毕业论文坐标转换中英文翻译

word文档可自由复制编辑本科毕业设计（论文）中英文对照翻译院（系部）专业名称年级班级学生姓名指导老师XXX年X月XAbstractStudiesonqualityevaluationofcoordinatetransformationhavenotyettocomprehensivelyinvestigatethesimulationabilityandreliabilityofatransformation.Thispaperpresentsacomprehensivequalityevaluationsystem(QES)forcoordinatetransformationthatincludesthetestingofreliabilityandsimulationability.TheproposedQESwasusedtotestandevaluatetransformationsusingtypicalcommonpointdistributionsandtransformmodels.Boththetransformationmodelanddistributionofcommonpointsarefactorsintheeffectivenessofatransformation.TheperformancesoftypicalcommonpointdistributionsandtransformmodelsaredemonstratedusingtheproposedQES.Keywords：coordinatetransformation;QES;reliability;simulationreliability;commonpointdistribution;transformmodel =1\*ROMANI.INTRODUCTIONInformationaboutcommonpointsconsistsofsignals.However,noisecausedbyinadequaciesintheprecisionofsurveyingtechniques,byshortcomingsincomputationalmodels,andbyvariationsduetocrustalmovements,etc.alsobecomeincorporated.Thisnoisecanshowsystematicorrandomcharacteristics,orcanevenappearatsomepointsasgrosserrors.Duringcomputations,randomerrorscanbeexposedasresiduals,whilesystematicerrorscanbesimulatedbysuitabletransformationmodels.Incontrast,grosserrorsareabsorbedinparametersthatresultinremarkabledistortionofthetransformation.Forthisreason,anoptimaltransformationmusthavetheabilitytosimulatesignalsandsystematicerrors(simulationability)andalsotodetectanddefendagainstgrosserrors(reliability).Precisionisgenerallyconsideredtobeauniqueindicatorthatreflectsthequalityofatransformation(WellsandVanicek1975;Appelbaum1982;Featherstoneetal.1999).Chenetal.(2005)proposedanumberofsimulationindicatorsforevaluationoftheperformanceofatransformation.Youetal.(2006)usedleast-squarescollocationtoeliminatenoisefromcommonpoints,butfoundthattheresultingisotropicalcovariancewasoftennotcorrect.Hakanetal.(2006)investigatedtheeffectofcommonpointdistributiononreliabilityofadatatransformation.Theyestablishedthattheredundancynumbersindatatransformationweredeterminedbythedistributionofcommonpointsintheareathattheybounded.Guietal(2007)presentedaBayesianapproachthatallowedgrosserrordetectionwhenpriorinformationoftheunknownparameterswasavailable.However,theseexistingreportsonevaluationofthequalityofcoordinatetransformationdidnotcomprehensivelyinvestigateeitherthesimulationabilityorthereliabilityofthetransformationbeingstudied.Theobjectivesofthispaperweretherefore:(1)tointroduceacomprehensivequalityevaluationsystem(QES)forcoordinatetransformationthatwouldincludetestsofsimulationabilityandreliability;(2)toanalyzetheeffectsofcommonpointdistributionandthetransformationmodelonsimulationabilityandreliability;and(3)toinvestigateperformanceoftypicalcommonpointdistributionsandtransformationmodelsusingtheproposedQES.Section2providesanintroductiontotheQESthatisproposedforcoordinatetransformation.Transformationswithtypicalcommonpointdistributionsandtransformmodelsarethentestedandevaluatedinsection3.Lastly,section4presentsconclusions.II.THEPROPOSEDMETHODFig.1.FlowchartofproposedQES.Fig.1showstheflowchartfortheproposedQES.Inthispaper,boththedistributionofcommonpointsandthetransformationareconsideredtobethedeterminingfactors,whilereliabilityandsimulationabilityarethemainindicatorsusedforevaluation.Ifperformancesofcandidatedistributionsandmodelsarebothabletosatisfycertainchosencriteria,thenan“optimum”transformationappears.Otherwise,othercandidatesareintroducedfortestingperformancesoftheindicators.Thus,Fig.1isalsotheflowchartthatleadstoan“optimum”transformation.Whenreliabilityistakenintoconsideration,theinvestigationofsimulationabilityprovesbothfeasibleandvaluable.Thereliabilityindicatorsconsistofredundantobservationcomponents(ROC)andinternalandexternalreliabilities(LiandYuan2002),whilethesimulatedindicatorsconsistofprecision,extensibility,anduniqueness.A.ReliabilityIndicators1)RedundantObservationComponentsThegenerallinearizedGauss-Markovmodelisexpressedasfollows:(1)Here,listhevectorofobservations,Visthevectorofresiduals,Aisthelinearizeddesignmatrix,andistheapproximationofunknownparameters.Itsnormalequationisasfollows: (2)Here,.Then:(3)Eq.3describestherelationshipbetweenresidualsandtheinputerrors.Residualsdependonthematrix,whichisdecidedbythedesignmatrixAandtheweightmatrixP.Thisrepresentsthegeometricalconditionofanadjustment,termedthereliabilitymatrix,becauseitreflectstheeffectofinputerrorsonresiduals.Sincethereliabilitymatrixisindependentofobservations,theadjustmentcanbedesignedandtestedpriortofieldobservation.Thetraceofisequaltotheredundantobservationnumberr,soitsithdiagonalelementisconsideredtobetheithredundantobservationcomponent,asfollows:,.(4)Ingeneral,.2)InternalReliabilityTheinternalreliabilityreferstothemarginaldetecTablegrosserrorwithsignificancelevelandpowerfunction,asfollows:,(5)Whereisthenon-centralityparameterofnormaldistributioncausedbygrosserror.reflectstheabilitytodetectgrosserrorincertainobservations.Asmallerinnerreliabilitywillleadtothedetectionofmoregrosserrors.IftheprecisioncomponentisremovedfromEq.5,thenapurescaleofinnerreliabilityispresentedasthecontrollablevalue,asfollows:(6)Thiscontrollablevalueindicateshowmanytimeslargeragrosserrorinacertainobservationmustbe,comparedtoitsstandarddeviation,sothatcanitbedetectedatleastwithconfidencelevel0andthepoweroftests0.Thisvalueisindependentoftheobservationunit.3)ExternalReliabilityExternalreliabilityreflectstheeffectsofundetectedgrosserrorsonadjustment(includingallunknowncoefficients,etc.).Giventhatthereisjustonegrosserrorandthatalloftheobservationsareuncorrelated,theeffectvectorofundetectedgrosserrorsincertainobservationsonunknownscanbededucedfromEq.2.Itsmoduleisasfollows:(7)Therearemanytheoreticalmethods,butinpractice,thedatasnoopingmethodpresentedbyBaarda(1976)isoftensuccessivelyusedtodetectgrosserrorsandtofinddubiTableobservations.Itsgeneralizedmodelisasfollows:(8)and;whereisthestandardizedresidual.When~,itwillbecomparedwith,whichdecideswhetheritwillbedetectedasagrosserror.4)PrecisionPrecisionindicatesthedifferencebetweenthetransformedcoordinatesfromonereferencesystemandtheknowncoordinatesinanotherreferencesystem.Theresidualsbetweenthetransformedandtheknowncoordinatesaregenerallyconsideredtorepresentprecision.Mathematicalexpectationandstandarddeviationhavebeenwidelyusedinstatisticstoexpressprecisionofacalculation,shownasfollows:(9)(10)wherexirepresentstransformedcoordinates,Xirepresentsknowncoordinates,nisthenumberofcommonpoints;ismathematicalexpectationandstdisstandarddeviation.However,thisdoesnotprovidethedistributionofresiduals.Arandomselectionof75%ofallavailabledataisusedtogenerateatransformationmodel,whiletheother25%areusedtotestthemodel(WuChenetal.2005).Theresidualsfrombothdatasetsareusedtoquantifytheprecisionofthetransformation.Ifallcommonpointsavailableareusedtogeneratethemodel,withoutleavingdatafortestingthemodel,theresultwillonlyshowhowwellthemodelfitstheexistingdata.Theprecisionofthetransformationmaybemisleading,resultinginnoclearindicationofhowwellthetransformationwillperformwithindependentdata.5)ExtensibilityExtensibilityrequiresthatthetransformationmodelobtainedfromagivendistributionofcommonpointswillbeapplicablebeyondtheboundariesofthedistribution,withincertainprecisionlimits.Ifthetransformationprecisionwiththesurroundingpointsiscomparabletothatobtainedforthepointsusedtogeneratethemodel,thistransformationisextensible.Extensibilityisimportanttoatransformation.Ifnodataareavailableoutsidethedistributionforgeneratingcorrespondingtransformationparameters,anumberofcommonpointsintheinteriorofthedistributionneedtobeselectedtogeneratetheseparameters.Predictionorcheckingtransformationsbeyondtheboundariesofthedistributionisdoneinasimilarmanner.6)UniquenessUniquenessrequires:(1)thateachpointincoordinatesystem1transformstoasingleuniquecoordinateinsystem2;(2)thatdifferenttransformationsusedindifferentregionsagreeattheboundaryofadjoiningregions.B.SimulationIndicatorsWhenthereliabilityistakenintoconsiderationfordatatransformation,theissuebecomesamatterofdistortionsratherthanofgrosserrors.Theinvestigationofitssimulationabilitybecomesbothfeasibleandvaluable.III.EXPERIMENTSANDDISCUSSIONSA.DataandMethodsInthisstudy,atotalof30GCPsinthecityofAnyangChina,withcoordinatesinboththeWGS84andXi’an80coordinatesystem(asshowninFig.2a),areusedtoprovideseveraltypicalcommonpointdistributions.CoordinatesoftheGCPsinWGS84areobtainedbytertiaryGPScontrolsurveying.UTMsareusedtotransformtheseintoaplanecoordinatesystem.CoordinatesoftheGCPsinXi’an80areobtainedbytriangularsurveying.The15GCPsinthelowerrightofFig.2aareselectedasanewdistributionofcommonpointsinasmallerarea(asshowninFig.2b).SomeGCPsaresoclosetoeachotherthattheycannotbedistinguishedeasilyineitherofthesmall-scalemapsshowninFig.2aandFig.2b.Typicaltransformationmodelsusedinthesetypesofexperimentshaveincludedanalytictransformation,planesimilaritytransformation,andpolynomialtransformation.Inanalytictransformation,thecoordinatesintheplanesystemmustfirstbetransformedintoageodeticcoordinatesystem,andthenintoarectangularspacecoordinatesystem.Theparametersofa3DtransformationmodelbetweentworectangularspacecoordinatesystemsarethengeneratedbycommonpointstransformedfromXi’an80andWGS84.Inthecurrentpaper,weuseMolodenskitransformationwith3parametersandHelmerttransformationwith7parametersas3Dtransformationmodels.Giventhatthecoordinateinthesourcesystemis,andthetransformedcoordinateinthetargetsystemis,theMolodenskitransformationandHelmerttransformationareshownasEq.11andEq.12,respectively:(11)(12) Here,[dXdYdZ]Tisthetranslationvectorbetweentheoriginsofthetwosystems,Misrelativescalefactorbetweentwosystems,andRX,RY,RZaretherotationparametersfromthesourcesystemtothetargetsystem.Planesimilaritytransformationandquadraticpolynomialtransformationcanbeimplementedwhenbothsystemsareplanecoordinatesystems.Giventhatthecoordinatesinsourcesystemare[XSYSZS],andthetransformedcoordinatesinthetargetsystemare[XTYT]T,planesimilaritytransformationandpolynomialtransformationareshownasinEq.13andEq.14,respectively. (13) whereistherotationanglebetweentwosystems,isthecoordinateoftheoriginofthesourcesysteminthetargetsystem,anddSrepresentstheincrementofscalebetweenthetwosystems,asfollows: ,(14) Here,areparametersofpolynomialtransformation. InFig.3a,thedistributionsofROCsandinternalreliabilitiesaremaintainedevenly,withnosuddendisruptions.Althoughthedistributionofexternalreliabilitiesbecomessomewhatsteeper,theactualvaluesremainsmall.InFig.3b,thedistributionsofallreliabilityindicatorsbecomesteeper;buttheyallstillmaintainarelativelysmallvalue.InFig.3c,distributionsofallreliabilityindicatorsarethesteepest;inparticular,theexternalreliabilitiesatsomepointsaremuchgreaterthanareothers.Inotherwords,itbecomesmoredifficulttodetectandtoeliminategrosserrors,andmoreerrorsmaybeabsorbedwithintheparametersatthesepoints.Theeffectsofdifferenttransformationmodelsonreliabilityofatransformationclearlyindicatethatrigorousanalyticaltransformationprovidesbetterreliability.Fig.4followssimilarrules.However,distributionsofreliabilityindicatorsineachpanearenowallworsethanB.TestingReliabilityFigures3and4showtheeffectsofcommonpointdistributionandtransformationmodelsonthereliabilityofacoordinatetransform.Toconservethenumberofpages,onlyexperimentsonmoretypicalmodelssuchastheHelmerttransformation,planesimilaritytransformation,andquadraticpolynomialtransformation,areshownandcomparedbelow.IntheFigures,theROC,internalreliability,andexternalreliabilityarecalculatedandshownasbarsateachpoint,givensignificancelevel,powerfunction,andthenon-centralityparameter.Fig.4.ReliabilityindicatorsgeneratedbytypicaltransformationmodelswithcommonpointsshowninFig.2barethoseinthecorrespondingpanesinFig.3.ComparingFig.1aandFig.1b,thenumberofcommonpointsinFig.2bbecomesfewerandthedistributionofcommonpointsalsobecomesmoreuneven.Fig.3andFig.4showthattheredundancynumbersanddistributionofcommonpointsarekeyfactorsthatimpingeonreliabilityindicators.Adistributionofcommonpointsthatprovideshighredundancynumbersthereforeleadstoreliableestimationsforresidualsandparametersoftransformationmodels.Bothtransformationmodelsanddistributionsofcommonpointsaredeterminingfactorsforthereliabilityofatransformation.Forthisreason,investigationofbothfactorsshouldbecarriedoutinordertoensureareliabletransformation. C.TestingSimulationAbilityToinvestigatethesimulationabilityofthetransformationmodelsshownanddiscussedinsection3.1,twoexperimentsweredevelopedandimplemented.First,totestprecisionoftypicaltransformationmodels,arandomselectionof3/4oftheGCPsshowninFig.2awasusedtogenerateparametersoftransformationmodels.TheremainingGCPswereusedasdatapointsfortestingthemodel.TheseresultsareshowninTableI.Secondly,totesttheextensibilityoftypicaltransformationmodels,thecommonpointsshowninFig.2bwereusedtogenerateparametersoftransformationmodels,whileotherpointsofthetotal30GCPsshowninFig.2awereusedascheckpoints.TheseresultsareshowninTableII.Thepointsusedtogenerateparametersaredesignatedasfittedpointsinthispaper.TableI.comparestheprecisionoftypicaltransformationmodelsandthestatisticsofresidualsgeneratedbythesemodels,withthecommonpointsshowninFig.2a.Theresultingperformanceoftypicaltransformationmodelsontransformationprecisionismeaningfulforfurtherexperimentsandapplications.Residualsatfittedpointsgeneratedbyaquadraticpolynomialaresmallerthanthosegeneratedbyaplanesimilaritytransformation;however,residualsatcheckpointsgeneratedbyaquadraticpolynomialarelargerthanthosegeneratedbyaplanesimilaritytransformation.Thus,thetransformationmodelthatadequatelyfitsthepointsusedtogenerateparametersmaynotperformwellatotherpoints.Thisverifiestheneedtosetcheckpoints.TableII.showstestsofextensibilityoftypicaltransformmodelsusingthefittedpointsshowninFig.2bandthecheckpointsofthe30GCPsshowninFig.2a,minusthelowerleft15pointsshowninFig.2b.Inthispaper,theratiobetweenRMSEoffittedpointsandthatofthecheckpointsisusedtoquantifytheextensibilityoftypicaltransformmodels,andisdesignatedastheextensibilityratio.BasedontheextensibilityratioofthetypicaltransformmodelsshowninTableII,planesimilaritytransformationappearstohavethebestextensibility.TheHelmertandMolodenskitransformationsalsoperformwell,whiletheperformanceofthepolynomialmodelsisworse.Theextensibilityratioincreasesdramaticallywiththeexponentofpolynomialmodels.TheresultsshowninTableI.provethatalltransformationmodelssatisfythefirstrequirementofuniqueness,asgoodprecisioncannotbegeneratedbymodelswithoutone-to-oneprojection.Extensibilityofatransformationmodeldeterminesitsabilitytosatisfythesecondrequirementofuniqueness,astheextensibilityratiodetermineshowwelldifferenttransformationsagreeattheboundaryofadjoiningregions.AlthoughthenumberofcommonpointsusedtogenerateparametersinTableIislargerthanthatinTableII,theresidualsoffittedpointsshowninTableIIarebetterthanthoseshowninTableI.ThisisbecausethedensityofcommonpointsinFig.2bisgreaterthanthatshowninFig.2a.Therefore,thedistributionofcommonpointsalsodeterminesthesimulationabilityofaIV.CONCLUSIONSSimulationabilityandreliabilityarecrucialtoanytransformation.However,existingreportsonqualityevaluationofcoordinatetransformationhavenotyetcomprehensivelyinvestigatedthesimulationabilityandreliabilityofatransformationortheeffectsofcommonpointdistributionandtransformmodelsontheseabilities.ThispaperpresentsacomprehensiveQESforcoordinatetransformationthatincludesthetestingofcommonpointdistributions.Italsocomparestransformationmodelsbasedonreliabilityindicatorsandsimulationindicatorsanddiscussestheseindicatorsindetail.TheexperimentsanddiscussionsadequatelysupportthevalidityandfeasibilityoftheproposedQES.WiththisQES,transformationswithtypicalcommonpointdistributions,aswellastransformationmodels,havebeentestedandevaluated.Boththetransformationmodelandthedistributionofcommonpointsareimportantfactorsthatdeterminethereliabilityandsimulationabilityofagiventransformation.Thus,investigationofbothofthesefactorsshouldbecarriedout,inordertoensureareliableandprecisetransformation.TheperformancesoftypicalcommonpointdistributionsandtransformationmodelsusingtheproposedQESshowthatitisworthpursuinginfurtherexperimentsandapplications.摘要研究坐标转换的质量评价尚未全面了解一个转换的仿真能力和可靠性。本文提出一种坐标转换综合素质评价体系(QES),包括可靠性测试和仿真能力测试。拟议中的QES被用来测试和评估转换使用典型的公共点分布和变换模型。转换模型和点分布都是影响转换的有效性的因素。使用拟议的QES能展示典型的公共点分布和转换模型的优劣。关键字:坐标转换;QES;可靠性;仿真可靠性;公共点分布;转换模型一、介绍所有公共点点的信息组成信号。然而,测量技术的精度不足,计算模型的缺点,和地壳运动和变化等造成的噪声也变成一个总和。这噪音可以表示系统的随机特性,或甚至可以作为粗差出现在一些公共点上。在计算中,随机误差可以体现为残差,当系统误差能被合适的转换模型模拟。相反,粗差被参数吸收导致转换的显著的扭曲。出于这个原因,一个最佳的转换,必须有信号仿真能力和系统误差仿真能力以及检测和抵御粗差(可靠性)。精度普遍被认为是一个独特的指标,反映了坐标转换的质量(Wells和Vanicek1975;Appelbaum1982;费瑟斯通.1999年)。为了评价的坐标转换的性能，陈etal(2005)提出了一系列模拟指标。Youetal.(2006)在点处使用最小二乘法消除噪声,但是发现产生的各向同性的协方差往往是不正确的。Hakanetal.(2006)调查公共点分布对数据转换可靠性的影响。在他们限定的区域内，他们在数据转换中建立了冗余公共点个数。Guietal(2007)提出了一种贝叶斯方法,允许粗差的侦测当未知的参数主要信息没有被利用之前。然而,这些现有的坐标变换的质量评估报告并没有全面调查证明仿真能力可行和有价值。因此本文的目的是:(1)是引入全面质量评价体系(QES)进行坐标变换,包括仿真能力和可靠性的测试;(2)是分析公共点分布的影响和转换模型的仿真能力和可靠性,(3)是研究对典型的公共点分布和坐标转换模型使用拟议的QES转换模型性能的表现。第2节介绍了坐标变换的拟议的QES。在第三节对典型的公共点分布和坐标转换模型进行了测试和评估。最后,在第四节给出结论。二、拟议的方法图1拟议的QES的流程图 图1展示了拟议的QES的流程图。在这篇论文中，公共点的分布和坐标转换模型被认为是决定性的因素，而可靠性和仿真能力是评估的主要指标。如果公共点的分布和转换模型的表现都能够满足被选定的标准,那么这就是一个“最佳”转换。否则,其他候选的方法被引入了用于测试其表现。因此,图1的流程图也能产生一个“最佳”转换。当考虑到可靠性的问题，对仿真能力的研究变得可行又有价值。可靠性指标由冗余观测值组成(ROC)和内部和外部可靠性组成(Li和Yuan2002),而仿真指标包括精度、可扩展性和独特性。A．可靠性指标1)冗余观测值一般的线性化高斯-马尔可夫模型表示如下:(1)上式中,是观测向量,V是残差向量,是线性化设计矩阵,是未知参数的近似。它们的正式的方程如下:(2)在式中,.所以:(3)等式3描述了残差和输入错误之间的关系。残差值取决于矩阵,这是由设计矩阵A和权重矩阵P决定的。这代表了几何条件调整,被称为可靠性矩阵,因为它反映了输入错误对残差的影响。由于可靠性矩阵是独立于观测值的，对它的调整可以被设计和测试在实地观测之前。而矩阵的迹等于多余的跟踪观测数r,所以它的第i个对角元素被认为是第i个多余观测值,如下: ，(4)一般来说,.2)内部可靠性内部可靠性指的是边缘检测粗差和显著性水平和幂函数,如下:,(5)当因粗差导致不符合正态分布参数时。反映了检测某些观测误差的粗差的能力。更小的内部可靠性将导致更多的粗差被检测。如果精确的成分从等式(5)中移动,然后内在可靠性一个确定的范围被体现作为可控的数值,如下:(6)这个控制值表示在一次观测中对于一个观测值来说一个粗差超过其标准偏差的倍数。所以它以最小置信区间和置信水平被检测出来。这两个数是独立于观测过程而存在的。3)外部可靠性外部可靠性反映了在调整中未检测出粗差的效果(包括所有的无效的，等等)。所有的不相关的观测值下只有一个粗差，其影响效果向量在确定的观测值下能在公式(2)下被推导出来。其公式如下：(7)有许多理论方法,但在实践中,Baarda(1976)提出的数据监听方法总值通常是先后用于检测粗差和发现可疑的观测值。其广义模型如下: (8)式中，；上式中是标准的偏差值。当~时，它会被与相比,以决定它是否会被检测作为一个粗差。B．仿真指标当考虑数据转换的可靠性时,这个问题就不仅仅只是涉及到粗差了。其仿真能力的研究就变得可行和有价值的。4)精度精度表现了从一个参考系统转换被转换的坐标和另一参考系统中已知坐标之间的差异。已知的坐标和转换坐标之间的残差通常被认为是精度的代表。数学期望和方差已经广泛应用于统计在计算中表达精度,表示如下:(9)(10)其中代表转换坐标,代表已知坐标,是公共点的数量,是数学期望和是方差。然而,这并不提供残差的分布信息。随机选择75%的所有可用的数据用于生成转换模型,而另25%是被用于测试模型(陈吴etal.2005)。残差的两个数据集被用来量化转换的精度。如果所有公共点可用来生成模型,无需留下数据用来测试模型,结果将只显示模型符合现有的数据。转换的精度可能会被结果误导,导致没有明确的体现转换用独立数据将会有怎样的表现。5)可扩展性可扩展性要求转换模型在一定的精度范围内获得从给定的公共点分布将能适用的边界之外的分布。如果转换精度与周围的点与用于生成模型获得的点一致,则该转换是可扩展的。可扩展性对于坐标转换来说是很重要的。如果没有在分布之外的可用的数据用于生成相应的转换参数,则需要选择一定数量内部公共点分布用于生成这些参数。预测或检查边界之外分布的转换坐标是以类似的方式完成的。6)独特性独特性要求:(1)在坐标系统1中的每个点转换为坐标系统2中单个的独特的坐标;(2)在不同地区使用不同的转换而在相邻的边界地区则具有一致性。三、实验和讨论A.数据和方法在这项研究中,在中国安阳城共计30个公共点的空间直角坐标,每个点同时拥有在WGS84和80西安坐标系下坐标(如图2所示),用于提供几种典型公共点分布。WGS84下空间直角坐标的都是通过三级的GPS控制测量获得。并使用UTMs投影将这些坐标转化为平面直角坐标。西安80系下空间直角坐标均由三角控制测量得到。15个GCPs在右下角的图2a中被选择作为一个在较小