简介:自愿的云是云计算的一个新范例。它与一些好预备的云一起提供一种其他的选择。然而,在不明确的时间跨越那参加者份额他们在自愿的云的计算资源,有一些,质问问题,即,变化,在能力下面并且低利益。在这份报纸,体系结构首先基于Bittorrent协议被建议。在这体系结构,资源能从保留的例子市场被保留或请求并且能在一个短圆与更低的价格被存取。实际上,这些资源能填满不适当的资源在自愿的云分享并且减轻变化和在能力下面问题。然后,每个节点的差错率被用来评估它的分享的时间的无常。由利用一个线性预言模型,它被被用于评估系统的计算能力的一个分发函数启用。而且,费用优化问题被调查,一个计算方法被介绍在自愿的云解决低利益的问题。最后,系统表演被模拟的二个集合验证。并且试验性的结果为资源保留优化显示出我们的计算方法的有效性。
简介:Theradiancesscatteredoremittedbycloudsdemonstratediversefeaturesatdifferentwavelengthsduetodifferentcloudphysicalstructures.Thispaperpresentsamethod(thesmallest-radiance-distancemethod,SRaDM)ofrevealingthephysicalstructuresofclouds.Themethodisbasedonmulti-spectralradiancesmeasuredbytheModerateResolutionImagingSpectroradiometer(MODIS)onboardAqua.TheprincipleandmethodologyofSRaDMisdeducedandprovidedinthispaper.CorrelationanalysisbasedondatafromMODISandCloudProfilingRadar(onboardCloudSat),collectedfromJanuary2007toDecember2010overanoceanarea(15°N–45°N,145°E–165°E),ledtoselectionofradiancesat13wavebandsofMODISthatdemonstratedhighsensitivitytocloudphysicalstructures;radiancesattheselectedwavebandsweresubjectedtoSRaDM.TheStandardizedEuclideandistanceisintroducedtoquantifythedegreeofchangesinmulti-spectralradiances(termedDrd)andinphysicalstructures(termedDst)betweencloudprofiles.StatisticsbasedonnumerouscloudprofilesshowthatDrddecreasesmonotonicallywithadecreaseinDst,whichimpliesthatsmallDrdalwaysaccompaniessmallDst.AccordingtothelawofDrdandDst,thenewmethod,SRaDM,forrevealingphysicalstructuresofcloudsfromthecollocationofcloudprofilesofsimilarmulti-spectralradiances,ispresented.Then,twosuccessfulexperimentsarepresentedinwhichcloudphysicalstructuresarecapturedusingmulti-spectralradiances.SRaDMprovidesawaytoobtainknowledgeofthephysicalstructuresofcloudsoverrelativelylargerareas,andisanewapproachtoobtaining3Dcloudfields.
简介:Themacroandmicrocloudphysicsstructuresandtheirevolutionwithtimearethecoreofdescribingcloudfieldsinessence.Theyarenecessaryatmosphericenvironmentnotonlyinaviationandspaceflightactivitiesbutalsoforatmos-phericradiationtransferandacidrainformationresearch.Unfortunatelyitisdifficulttoobtainanentireenvironmentalcloudfieldbyusingobservationmethodsdirectly.Thus,byuseofcomputationphysicsmethodtobuildacloud-systemmodelmaybeanindispensablewayforthistopic.Thispaperpresentedacloud-systemmodelforthisgoal,andsimu-latedarealcase.Theresultsofcomputationshowedthatthemacrostructureofthecloudfieldwasbetterconsistentwithrealobservation,andthemicrostructurewasfairlyreasonable.Theoutputofmodelcouldprovidealltheinformationaboutthecloudfield:(1)size-distributionspectrumofhydrometeorparticles(point),(2)verticalprofile(line),(3)hori-zontalorverticalsectionofmacroandmicroparameters(surface),and(4)cloudcover,patternofcloudandconfigura-tionofcloud,etc.(body).
简介:A2-Dslab-symmetricmodelofmixedconvective-stratiformcloudisdevelopedbysuperimposingconvectivecloud-sizefieldontheconvergencefield,inordertosimulateandstudythemixedcloudsconsistingofstratiformcloudandconvectivecloud.Adeepconvective,anelasticandconservativesystemofequationswithbasicvariables(V,θ,π’)issolvedbyanewmethodtocalculatedynamicfield.Thewatersubstanceinthecloudisdividedinto6categoriesandthemicrophysicalprocessesaredescribedinspectrumwithtwovariableparametersandmorereasonableparticlenumber/sizedistributions.Tocomparewithmeasuredradarechointensityandstructure,themodelmaycalculateechointensityofthemodelcloudobservedbyradar.
