简介:Inthispaper,simultaneousuniformapproximationandmeanconvergenceofquasi-HermiteinterpolationanditsderivativebasedonthezerosofJacobipolynomialsareconsideredseparately.Thedegreesofthecorrespondingapproximationsarerespectivelygivenalso.Someknownresultsareimprovedaudextended.
简介:摘要飞机的起落架系统为飞机在地面停放和操作时提供支撑作用。起落架系统包括两个主起落架和一个前起落架。本文主要介绍了B737的起落架系统、系统部件,一些实例故障分析和维护方法,对B737的起落架系统有更深的了解。
简介:Thegravityp-medianmodelisanimportantimprovementtothewidely-usedp-medianmodel.However,thereisstilladebateonitsvalidityinempiricalapplications.Previousstudiesevendoubtthesignificanceofthegravityp-medianmodel.UsingacasestudyoftertiaryhospitalsinShenzhen,China,thisstudyre-examinesthedifferencebetweenthegravityp-medianmodelwiththep-medianmodel,bydecomposingthedifferencebetweenthetwomodelsintogravityruleandvariantattraction.Thisstudyalsoproposesamodifiedgravityp-medianmodelbyincorporatingadistancethreshold.Theempiricalresultssupportthevalidityofthegravityp-medianmodel,andalsorevealthatonlywhentheattractionsofcandidatefacilitylocationsarevariablewillthegravityp-medianmodelleadtodifferentresultswiththep-medianmodel.Thedifferencebetweenthemodifiedgravityp-medianmodelandthegravityp-medianmodelisalsoexamined.Moreover,theimpactsofthedistance-decayparameteranddistancethresholdonsolutionsareinvestigated.Resultsindicatethatalargerdistance-decayparametertendstoresultinamoredisperseddistributionofoptimalfacilitiesandasmalleraveragetraveltime,andasmallerdistancethresholdcanbetterpromotethespatialequityoffacilities.Theproposedmethodcanalsobeappliedinstudiesofothertypesoffacilitiesorinotherareas.
简介:High-frequencystocktrendpredictionusingmachinelearnershasraisedsubstantialinterestinliterature.Nevertheless,thereisnogoldstandardtoselecttheinputsforthelearners.Thispaperinvestigatestheapproachofadaptiveinputselection(AIS)forthetrendpredictionofhigh-frequencystockindexpriceandcomparesitwiththecommonlyuseddeterministicinputsetting(DIS)approach.TheDISapproachisimplementedthroughcomputationoftechnicalindicatorvaluesondeterministicperiodparameters.TheAISapproachselectsthemostsuitableindicatorsandtheirparametersforthetime-varyingdatasetusingfeatureselectionmethods.Twostate-of-the-artmachinelearners,supportvectormachine(SVM)andartificialneuralnetwork(ANN),areadoptedaslearningmodels.AccuracyandF-measureofSVMandANNmodelswithboththeapproachesarecomputedbasedonthehigh-frequencydataofCSI300index.TheresultssuggestthattheAISapproachusingt-statistics,informationgainandROCmethodscanachievebetterpredictionperformancethantheDISapproach.Also,theinvestmentperformanceevaluationshowsthattheAISapproachwiththesamethreefeatureselectionmethodsprovidessignificantlyhigherreturnsthantheDISapproach.
简介:由在卡尔弗特和Gupta的非线性的accretivemappings的范围的和上使用不安理论,我们在答案u∈L~S的存在上学习抽象结果(包含p拉普拉斯算符操作员的非线性的边界价值问题的Ω),在此2≤s≤+∞,并且(2N)/(N+1)<p≤2为N(≥1)它表示R~N的尺寸。获得结果,一些新技术在这篇论文被使用。在这里的这篇论文和我们的方法讨论的方程是扩展和补充到L.魏和Z.的相应结果他。