简介:AfterexpandingthecapacitybywideningthetrolleyoftheNo.3sinteringmachine,severeunevensinteringoccurredinthetrolley’slateraldistribution,whichaffectedtheoutputandqualityofsinter.Inthisstudy,thequantitativeevaluationindicesoftheburdenuniformdistributioninthewidthdirectionofthesinteringmachineisintroducedforthefirsttime.Bymeasuringthetemperatureofdischargedgas,aplanetemperaturefieldisconstructed.Throughanalyzingthetemperaturefieldandtheburdenlayer’sdifferentialthermalequilibrium,amathematicalmodelforevaluatingtheindices,whichisanonlinereflectionofthedegreeofuniformdistribution,isbuilt.Followingtheimprovementsinburdendistributionequipment,theoptimizationoftheignitionsystemandthedynamicadjustmentoftheprocess,theproblemofunevensinteringinlateraldistributionhasbeensolved,andthequalityandtheyieldofsinterhavebeenimproved.
简介:<正>Weshowthatforanydifferentiableinvolutiononanr-dimensionalmanifold(M,T)whosefixedpointsetFisadisjointunionofrealprojectivespacesofconstantdimension2n,wehave:ifr=4nthen(M,T)isbordantto(F×F,twist),if2n
简介:ThispaperdescribesageneralizedtweakableblockcipherHPH(Hash-Permutation-Hash),whichisbasedonapublicrandompermutationPandafamilyofalmost-XOR-universalhashfunctionsH={HK}K∈κasatweakandkeyschedule,anddefinedasy=HPHK((t1,t2),x)=P(xHK(t1))HK(t2),whereKisakeyrandomlychosenfromakeyspace/C,(tl,t2)isatweakchosenfromavalidtweakspaceT,xisaplaintext,andyisaciphertext.WeprovethatHPHisasecurestrongtweakablepseudorandompermutation(STPRP)byusingH-coefficientstechnique.ThenwefocusonthesecurityofHPHagainstmulti-keyandrelated-keyattacks.WeprovethatHPHachievesbothmulti-keySTPRPsecurityandrelated-keySTPRPsecurity.HPHcanbeextendedtowideapplications.Itcanbedirectlyappliedtoauthenticationandauthenticatedencryptionmodes.WeapplyHPHtoPMAC1andOPP,provideanimprovedauthenticationmodeHPMACandanewauthenticatedencryptionmodeOPH,andprovethatthetwomodesachievesingle-keysecurity,multi-keysecurity,andrelated-keysecurity.
简介:Thispaperinvestigatesaclassofevenorderfunctionaldifferentialequationswithdampedterm,andderivestwonewoscillatorycriteriaofsolution.
简介:ByRiccatitransformation,weestablishsomenewoscillationcriteriaforaclassofevenorderdelaydifferentialequationswithnonlinearterm.Insomesense,theresultsobtainedextendsomeknownresultsintheliterature.
简介:动人的镜子以甚至速度的速度平均和距离决定光谱图质量和Fourier变换分光计(英尺)的分辨率。改进英尺的性能,一个精确控制系统被设计认识到动人的镜子(公里)的以甚至速度的互给的行动。激光参考测量干涉仪与通过极化转移阶段被介绍,它让位置测量决定到达一半激光的波长。目前,公里改变方向,干扰信号的配置是复杂的,它用一个普通方向判断方法导致测量计数错误。在这篇论文,当公里改变方向时,一个改进方向判断方法基于介入的信号的分析被建议,并且相应逻辑电路在域里被设计可编程的门数组(FPGA)。公里被动人的卷驾驶直接电流(DC)线性马达,和数学模型被描述。根据系统特征和要求的分析,fuzzy-PID控制策略被建议。fuzzy-PID控制算法和它的数字实现被学习。以便减少计算数量,为不同输入的PID参数被计算机预先计算并且作为桌子在存储器存储了,那么数字控制算法是的fuzzy-PID的主要工作桌子的简单查找,它使计算数量很小、容易在一块数字信号处理(DSP)芯片认识到。控制系统被认识到,并且实验结果证明动人的镜子在没有几乎的0.1s以内的速度活动范围平均在改变方向以后射。
简介:<正>Inthisdynamicandfreshseasonofspringwhentheflowersareinfullblossom,onbehalfoftheChineseAssociationforInternationalUnderstanding(CAFIU),I’dliketoextendcordialgreetingsandwishestothereadersoftheInternationalUnderstandinghomeandabroadandpeoplefromallwalksoflifeforyourlongstandingattentionandsupportfortheworkanddevelopmentofCAFIU.
简介:Background:LeafAreaIndex(LAI)isanimportantparameterusedinmonitoringandmodelingofforestecosystems.Theaimofthisstudywastoevaluateperformanceoftheartificialneuralnetwork(ANN)modelstopredicttheLAIbycomparingtheregressionanalysismodelsastheclassicalmethodinthesepureandeven-agedCrimeanpineforeststands.Methods:OnehundredeighttemporarysampleplotswerecollectedfromCrimeanpineforeststandstoestimatestandparameters.EachsampleplotwasimagedwithhemisphericalphotographstodetecttheLAI.ThepartialcorrelationanalysiswasusedtoassesstherelationshipsbetweenthestandLAIvaluesandstandparameters,andthemultivariatelinearregressionanalysiswasusedtopredicttheLAIfromstandparameters.DifferentartificialneuralnetworkmodelscomprisingdifferentnumberofneuronandtransferfunctionsweretrainedandusedtopredicttheLAIofforeststands.Results:ThecorrelationcoefficientsbetweenLAIandstandparameters(standnumberoftrees,basalarea,thequadraticmeandiameter,standdensityandstandage)weresignificantatthelevelof0.01.Thestandage,numberoftrees,siteindex,andbasalareawereindependentparametersinthemostsuccessfulregressionmodelpredictedLAIvaluesusingstandparameters(/?;adj=0.5431).AscorrespondingmethodtopredicttheinteractionsbetweenthestandLAIvaluesandstandparameters,theneuralnetworkarchitecturebasedontheRBF4-19-1withGaussianactivationfunctioninhiddenlayerandtheidentityactivationfunctioninoutputlayerperformedbetterinpredictingLAI(SSE(12.1040),MSE(0.1223),RM5E(0.3497),AIC(0.1040),BIC(-777310)andR2(0.6392))comparedtotheotherstudiedtechniques.Conclusion:TheANNoutperformedthemultivariateregressiontechniquesinpredictingLAIfromstandparameters.TheANNmodels,developedinthisstudy,mayaidinmakingforestmanagementplanninginstudyforeststands.