简介：Thispaperdescribestheinverstigationdevotedtoestablishsuitableweightsinafeed-forwardneuralnetworkrealizingthenarrow-bandfilteringmapinthecaseofadaptivelineenhancement(ALE)bytheutilityoftheoptimumcommonlearningratebackpropagation(OCLRBP)algorithm.Itisfoundthatafeed-forwardnetworkwith64linearinputandoutputneurons,and8oddsigmoidneuronsinthehiddenlayer,i.e.an(64→8→64)architecture,couldestablishthespecificinput-outputfunctioninthecaseofrelativelylowsignal-to-noiseradio.Onlyisaninputsignalconsistingofmixedperiodicandbroad-bandcomponentsavailabletothenetworksystem.Afterlearning,boththe"fanning-in-connectionpatterns",eachofwhichconsistsofweightsfanningintoahidden-neuronFromalltheoutputsofinput-neurons,andthe"fanning-out-connectionpatterns",eachofwhichconsistsofweightsfanningoutfromahidden-neurontoalltheinputsofoutput-neurons,aretunedtotheperiodicsignals.Thenonline

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简介：Inacomputationalgrid,jobsmustadapttothedynamicallychangingheterogeneousenvironmentwithanobjectiveofmaintainingthequalityofservice.Inordertoenableadaptiveexecutionofmultiplejobsrunningconcurrentlyinacomputationalgrid,weproposeanintegratedperformance-basedresourcemanagementframeworkthatissupportedbyamulti-agentsystem(MAS).Themulti-agentsysteminitiallyallocatesthejobsontodifferentresourceprovidersbasedonaresourceselectionalgorithm.Later,duringruntime,ifperformanceofanyjobdegradesorqualityofservicecannotbemaintainedforsomereason(resourcefailureoroverloading),themulti-agentsystemassiststhejobtoadapttothesystem.Thispaperfocusesonapartofourframeworkinwhichadaptiveexecutionfacilityissupported.Adaptiveexecutionfacilityisavailedbyreallocationandlocaltuningofjobs.Mobile,aswellasstaticagentsareemployedforthispurpose.Thepaperprovidesasummaryofthedesignandimplementationanddemonstratestheefficiencyoftheframeworkbyconductingexperimentsonalocalgridtestbed.

简介：从图象序列的背景抽取的急和稳定性是不兼容的，(GMM)如果景色的背景区域被改变，也就是说，常规Gaussian混合物什么时候当模特儿，被用来重建背景，提取背景变得坏直到转变完了。一个新奇适应方法被介绍在一个Hilbert空格调整GMM的学习的率。背景抽取被当作在Hilbert空格来临到某个点的进程因此即时学习率能被计算在二幅邻近的提取背景图象之间的距离获得，并且背景的稳定性的一个判断方法也被得到。与常规GMM相比，方法同时有高急和好稳定性，并且它能在网上调整学习的率。实验证明它比常规GMM好，特别在背景抽取的转变过程。

简介：Inthispaper,weproposeafacerecognitionapproach-StructedSparseRepresentation-basedclassificationwhenthemeasurementofthetestsampleislessthanthenumbertrainingsamplesofeachsubject.Whenthisconditionisnotsatisfied,weexploitNearestSubspaceapproachtoclassifythetestsample.Inordertoadaptallthecases,wecombinethetwoapproachestoanadaptiveclassificationmethod-Adaptiveapproach.TheadaptiveapproachyieldsgreaterrecognitionaccuracythantheSRCapproachandCRC_RLSapproachwithlowsamplerateontheExtendYaleBdataset.Anditismoreefficientthanothertwoapproaches.

简介：这份报纸涉及一口跳入的液体喷气的三维的数字模拟。在喷气附近形成一个空气洞的短暂过程，捕获这个大塑造toroidal的水泡的一个开始大的空气水泡，和分散进更小的水泡被分析。一个稳定的有限元素方法(女性)基于适应、未组织的格子在平行数字模拟下面被采用并且结合了一个水平集合方法追踪在空气和液体之间的接口。这些模拟证明液体喷气的惯性开始压抑水池的表面，形成包围液体喷气的一个环形的空气洞。随后在液体水池被形成的一个toroidal液体旋涡导致空气洞倒塌，并且接着乘火车空气进从在液体喷气附近的不稳定的环形的空气差距区域的液体水池。

