简介：Inordertoovercomethedisadvantagesoflowaccuracyrate,highcomplexityandpoorrobustnesstoimagenoiseinmanytraditionalalgorithmsofcloudimagedetection,thispaperproposedanovelalgorithmonthebasisofMarkovRandomField(MRF)modeling.Thispaperfirstdefinedalgorithmmodelandderivedthecorefactorsaffectingtheperformanceofthealgorithm,andthen,thesolvingofthisalgorithmwasobtainedbytheuseofBeliefPropagation(BP)algorithmandIteratedConditionalModes(ICM)algorithm.Finally,experimentsindicatethatthisalgorithmforthecloudimagedetectionhashigheraverageaccuracyratewhichisabout98.76%andtheaverageresultcanalsoreach96.92%fordifferenttypeofimagenoise.

简介：为在目标之中为相互作用建模的一个典型Markov网络能处理错误合并问题，而是它在合作追踪者之中由于盲目比赛受不了标记的问题。在这篇论文，我们为追踪的多目标建议一个运动限制Markov网络模型。由扩充典型Markov，有特定的Markov的网络锁住哪个带动议限制优先，这个建议模型能克服盲目比赛并且甚至在严重吸藏的情况中指导标签到相应目标。另外，运动限制优先作为本地潜在的功能被提出并且能容易在新奇模型的联合分发表示被合并。试验性的结果证明我们的模型比在解决错误的另外的方法优异合并并且同时并且高效地把问题标记。

简介：Recently,anewsoft-insoft-outdetectionalgorithmbasedontheMarkovChainMonteCarlo(MCMC)simulationtechniqueforMultiple-InputMultiple-Output(MIMO)systemsisproposed,whichisshowntoperformsignificantlybetterthantheirspheredecodingcounterpartswithrelativelylowcomplexity.However,theMCMCsimulatorislikelytogettrappedinafixedstatewhenthechannelSNRishigh,thuslotsofrepetitivesamplesareobservedandtheaccuracyofAPosterioriProbability(APP)estimationdeteriorates.Tosolvethisproblem,animprovedversionofMCMCsimulator,namedforced-dispersedMCMCalgorithmisproposed.Basedontheaposteriorivarianceofeachbit,theGibbssamplerismonitored.Oncethetrappedstateisdetected,thesampleisdispersedintentionallyaccordingtotheaposteriorivariance.Extensivesimulationshowsthat,comparedwiththeexistingsolution,theproposedalgorithmenablesthemarkovchaintotravelmorestates,whichensuresanear-optimalperformance.

简介：Inthispaper,elitistreconstructiongeneticalgorithm(ERGA)basedonMarkovrandomfield(MRF)isintroducedforimagesegmentation.Inthisalgorithm,apopulationofpossiblesolutionsismaintainedateverygeneration,andforeachsolutionafitnessvalueiscalculatedaccordingtoafitnessfunction,whichisconstructedbasedontheMRFpotentialfunctionaccordingtoMetropolisfunctionandBayesianframework.Aftertheimprovedselection,crossoverandmutation,anelitistindividualisrestructuredbasedonthestrategyofrestructuringelitist.ThisprocedureisprocessedtoselectthelocationthatdenotesthelargestMRFpotentialfunctionvalueinthesamelocationofallindividuals.Thealgorithmisstoppedwhenthechangeoffitnessfunctionsbetweentwosequentgenerationsislessthanaspecifiedvalue.Experimentsshowthattheperformanceofthehybridalgorithmisbetterthanthatofsometraditionalalgorithms.

简介：一个改进方法被建议模仿骚乱介绍的焕发，基于有限Markov州的模型。作为到文学的一个对比，一致地分布式的变量发生在某个状态期间，它比传统的Markov模型与更少状态贡献紧张变化的相等的模拟。它的也发现有20个状态的建议Markov模型在汽车协变性分析提供令人满意的近似给试验性的结果。而且，停止概率和吝啬的褪色时间是比有相等的状态的传统的Markov模型的那些更精确的。