简介:在极的同温层的云(PSC)的液体和稳固的粒子被知道了在迟了的冬季和早春在南极和北极区域上在同温层的臭氧的化学损失起一个关键作用。同温层的喷雾器和云粒子提供快异构的化学反应把不活跃的卤素水库种类变换成消灭激进分子的潜在的臭氧的地点。氮的酸包含的PSCparticles的沉积不可逆转地把HNO_3气体(denitrification)从更低的平流层移开,它减缓氯的回来到它的不活跃的形式,导致更多的严重同温层的臭氧破坏。尽管这些云在在situ领域观察,实验室实验和当模特儿的研究使用的过去的十年期间广泛地被调查了,在冷同温层的条件下面的详细微视物理学过程仍然是不明确的。这篇论文在我们PSC的理解考察最近的进展。
简介:Inthisstudy,cloudbaseheight(CBH)andcloudtopheight(CTH)observedbytheKa-band(33.44GHz)cloudradarattheBoseongNationalCenterforIntensiveObservationofSevereWeatherduringfall2013(September-November)wereverifiedandcorrected.Forcomparativeverification,CBHandCTHwereobtainedusingaceilometer(CL51)andtheCommunication,OceanandMeteorologicalSatellite(COMS).Duringrainfall,theCBHandCTHobservedbythecloudradarwerelowerthanobservedbytheceilometerandCOMSbecauseofsignalattenuationduetoraindrops,andthisdifferenceincreasedwithrainfallintensity.Duringdryperiods,however,theCBHandCTHobservedbythecloudradar,ceilometer,andCOMSweresimilar.Thinandlow-densitycloudswereobservedmoreeffectivelybythecloudradarcomparedwiththeceilometerandCOMS.Incasesofrainfallormissingcloudradardata,theceilometerandCOMSdatawereproveneffectiveincorrectingorcompensatingthecloudradardata.Thesecorrectedclouddatawereusedtoclassifycloudtypes,whichrevealedthatlowcloudsoccurredmostfrequently.
简介:基于国家中心因为环境预言(NCEP)和气候预言中心(CPC)合并了降水(CMAP)数据和CloudSat产品的分析,云性质的季节的变化,垂直出现频率,并且冰水内容阴郁东南的中国在这研究被调查。在CloudSat数据,在高或低的云模式的重要引申在东南的中国上从冬季被观察到夏天。东方亚洲夏天季风(EASM)发行量和它潮湿的运输导致有条件的不稳定性,这被发现,它在夏天有益于本地向上的运动,并且从而导致高云的增加的数量。深对流的云中心被发现与一致很好与向北方EASM前进,当触毛在传送对流中心后面稍微落后并且与流出和EASM的南方的风分叉与一致很好时。放射的加热率的分析表明丰富的夏天潮湿和更高的云在使动摇是有效的空气。而且,云热中间对流层并且云放射的加热被通过向上的运动的断热的冷却平衡,它由Sverdrup平衡引起南方的风。云强迫热的循环被观察与EASM发行量与一致很好,在EASM循环上用作积极效果。
简介:IfbeingaskedwhichisthelargestcityofYunnan,nineoutoftenpeoplewouldgivethewronganswer-justassomepeoplewouldreplythatTorontoisthecapitalofCanada,SydneyisthecapitalofAustraliaandGuilinisthecapitalofGaungxi.Infact,thelargestcityofYunnanisPu’er,notKunming.ManypeoplearefamiliarwithPu’erbecauseofPu’erTea.Theybelievetheplace'Pu’er'isnamedafterthetea.Butthe
简介:UsingaDMT(DropletMeasurementTechnologies)continuousflowstreamwisethermalgradientcloudcondensationnuclei(CCN)countermountedonaCheyenneⅢAaircraft,about20flightsforaircraftmea-surementsofCCNoverNorthChinawereconductedintheautumnof2005andspringof2006.Accordingtothedesignforaircraftobservation,themethodofspiralascentordescentinthetropospherewasusedfortheverticalmeasurementofCCN,andsomecertainlevelswerechosenforhorizontalmeasurement.TheverticaldistributionsofCCNconcentrationsshowthatmostCCNparticlesareconcentratedinthelowleveloftroposphereandCCNconcentrationdecreasedwithheightincreasing.ItsuggeststhatthemainsourceofCCNisfromthesurface.Thisresultisconsistentwithformerstudiesduring1983-1985inChinawithastaticthermalgradientCCNcounter.ThecomparisonofverticalobservationsbetweenpollutedruralareanearShijiazhuangandnon-pollutedruralareanearZhangjiakoushowsthatthereisaboutfivetimesdifferenceinCCNconcentration.Butovertwopollutedcities,ShijiazhuangandHandan,thereisnonotabledifferenceinCCNconcentration.ThehorizontalflightmeasurementsforpenetratingthecumuluscloudsexperimentshowtheapparentdecreaseofCCNinclouds.ItconfirmsthatcloudhasadefiniteconsumptiveeffectonCCNparticlesbecausesomeCCNparticlescanformclouddroplets.ThesurfacemeasurementsofCCNinShijiazhuangCityweremadeduringJune-August2005.Thesta-tisticalCCNdatashowthegreatdifferenceinconcentrationatthesamesupersaturation(S)inShijiazhuangsummertime.TheminimumCCNconcentrationswere584,808,and2431cm~(-3),andthemaximumconcen-trationswere9495,16332,and21812cm~(-3)atS=0.1%,0.3%,and0.5%,respectively.CCNhasadiurnalvariationcycle.From0600BT,theconcentrationbegantoincreaseandreachedthemaximumataboutnoon.Thenitgenerallydecreasedthroughouttheafternoon.Thereasonmaybeisrelatedtotheonsetofemissionsfromvehiculartraffi
简介:Withthefastgrowingofcloudcomputinginfrastructure,learningfromcloudserviceshasbecomemoreandmoreconvenientforpeopleworldwide.Inordertointegratethecloudcomputingtechnologyanddifferente-learningplatformsincludingvariantmobileapps,Windowsandweb-basedapplications,wedevelopourChineselearningsystem"analyticChinesehelper"withaservice-orientedarchitecture(SOA).Basedonthenewarchitecturewedesignedanddevelopedacloudserviceforthee-learningofChineselanguageontheInternetasaconvenientresourceforforeignstudents,especiallyinthereadingofChinesetexts.TherearetwoChinesephoneticsystems:PinyinandZhuyin.PinyinistheofficialRomanizationofChinesecharacters,andZhuyinincorporatesadditionalBopomofosymbolswhichtranscribeprecisesoundsofChinesecharacters.TheproposedanalyticChinesehelperprovidesreal-timeannotationswithPinyinorZhuyinsymbols,andtherebytheannotatedarticlescanbeusedase-learningobjectsinlearningChinese.
简介:Gao,ShoutingandLi,Xiaofan,2008,206pp.,US$149,hardbound,Springer,ISBN978-1-4020-8275-7Recentdecadeshavewitnessedtherapiddevelopmentofcloud-systemresolvingmodels(CRM),whicharenowcapableofsimulatingcloudsystemsandaccompanyinginteractionsonscalesuptoglobal,albeitinthelatterapplicationsmall-
简介:Smartappliancesandrenewableenergyresourcesarebecominganintegralpartofsmarthomes.Nowadays,homeappliancesarecommunicatingwitheachotherwithhomegateways,usingexistingshort-rangehomeareanetworkcommunicationprotocolssuchasZigBee,Bluetooth,RFID,andWiFi.AGatewayallowshomeownersandutilitiestocommunicateremotelywiththeappliancesvialong-rangecommunicationnetworkssuchasGPRS,WiMax,LTE,andpowerlinercarrier.ThispaperutilizestheInternetofThings(IoT)conceptstomonitorandcontrolhomeappliances.Moreover,thispaperproposesaframeworkthatenablestheintegrationandthecoordinationofHuman-to-Appliance,Utility-toAppliance,andAppliance-to-Appliance.UtilizingtheconceptsofInternetofThingsleadstoonestandardcommunicationprotocols,TCP/IPV6,whichovercomesthemanydiversehomeareanetworksandneighborhoodareanetworksprotocols.ThisworkproposesacloudbasedframeworkthatenablestheIoTsintegrationandsupportsthecoordinationbetweendevices,aswellaswithdevice-humaninteraction.Aprototypeisdesigned,implemented,andtestedtovalidatetheproposedsolution.1IndexTermsInternetofthings(IoT),Internetofthings(IoT)cloudframework,smarthomes,smartappliances.