简介：Thispaperpresentanimprovedpreciseintegrationalgorithmfortransientanalysisofheattransferandsomeotherproblems.Theoriginalpreciseintegrationmethodisimprovedbymeanoftheinve-rseaccuracyanalysissothattheparameterN,whichhasbeentakenasaconstantandanindependentpa-rameterwithoutconsiderationoftheproblemsintheoriginalmethod,canbegeneratedautomaticallybythealgorithmitself.Thus,theimprovdealgorithmisadaptiveandtheaccucacyofthealgorithmisnotdependentonthelengthofthetimestepintheintegrationprocess.Itisshownthatthenumericalresultsobtainedbythemethodproposedaremoreaccuratethanthoseobtainedbytheconventionaltimeintegrationmethodssuchasthedifferencemethodandothers.Fourexamplesaregiventodemonstratethevalidity,accuracyandeffi-ciencyofthenewmethod.

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简介：这研究介绍一个新方法识别敏感区域，它由为台风调用潮湿的潜在的涡度(MPV)的否定异例是坚定的适应观察。否定MPV的区域是对称的不稳定性区域并且能为台风作为敏感区域被拿，这被发现适应观察。在2008的三台风，Nuri，Fung-wong，和Fengshen，在MM5的帮助下被模仿模型。这些台风很好在开始的12个小时被模仿，这被显示出。把调查基于这些，MPV的计算顺序被执行。结果证明MPV的否定最大值总是绕为所有盒子的台风眼睛，它意味着敏感区域总是也在他们附近。

简介：FortheARMAXsystemwithunknowncoefficientstheoptimaladaptivecontrolisdesignedsothatthefollowingrequirementsaremetsimultaneously:1)thetransferfunctionfromareferencesignaltothesystemoutputintheclosedloopequalsaprescribedrationalfunction;2)undertheconstraintmentionedin1)aquadraticlossfunctionisminimized;3)theparameterestimateisstronglyconsistent.

简介：ThispaperpresentsapragmaticadaptiveschemeforTuCMoverslowlyfadingchannels.Theadaptiveschemeemploysasingleturbocodedmodulatorcomposedofavariable-rateturboencoderandavariable-ratevariable-powerMQAMforallfadingregions,soithasanacceptablecomplexitytoimplement.TheoptimaladaptiveTuCMschemeisdeterminedsubjecttovarioussystemconstraints.Simulationshavebeenperformedtomeasuretheperformanceoftheschemefordifferentparameters.Itisshownthatadoptingboththeturbocodedmodulatorandthetransmitpowerachievesaperformancewithin2.5dBofthefadingchannelcapacity.

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简介：整体变换(et)方法被显示了在为适应观察推广提供指导有用。它在整体subspace用它的相应转变矩阵为各可能的推广预言预报错误变化减小。在这份报纸，一个新基于et的敏感(et)方法，以分析错误变化减小计算预报错误变化减小的坡度，被建议为可能的适应观察指定区域。et是ET的第一顺序近似；它要求就一个转变矩阵的一计算，增加计算效率(在计算费用的60%80%减小)。ETS坡度的明确的数学明确的表达被导出并且描述。ET和et方法为比较被用于飓风艾琳(2011)箱子和一个重降雨箱子。数字结果暗示ETS和et估计的敏感区域是类似的。然而，et是更有效的，特别地当分辨率更高，整体成员的数字更大时。

简介：Stochasticapproximationproblemistofindsomerootorextremumofanon-linearfunctionforwhichonlynoisymeasurementsofthefunctionareavailable.TheclassicalalgorithmforstochasticapproximationproblemistheRobbins-Monro(RM)algorithm,whichusesthenoisyevaluationofthenegativegradientdirectionastheiterativedirection.InordertoacceleratetheRMalgorithm,thispapergivesaflamealgorithmusingadaptiveiterativedirections.Ateachiteration,thenewalgorithmgoestowardseitherthenoisyevaluationofthenegativegradientdirectionorsomeotherdirectionsundersomeswitchcriterions.Twofeasiblechoicesofthecriterionsarepro-posedandtwocorrespondingflamealgorithmsareformed.Differentchoicesofthedirectionsunderthesamegivenswitchcriterionintheflamecanalsoformdifferentalgorithms.Wealsoproposedthesimultanousperturbationdifferenceformsforthetwoflamealgorithms.Thealmostsurelyconvergenceofthenewalgorithmsareallestablished.Thenumericalexperimentsshowthatthenewalgorithmsarepromising.

