简介：在极的半导体量点(QD)的bipolaron的温度和LO声子效果被使用Tokuda学习修改线性联合的操作员方法和Lee-Low-Pines变化方法。为LO声子的吝啬的数字和bipolaron的有效的团的表情被导出。数字结果证明bipolaron的LO声子的吝啬的数字与与增加联合力量的电子声子增加在二个电子，而是增加之间的温度和相对距离r减少。bipolaronM的有效的团*当r更小时，与增加在二个电子之间的相对距离r很快增加，并且，它在那以后在r4.05rp到达最大值，M*与增加r慢慢地减少。bipolaronM的有效的团*与增加温度减少。显著地联合力量的电子声子影响LO声子和有效集体M的吝啬的数字的变化*与相对距离r和温度参数。

简介：Duetothedemandofdataprocessingforpolariceradarinourlaboratory,aCurveletThresholdingNeuralNetwork(TNN)noisereductionmethodisproposed,andanewthresholdfunctionwithinfinite-ordercontinuousderivativeisconstructed.ThemethodisbasedonTNNmodel.InthelearningprocessofTNN,thegradientdescentmethodisadoptedtosolvetheadaptiveoptimalthresholdsofdifferentscalesanddirectionsinCurveletdomain,andtoachieveanoptimalmeansquareerrorperformance.Inthispaper,thespecificimplementationstepsarepresented,andthesuperiorityofthismethodisverifiedbysimulation.Finally,theproposedmethodisusedtoprocesstheiceradardataobtainedduringthe28thChineseNationalAntarcticResearchExpeditionintheregionofZhongshanStation,Antarctica.Experimentalresultsshowthattheproposedmethodcanreducethenoiseeffectively,whilepreservingtheedgeoftheicelayers.