinterfaceshavebeendefinedinJavaandC++andimplementationsexistintheformoflibrariesandtoolsusingC++(Anaphe/Lizard,Openscientist)andJava(JavaAnalysisStudio),AspecialimplementationaimsataccessingtheJavaLiraries(throughtheirAbstractInterfaces)fromC++.ThispapergiveranoverviewofthearchitectureanddesignofthevariouscomponentsfordataanalysisasdiscussedinAIDA.

简介：Since1998,theALICEexperimentandtheCERN/ITdivisionhavejointlyexecutedseverallarge-scalehighthroughputdistributedcomputingexercises:theALICEdatachallenges.ThegoalsoftheseregularexercisesaretotesthardwareandsoftwarecomponentsofthedataacqusitionandcomputingsystemsinrealisticconditionsandtoexecuteanearlyintegrationoftheoverallALICEcomputinginfrastructure.ThispaperreportsonthethirdALICEDataChallenge(ADCIII)thathasbeenperformedatCERNfromJanuarytoMarch2001.ThedatausedduringtheADCⅢaresimulatedphysicsrawdataoftheALICETPC,producedwiththeALICEsimulationprogramAliRoot.ThedataacquisitionwasbasedontheALICEonlineframeworkcalledtheALICEDataAcquisitionTestEnvironment(DATE)system.Thedataaftereventbuilding,werethenformattedwiththeROOTI/OpackageandadatacataloguebasedonMySQlwasestablished.TheMassStorageSystemusedduringADCIIIisCASTOR.Differentsoftwaretoolshavebeenusedtomonitortheperformances,DATEhasdemonstratedperformancesofmorethan500MByte/s.Anaggregatedatathroughputof85MByte/swassutainedinCASTORoverseveraldays.Thetotalcollecteddataamountsto100TBytesin100.00files.

简介：Multisensordatafusion(MDF)isanemergingtechnologytofusedatafrommultiplesensorsinordertomakeamoreaccurateestimationoftheenvironmentthroughmeasurementanddetection.ApplicationsofMDFcrossawidespectruminmilitaryandcivilianareas.Withtherapidevolutionofcomputersandtheproliferationofmicro-mechanical/electricalsystemssensors,theutilizationofMDFisbeingpopularizedinresearchandapplications.ThispaperfocusesonapplicationofMDFforhighqualitydataanalysisandprocessinginmeasurementandinstrumentation.Apractical,generaldatafusionschemewasestablishedonthebasisoffeatureextractionandmergeofdatafrommultiplesensors.Thisschemeintegratesartificialneuralnetworksforhighperformancepatternrecognition.AnumberofsuccessfulapplicationsinareasofNDI(Non-DestructiveInspection)corrosiondetection,foodqualityandsafetycharacterization,andprecisionagriculturearedescribedanddiscussedinordertomotivatenewapplicationsintheseorotherareas.ThispapergivesanoverallpictureofusingtheMDFmethodtoincreasetheaccuracyofdataanalysisandprocessinginmeasurementandinstrumentationindifferentareasofapplications.

简介：ＡＭｅｔｈｏｄｏｆＩｍｐｒｏｖｉｎｇＳｅｉｓｍｉｃＤａｔａＲｅｓｏｌｕｔｉｏｎ：ＣｏｍｐｒｅｈｅｎｓｉｖｅＩｎｖｅｒｓｉｏｎｏｆＷｅｌｌｌｏｇｇｉｎｇａｎｄＳｅｉｓｍｉｃＤａｔａＺｈａｎｇＹｕｆｅｎ；ＨｏｎｇＦｅｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＡｐｐｌ...

简介：IthaslongbeenacknowledgedthatGISdatacanbeusedasauxiliaryinformationtoimproveremotesensingimageclassification.Inpreviousstudies,GISdatawereoftenusedintrainingareaselectionandpostprocessingofclassificationresultoractedasadditionalbands.Generally,itisfulfilledinastatisticalorinteractivemanner,soitisdifficulttousetheauxiliarydataautomaticallyandintelligently.　　Furthermore,iftheclassifierrequestscertainstatisticalcharacteristics,theadditionalbandmethodcannotbeusedbecausemostauxiliarydatadonotmeettherequirementsofstatisticalcharacteristics.Ontheotherhand,expertsystemtechniqueswereincorporatedinremotesensingimageclassificationtomakeuseofdomainknowledgeandlogicalreasoning.Butbuildinganimageclassificationexpertsystemwasverydifficultbecauseofthe“knowledgeacquisitionbottleneck”.　　Spatialdataminingandknowledgediscovery(SDMKD),istheextractionofimplicit,interestingspatialornon_spatialpatternsandgeneralcharacteristics.Weproposedatheoreticalandtechnicalframeworkofspatialdataminingandknowledgediscovery(Lietal.,1997).Andspatialdataminingissupposedtobeusedintwoaspects,oneisintelligentanalysisofGISdata,theotheristosupportknowledgedriveninterpretationandanalysisofremotesensingimages.SDMKDprovidesanewwayofknowledgeacquisitionforremotesensingimageclassification.Severalresearchershavedonesomeworkinthisfield.Eklundetal.(1998)extractedknowledgefromTMimagesandgeographicdatainsoilsalinityanalysisusinginductivelearningalgorithmC4.5.Huangetal.(1997)extractedknowledgefromGISdataandSPOTmultispectralimageinwetlandclassificationusingC4.5too.Inthesetwostudies,geographicdatawereconvertedfromvectortorasterformatinwhichthesamplingsizeisequaltoimagepixelsize.Theimplementationofdataminingtechniquesinspatialdatabase,especiallyinductivelearningmethod,andthecombinationo

简介：Inthispaperwepresentanoverviewoftheongoingstudiestobuildupaframeworkthatsupportstheanalysisafterthereprocessingphase.ThisframeworkaimstodevelopastandarddataquerylanguagefortheHEPcommunity,Therelatedstudieshavebeenconsideringtherelationaldatabasemodelaspossibleapproachopposedtotheobjectmodel.SeveraloptimizingandtunningtechniquesarebeingusedintechnologieslikeDB2[3],Oracle[5]andRoot[2],thataresimultaneouslybeingevaluated.TheexperienceobtainedcanbeseenasavaluabletestbedforthefutureLHCandsimultaneouslyasinterestinginputforthedevelopmentoftheGRID.

简介：分叉的河隧道和象长期的捱过和侵蚀的结果的山谷，山脉，山，和斜坡的共存形成唯一的黄土地形学。Changqing地球物理的公司，处于这些复杂条件工作，为高保真处理和地震数据的好解释建立了技术的一间套房。这篇文章介绍涉及处理的数据和解释的进程并且说明结果。关键词黄土高原-地震数据处理-静电干扰-箱优化-噪音变细-在教授水平的高级工程师数据解释第一作者江家玉从北京石油研究所在地球物理的探索毕业了，一所美国学院1970。她从事了关于地震数据的全面解释的研究。现在她在Dongfang地球物理的同伴的研究院的Changqing分割工作。26篇文章被出版。她授于我国家科学与技术的班奖金，和科学与技术的进步奖金由部和局进行四十次。