海洋数据集的质量检查验收抽样方法-翻译

./AcceptancesamplingplanofqualityinspectionforoceandatasetComparedwiththedatasetofindustrialproducts,oceandatasetshaveseveraldistinctcharacteristics,suchaslargequantitiesandbeingmulti-source,multi-dimensionandmulti-type.Basedontheacceptancequalitylevel<AQL>andlimitqualitylevel<LQL>,wedesignedanacceptancesamplingplanofqualityinspectionforoceandatasets<ASP-OD>,usedthisplantoinspectoceandatasetquality,andevaluateditsadvantage.ASP-ODhasaconsistentandstablediscriminatorypowerindependentoflotsize’whichsolvestheproblemof‘strictnessforlargelotsize,tolerationforsmalllotsize’inthepercentsamplingplan.ASP-ODestablishesarelationshipbetweenlotsizeandsamplingsize,andprovidesaplanforagivenlotsize.ThisplanovercomesthedeficiencyofISO2859-basedsamplingplans,differentlotsizecorrespondingtothesamesamplingplan,inthequalityinspectionofoceandatasets.Collectively,thisstudysuggeststhatASP-ODisasuitablesamplingplanfortheinspectionofoceandatasetquality.Keywords:oceandataset;qualityinspection;AQL;LQL;acceptancesamplingplan1.IntroductionWiththerapiddevelopmentofoceanmonitoringtechnology,hugeamountsofoceandatahavebeencollectedfromvarioussources,suchasremotesensingimages,buoys,cruisedataandunderwaterobservationdata.Thus,oceandatasetshavegraduallybecomeaclassicexampleofmulti-modalbigdata.However,thebiggestobstacleforpreparinganoceanatlasishowtocontrolthequalityofdata.Thequalitycontrolofoceandatasetsisanimportantpartofanyoceananalysis/forecastingsystem.Usingoracceptingerroneousdatacouldleadtoaninvalidconclusionoranincorrectanalysis.Bycontrast,rejectingextremebutvaliddatasometimescouldcausethemissingofkeyeventsandanomalousfeatures.Todate,agrowingnumberofscientistshavebeguntofocusonthequalityinspectionofoceandata.Anautomatedqualitycontrolsystemwasproposedtoinspectoceanictemperatureandtemperature-salinityprofiles.TheSurfaceOceanCO2Atlas<SOCAT>projectwasperformedtoinvestigatetheglobaldatasetofmarinesurfaceCO2.Duringthisproject,alldataweredesignedtobeputinauniformformatfollowingastrictprotocol.Qualitycontrolwasconductedaccordingtoclearlydefinedcriteria.Inaddition,thequalityandconsistencyofNASAoceancolourdata,includingspectralwater-leavingreflectance,chlorophyll-αconcentration,anddiffuseattenuation,wereexaminedusingcommonalgorithmsandimprovedinstrumentcalibrationknowledge.Thesestudieshaveputforwardseveralqualityinspectionplansforoceandata,especiallyforoneorafewelements.Oceandatasetsareusuallycomposedofmulti-element,multi-scaleandmulti-temporalgeo-informationelements.Moreover,thereisapotentialinterplaybetweendifferentelementsinanoceandataset.Thus,itisrequiredtoproposeanovelacceptancesamplingplantoinspectthequalityofoceandataasacompleteandindivisibledataset.Thegoalofqualityinspectionistojudgewhetherthedatareachtherequiredqualitythroughasamplingplan.Currently,theoptimisationofacceptancesamplingplanshasbeenconductedtosatisfythebalancebetweeninspectionriskandinspectioncostforthequalityinspectionofindustrialproducts.Someacceptancesamplingplanshavebeendesignedbasedoninspectionrisk,whichaimedtominimiseeithertheproducer’sriskortheconsumer’srisk.Someotheracceptancesamplingplansweredesignedbasedontheinspectioncost,whichaimedtoreducethesamplingnumber.Theseexistingplansaremainlyusedtoinspectthequalityofindustrialproducts.Generally,industrialproductsareproducedinacontrolledandconsistentmanner,andusuallyhavecertainitemsanduniformunits.Comparedwithindustrialproducts,oceandatahavesomedistinctcharacteristics,suchasbeingmulti-source,multi-dimensionalmulti-type,multi-time-state,withdifferentaccuracyandnonlinearity.Thus,theseexistingacceptancesamplingplansarenotsuitableforthequalityinspectionofoceandatasets.Inthispaper,wedesignedanacceptancesamplingplanofqualityinspectionforanoceandataset<ASP-OD>.Insection2,theconceptualframework,derivationprocessandtheformulasofASP-ODareshown.Insection3,weapplytheASP-ODtoinspectthequalityofoceandata,andcompareitsadvantagesoverexistingacceptancesamplingplans.Insection4,wesummarisethisstudy,andproposethatASP-ODisasuitableacceptancesamplingplanforthequalityinspectionofoceandatasets.2.DesignofASP-ODThetheoryofASP-ODTheacceptancesamplingplanofqualityinspectionforoceandatasetswasdesignedasS<N,n,c>.Here,Nisthelotsizeandcomprisesallinspectedoceandatafromwhichthesampleistobetaken;nisthesamplesizeandconsistsofanumberofsamplingunitsselectedfromthelotsize,whichisacompromisebetweentheaccuracyofproductinspectionandthecostoftheinspection;c,theacceptancenumber,isusedtojudgewhethertheinspectedoceandatameettherequirementoftheoceandataconsumer.Theprocessofqualityinspectionisshownasbelow:<1>n-sampleddataareextractedfromthelotsizeN;<2>thequalityofextracteddataisinspectedonebyone;<3>ifthenumberofnon-conformingdata<d>islargerthantheacceptancenumber<c>,thequalityofinspectedoceandataisconsideredtobenon-conforming.Otherwise,thequalityofinspecteddataisconsideredtobeconforming.BasedontheacceptancesamplingplanS<N,n,c>,thepercentnon-conforming<P>iscalculatedby<1>whereDisthenumberofnon-conformingoceandatainthetotaloceandataset.Generally,itisdifficulttoobtainthevaluesofDandPunlessthetotaldataare100percentinspected.Sampledoceandataareusedtoestimatetheparametersforlotsize.Thus,Pisusuallyestimatedusingthepercentnon-conformingestimator<p>;piscalculatedby<2>wheredisthenumberofnon-conformingoceandatainthesampleddataset.Basedontheabove-mentionedparameters,theacceptancequalityprobability<L<p>>oftheacceptancesamplingplanS<N,n,c>canbecalculatedby<3><0≦d≦n,d≦D,n-d≦N-NpOperatingcharacteristiccurves<OC-curve>arepowerfultoolsinthefieldofqualitycontrol,astheydisplaythediscriminatorypowerofanacceptancesamplingplan.Here,weconsideredthequalitylevelasthehorizontalaxisandthecorrespondingacceptanceprobabilityastheverticalaxis.TherelationshipbetweenL<p>andtheproportionpofnon-conformingitemswasrepresentedastheOC-curveofsamplinginspectioninarectangularcoordinatesystem.Generally,consideringtheinterestsofboththeproducersandconsumers,acceptancequalitylevel<AQL>andlimitingqualitylevel<LQL>wereadoptedtodesigntheacceptancesamplingplan.LQLisamaximumqualitylevelofdefectivestoleratedintheinspectiondata.WhenthequalitylevelisworsethanLQL,theconsumerstendtorejecttheinspecteddata.AQLrepresentsameanqualitylevelofdefectivesamplestoleratedintheinspection.IfthequalityleveloftheinspecteddataisbetterthanAQL,theproducerstendtoaccepttheinspecteddata.Tomeettherequirementofbothproducersandconsumers,AQLandLQLweretakenintoaccountintheASP-ODdesign,whichwasshownastwopointsintheOC-curve<Figure1>.ThefirstpointisdenotedasFigure1.OC-curveoftheacceptancesamplingplan<p0,1-α>p0,i.e.AQL,istheproportionofnon-conformingitemsthatcanbetoleratedtojudgethattheentirelotcanbeaccepted.α,theproducer’srisk,istheprobabilityofrejectionoftheinspectedloteventhoughthequalitylevelofthelotisequaltoorbetterthanAQL.Thesecondpointisdenotedas<p1,β>.p1,i.e.LQL,istheproportionofnonconformingitemsthatcanbetoleratedtojudgethattheentirelotcanberejected.β,theconsumer’srisk,istheprobabilityofacceptanceoftheinspectedloteventhoughthequalitylevelofthelotisequaltoorworsethanLQL.UndertheconditionofthetwopointsontheOC-curve,therelationshipbetweenthelotsize,thesamplesizeandtheacceptancenumberiscalculated.Theproblemcouldbeformulatedasanonlinearprogrammingproblem.TheASP-ODmodelFromtheperspectiveoftheproducer,theacceptancesamplingplanshouldsatisfythefollowingcondition:<4>D1,apositiveinteger,isthenumberofnon-conformingdataelementsintheinspectedoceandataset.Whentheproportionofnon-conformingdataisequaltoAQL,thevalueofD1iscalculatedbyD1=round<N·p1><5>Fromtheperspectiveoftheconsumer,theacceptancesamplingplanshouldsatisfythefollowingcondition<6>D2,apositiveinteger,isthenumberofnonconformingdataelementsintheinspectedoceandataset.Whentheproportionofnonconformingdataisworsethanthelimitingqualitylevel<LQL>,thevalueofD2iscalculatedbyD2=round<N·p2><7>Thetotalresidualerror,ε,meansthesumofresidualerrorsoftheacceptanceprobabilityatbothAQLandLQL.Theroleofεisusedforthecalculationofthevalueofnandcintheacceptancesamplingplan.Here,wechosetheminimalεtodeterminetheoptimalnandcfortheacceptancesamplingplanatAQLandLQL.Theoptimalacceptancesamplingplanisformulatedasthefollowingnonlinearoptimisationproblems.t.<8><9>ε1istheresidualerroroftheacceptanceprobabilitybasedontheproducer’srisk.ε2istheresidualerroroftheacceptanceprobabilitybasedontheconsumer’srisk.Thenonlinearoptimisationproblemissolvedbasedontheiterativealgorithm.TheiterativealgorithmisimplementedinMatlabsoftware.CasestudyInthissection,weemployedASP-OD,thepercentsamplingplan<PSP>andtheISO2859-basedsamplingplan<ISO2859>toinspectthequalityofoceandatasets,anddiscussedwhethertheproposedASP-ODhasasignificantadvantageinqualityinspectionforoceandatasets.StudyareaanddatasetThestudyareaislocatedinacultivationareainSouthernChina,andcontains5093monitoringsites<Figure2>.Thedatasetsconsistofthreedifferentcharacteristics,attributecharacter,spatialcharacterandtemporalcharacter<Table1>.Here,depositsedimentdatawerecollectedusingresearchvesselswithanuncertaincollectioncycle.Hydrometeor-ologicaldatawerefromremotesensingonceadayortwiceaday.Waterqualitydata,megalobenthos,zooplanktonandphytoplanktondatawerecollectedusingbuoysper10minutes.Figure2.StudiedoceanareawiththemonitoringsitesTheoceandatasetcontainsthelocation<X/Ycoordinates>andattributeinformationthatisrepresentativeofthecorrespondinglocation.Hereweusedthexi/yivaluetorepresentthelatitudeandlongitudeofthemonitorsites<Table2>.However,theseparameterswerecollectedusingdifferentmonitoringtoolsandmethods.Itisdifficulttoguaranteetheaccuracyofdataacquisition,thecompletenessofthedatasetandtheconsistencyofthedata.Undoubtedly,alotofabnormaldatasetshavearisen.Forexample,thesalinityoflocation<x15,y15>is70.751,whichissignificantlyhigherthanthatoftheneighbouringsite.Thereactivesilicateoflocation<x12,y12>isnull.Thetotalphosphorusofthelocations<x4,y4>,<x5,y5>,<x6,y6>,and<x7,y7>issignificantlydifferentfromthevalueofotherlocations.Thus,itisrequiredtoconductaqualityinspectionoftheoceandataset.AcceptancesamplingplansWedesignedASP-ODtoinspectthequalityoftheoceandataset.Here,fivelotsofoceandatasetswerecollectedfordifferentmonitoringsites.Meanwhile,thequalityofthesedatasetswasevaluatedusingtwootherkindsofplan,PSPandISO2859.Finally,wedeterminedwhichplanhadtheadvantageinthequalityinspectionofoceandata.TheresultsofthepercentsamplingplanwithdifferentsamplingratesareshowninTable3,Figure3andFigure4.nisthesamplesize.Here,weadopteddifferentsamplingratesof10percent,20percentand30percentofthelotsizeforinspection.cistheacceptancenumber,and1percent,2percentand3percentofthesamplesizewereused<Table3>.Operatingcharacteristiccurves<OC-curves>arepowerfultoolsinthefieldofqualitycontrol,astheydisplaythediscriminatorypowerofanacceptancesamplingplan.Figure3showstheOC-curvesoffivelotsofoceandatasetswithsamplesize<n1=N*10%>andacceptancenumber<c2=n*2%>.Figure4showstheOC-curvesofonelotofoceandatasets<N5093>atdifferentsamplesizes<n1=N*10%,n2=N*20%andn3=N*30%>andacceptancenumbers<c1=n*1%,c2=n*2%,c3=n*3%>.Takentogether,theseresultssuggestthefollowing.Figure3.OC-curvesoffivelotsofoceandatasetswiththesamplesize<n=10%*N>andacceptancenumber<c2=2%*n>Figure4.OC-curvesofonelotofoceandatasets<N=5093>withdifferentsamplesizes<n=10%*N,20%*Nand30%*N>andacceptancenumbers<c=1%*n,2%*nand3%*n><1>Theproblemwiththismethodisthatthesampletakenfromsmalllotsmaynotberestrictiveenoughandthesampletakenfromlargelotsmaybetoorestrictive.Forexample,withthequalitylevelchangesoccurring,theacceptanceprobabilityoflargelots<N5093>decreasesfasterthanthatofsmalllots<N1450><Figure3>.<2>Differentsamplesizesandacceptancenumberscouldgeneratedifferentacceptancesamplingplans,whichcouldleadtovarieddiscriminatorypowerforthepercentacceptanceofthesamplingplan<Figure4>.ToovercometheselimitaionsofPSP,weproposedanewmethodcalledASP-ODforthequalityinspectionofoceandata.ISO2859providesasamplingplantoassesswhetheraspecificlotofindustrialproductswillmeetthecustomers’expectations.Here,wealsoemployeditforthequalityinspectionofoceandata.Furthermore,wecomparedtheirdifferencesondiscriminatorypowerandsamplerate.Theresultsoftheacceptancesamplingplansshowsthefollowing.ComparedwithISO2859,ASP-ODismoreflexibleandallowsasavinginexperimentaltimeandcost.AsforISO2859,differentlotsizesmayhavethesameacceptancesamplingplan.Forexample,aproductwithalotsizeof3500,5093or4895hasthesamesamplesize<315>andsameacceptancenumber<11><Table4andFigure5>.ThediscriminatorypowerofSPSistightlyassociatedwiththelotsize,whilethatofASP-ODisnearlyunaffectedbylotsize.Thus,ourproposedASP-ODovercomestheSPSlimitation,whichis‘strictnessforlargelotsize,tolerationforsmall’<Figure5>.ThesamplesizeofASP-ODisclosetothatofISO2859,butissignificantlysmallerthanthatofPSP<Table4andFigure6>.Figure5.OC-curvesoffivelotsofoceandatasetsdrawnusingdifferentacceptancesamplingplansFigure6.Samplerateoffivelotsofoceandatasetsobtainedusingdifferentacceptancesamplingplans4.ConclusionsAdvancesinobservationtechniquesresultinanexponentialgrowthofoceandatasets.Nevertheless,thequalityofoceandatasetshasgraduallybecomethemajorconcernofdatausers.Oceandatasetsaresignificantlydifferentfromthedatasetofindustrialproducts.Theyusuallyhaveavarietyofdistinctcharacteristics,suchaslargevolumesandbeingmulti-source,multi-dimensionandmulti-type.Theexistingacceptancesamplingplan,theincludingpercentsamplingplanandtheISO2859-basedsamplingplanarenotfitforthequalityinspectionforoceandatasets.Thus,weproposedanewplancalledASP-OD,andestimateditspotentialforthequalityinspectionforoceandata.WefoundthatASP-ODcouldovercomesomelimitationsofpercentsamplingplans,suchas‘strictnessforlargelotsize,tolerationforsmalllotsize’.ASP-ODhasconsistentandstablediscriminatorypowerindependentoflotsize.Moreover,weconsideredtheaccuracyandqualityrequirementsofbothconsumersandproducers.Thisconsiderationcouldhelpsolvetheuncertaintyofsamplerateandacceptancenumberthatoccursduringpercentsamplingplans.TheISO2859-basedplanismainlyusedforthequalityinspectionofindustrialproducts,whichhavestableproductionenvironments,consistentlotsize,anduniformproductionunits.Bycontrast,theoceandatasetshaveentirelydifferentcharacteristics.Theresource,productionenvironment,methodsofproduction,andlotsizevarygreatly.ASP-ODcanprovideacorrespondingacceptancesamplingplanforagivenlotsize.Importantly,ASP-ODdoesnotimposeadditionalfinancialburdensonconsumersorproducers.ThisevidencesuggeststhatASP-ODhasaclearadvantageoveranISO2859-basedplanininspectingthequalityofoceandatasets.Takentogether,thisstudysuggeststhatASP-ODisasuitableplanfordatasetqualityinspectionespeciallyforoceandatasets.DisclosurestatementNocompetingfinancialinterestsexist.FundingThisworkwassupportedbytheNationalScienceFoundation,China<grantsnumber61272098toD.-M.H.>,andtheNaturalScienceFoundationofShanghai,China<grantnumber13ZR1455800to22Z.-H.W.>.References[1]Balamurali,S.,etal.<2005>Designingofvariablesrepetitivegroupsamplingplaninvolvingminimumaveragesamplenumber.CommunicationsinStatistics-SimulationandComputation,vol.34,pp.799–809.[2]Burr,I.W.<1976>StatisticalQualityControlMethods,vol.16MarcelDekker,NewYork.