简介：设计者被要求计划让未来扩大估计格子的未来利用。有效建模和预报技术，它将高效地使用信息在可用数据，可用资料包含了的这个工具，被要求，以便重要数据性质能被提取并且投射进未来。这研究基于划分算法(MMPA)的多模型建议一个适应方法，为短期的电负担用真实数据预报。格子的利用开始用趋于增加的季节的ARIMA被建模(汽车回归的综合移动平均数)模型。建议方法经过数据使用听说并且当模特儿正常周期的行为电的格子。任何一个ARMA(汽车回归的移动平均数)或州空间的模型能被用于当模特儿的负担模式。象可以出现在夏天或意外差错(停电)期间的意外山峰那样的负担异例也被建模。如果负担模式不匹配负担的正常行为，一个异例被检测，而且，当模式匹配异例的一个已知的盒子时，异例的类型被识别。真实数据被使用，真实盒子基于测量被测试大量希腊公共力量合作S.A，雅典，希腊。过滤算法的应用适应多模型成功地识别正常周期的行为和电的格子的任何不平常的活动。建议方法的表演也与由ARIMA模型生产了那相比。

简介：Agoodmodelcanextractusefulinformationaboutthetarget'sstatefromobservationseffectively.Therearemanymodelsusedtotrackinga,maneuveringtargetsuchasconstant-velocity(CV)model,Singeraccelerationmodel(zero-meanfirst-orderMarkovmodel)andcurrentmodel(mean-adaptiveaccelerationmodel),etc.Whileduetothecomplexityofmaneuveringtarget,toseekthetargetmodelwhichcangetbetterperformanceisstillasubjectworthyofstudy.BasedonstatisticsrelationbetweentheautocorrelationfunctionandthecovarianceofMarkovrandomprocessing,thispaperdevelopsamodelwhichcanadaptivelyadjustsystemparametersonline.Simulationsshowthegoodestimationperformancegetbythemodeldevelopedhere,andcomparingCV,Singerandcurrentmodels,themodelcanadaptivelygetthemodelparameterwhiletrackingthetrajectoryandneedn'tdoingseveralteststoobtainaprioriparameter.

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简介：Awoofer–tweeteradaptiveopticalstructuredilluminationmicroscope(AOSIM)ispresented.Bycombiningalow-spatial-frequencylarge-strokedeformablemirror(woofer)withahigh-spatial-frequencylow-strokedeformablemirror(tweeter),weareabletoremovebothlarge-amplitudeandhigh-orderaberrations.Inaddition,usingthestructuredilluminationmethod,ascomparedtowidefieldmicroscopy,theAOSIMcanaccomplishhighresolutionimagingandpossessesbettersectioningcapability.TheAOSIMwastestedbycorrectingalargeaberrationfromatriallensintheconjugateplaneofthemicroscopeobjectiveaperture.TheexperimentalresultsshowthattheAOSIMhasapointspreadfunctionwithanFWHMthatis140nmwide(usingawaterimmersionobjectivelenswithNA=1.1)aftercorrectingalargeaberration(5.9μmpeak-to-valleywavefronterrorwith2.05μmRMSaberration).Afterstructuredlightilluminationisapplied,theresultsshowthatweareabletoresolvetwobeadsthatareseparatedby145nm,1.62×belowthediffractionlimitof235nm.Furthermore,wedemonstratetheapplicationoftheAOSIMinthefieldofbioimaging.Thesampleunderinvestigationwasagreen-fluorescentprotein-labeledDrosophilaembryo.Theaberrationsfromtherefractiveindexmismatchbetweenthemicroscopeobjective,theimmersionfluid,thecoverslip,andthesampleitselfarewellcorrected.UsingAOSIMwewereabletoincreasetheSNRforourDrosophilaembryosampleby5×.

简介：Totacklemulticollinearityorill-conditioneddesignmatricesinlinearmodels,adaptivebiasedestimatorssuchasthetime-honoredSteinestimator,theridgeandtheprincipalcomponentestimatorshavebeenstudiedintensively.Tostudywhenabiasedestimatoruniformlyoutperformstheleastsquaresestimator,somesufficientconditionsareproposedintheliterature.Inthispaper,weproposeaunifiedframeworktoformulateaclassofadaptivebiasedestimators.Thisclassincludesallexistingbiasedestimatorsandsomenewones.Asufficientconditionforoutperformingtheleastsquaresestimatorisproposed.Intermsofselectingparametersinthecondition,wecanobtainalldouble-typeconditionsintheliterature.