[3]Chen,C.-H.<2013>EconomicselectionofDodgeRomigAOQLsamplingplanunderthequalityinve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洋数据集中不合格数据的数量。当不合格数据的比例大于限制质量水平〔LQL,D2的值由下式计算D2=round<N·p2><7>总剩余误差,ε,是指在ε既AQL和LQL.The角色接受概率的残余误差的总和是用于在接受抽样plan.Heren和c的值的计算,我们选择了最小ε确定为在AQL和LQL.The最佳验收取样计划接受取样计划的最优N和C配制成下列非线性优化问题s.t.<8><9>ε1是基于生产者风险的接受概率的误差。ε2是基于消费者风险的接受概率的误差。非线性优化问题基于迭代算法求解,迭代算法由Matlab软件实现。案例研究在本节中,我们采用ASP-OD,百分比抽样方法〔PSP和基于2859ISO抽样方法〔ISO2859检查海洋数据集的质量,并讨论ASP-OD在海洋数据质量检验的显著优势。研究区和数据集该研究区位于华南的一个种植区,并包含5093个监测点〔图2。数据集包括三个不同的特征、属性特征、空间特征和时间特征〔表1。在这里,沉积的泥沙数据收集使用的研究船与不确定的收集周期。水文气象地质数据进行遥感一天一次或一天两次。水质数据,底栖生物,浮游动物和浮游植物数据采用每10分钟浮标收集。海洋数据集包含的位置〔x坐标和属性信息,是代表相应的位置。在这里,我们使用的西/意值来表示的纬度和经度的监测〔表2。然而,这些参数被收集使用不同的监测工具和方法。数据采集的准确性、数据的完整性和数据的一致性很难保证,这无疑是一个异常数据集的出现。例如,地理位置的盐度〔x15、y15是70.751,这明显高于邻近的。位置的活性硅酸盐〔x12,y12是无效的。位置的总磷〔x4,y4,〔x5,y5,〔x6、y6,和〔x7,y7是从其他位置的值明显不同。因此,它需要进行海洋数据集质量检验。验收抽样方案我们设计了ASP-OD考察海洋数据的质量。在这里,收集了五个不同的监测点的海洋数据集。同时,使用其他的两种方案评估这些数据集的质量,PSP和ISO2859。最后,我们确定的计划在海洋数据质量检查的优势。不同采样率的百分比抽样方案的结果显示在表3,图3和图4。n是样本大小。在这里,我们采用了百分之10种不同的采样率,为检验批量的百分之20和百分之30。c是接受的数目,和百分之1,使用的样本百分之2和百分之3〔表3。操作特性曲线〔OC曲线在质量控制领域的强大工具,他们显示一个验收抽样方案。图3显示五大量的海洋数据集样本的OC曲线的区分能力〔n1=N×10%和接受数〔C2=n×2%。图4显示了一批海洋数据集的OC曲线〔n5093在不同的样本大小〔n1=N×10%,n2=N×20%,n3=N×30%和接受〔c1=n×1%,c2=n×2%,c3=N×3%。综上所述,这些结果表明：这个方法的问题是,小批量取的样本可能不够严格,而大批量取的样本也可能过于严格。例如,与质量水平发生变化,大批量的接收概率降低〔n5093比小的快很多〔n1450〔图3；不同的样本大小和接受可能产生不同的抽样验收计划,这将对抽样方案的百分验收导致不同的区分能力〔图4。为了克服PSP的局限性,我们提出了一种新的方法称为ASP-OD对海洋数据的质量检查。ISO2859提供了一个抽样方案,以评估是否有特定的工业产品将满足客户的期望,在这里,我们还采用了海洋数据的质量检验。此外,我们比较了歧视性的权力和抽样率的差异。验收抽样方案的结果显示如下。与ISO2859相比,ASP-OD更加灵活,可以节省实验时间和成本。对于ISO2859,不同的地段有相同的验收抽样方案。例如,一个3500,5093和4895批量产品具有相同的样本〔315和〔11同时接受数〔表4和图5。SPS的鉴别力与很多的大小密切相关,而ASP-OD几乎不受批量大小。因此,我们提出的ASP-OD克服所述SPS的限制,它是"严格对于大批量,宽容对于小批量"〔图5。ASP-OD样本量大小接近ISO2859,但比明显小于PSP〔表4和图6。4.结论观测技术的进步导致了海洋数据的指数级增长,但海洋数据集的质量已逐渐成为数据使用者的主要关注问题。海洋数据集与工业产品的数据集有显著的不同。他们通常有各种不同的特性,如体积大、多源、多维度、多类型。现有验收抽样计划,包括百分比抽样和ISO2859抽样方案不适合海洋数据的质量检查。因此,我们提出了一个新的方案称为ASP-OD,并估计其潜在的对海洋数据质量检查。我们发现,ASP-OD可以克服百分比抽样计划的一些局限性,如"严格对于大批量,小批量的宽容"。ASP-OD具有一致性和稳定性判别功率独立批量。此外,我们认为消费者和生产者的准确性和质量要求。该方案有助于解决百分抽样计划中抽样率和接受度的不确定度,主要用于工业产品的质量检验,生产环境稳定,生产规模稳定,生产单位一致。相比之下,海洋数据集有完全不同的特点。资源,生产环境,生产方法,和很多大小变化。ASP-OD可以提供给很多的大小相应的验收抽样方案。重要的是,ASP-OD不会对消费者或生产者施加额外的财政负担,这方面的证据表明,ASP-OD具有在海洋数据质量检查的基础计划ISO2859优势明显。总之,这项研究表明,ASP-OD是合适的海洋数据的数据质量检验方案。披露声明不存在竞争的经济利益。资金这项工作是由美国国家科学基金会的支持下,中国〔grantsnumber61272098tod-M.H.,和市自然科学基金、中国〔grantnumber13zr1455800to22Z-H。参考文献[1]Balamurali,S.,etal.<2005>Designingofvariablesrepetitivegroupsamplingplaninvolvingminimumaveragesamplenumber.CommunicationsinStatistics-SimulationandComputation,vol.34,pp.799–809.[2]Burr,I.W.<1976>StatisticalQualityControlMethods,vol.16MarcelDekker,NewYork.[3]Chen,C.-H.<2013>EconomicselectionofDodgeRomigAOQLsamplingplanunderthequalityinvestmentandinspectionerror.AfricanJournalpp.3516–3534.[4]Cummings,J.A.<2011>Oceandataqualitycontrol.In:Schiller,A.,&Brassington,G.,eds.OperationalOceanographyinthe21stCentury,Springer,NewYork,pp.91–121.[5]Duarte,B.P.,&