function[CellSpace_nextstepVehicleSpace]=TrafficSimulating(SimTime,TimeStep,CellSpace_current,CellSpace_nextstep,VehicleSpace,VMAX)%TRAFFICSIMULATINGSummaryofthisfunctiongoeshere%仿真程序主体CellSpace_Init=CellSpace_nextstep;%读取信号配时数据SignalCycleMat=load('SignalCycleInfo.ini');sCycle=SignalCycleMat(1);%周期长度sGreenTime=SignalCycleMat(2);%绿灯时长sRedTime=SignalCycleMat(3);%红灯时长%%是否加载换道模型LaneChangingModelINIMat=load('LaneChangingModeInfo.ini');UseLaneChangingModelFlag=LaneChangingModelINIMat(1);end

MIMOOFDMSimulator:OFDM.m:OFDMSimulator(outerfunction)create_channel.m:GeneratesaRayleighfadingfrequency-selectivechannel,parametrizedbytheantennaconfiguration,theOFDMconfiguration,andthepower-delayprofile.svd_decompose_channel.m:Sincefullchannelknowledgeisassumed,transmissionisacrossparallelsingularvaluemodes.Thisfunctiondecomposesthechannelintothesemodes.BitLoad.m:Applythebit-loadingalgorithmtoachievethedesiredbitandenergyallocationforthecurrentchannelinstance.ComputeSNR.m:Giventhesubcarriergains,thissimplefunctiongeneratestheSNRvaluesofeachchannel(eachsingularvalueoneachtoneisaseparatechannel).chow_algo.m:ApplyChow'salgorithmtogenerateaparticularbitandenergyallocation.EnergyTableInit.m:GiventheSNRvalues,formatableofenergyincrementsforeachchannel.campello_algo.m:ApplyCampello'salgorithmtoconvergetotheoptimalbitandenergyallocationforthegivenchannelconditions.ResolvetheLastBit.m:Anoptimalbit-loadingofthelastbitrequiresauniqueoptimization.modulate.m:Modulatetherandominputsequenceaccordingtothebitallocationsforeachchannel.ENC2.mat:BPSKModulatorENC4.mat:4-QAMModulator(Graycoded)ENC16.mat:16-QAMModulator(Graycoded)ENC64.mat:64-QAMModulator(Graycoded)ENC256.mat:256-QAMModulator(Graycoded)precode.m:Precodethetransmittedvectorateachtimeinstancebyfilteringthemodulatedvectorwiththeright-inverseofthechannel'srightsingluarmatrix.ifft_cp_tx_blk.m:IFFTblockoftheOFDMsystem.channel.m:ApplythechanneltotheOFDMframe.fft_cp_rx_blk.m:FFTblockoftheOFDMsystem.shape.m:Completethediagonalizationofthechannelbyfilteringthereceivedvectorwiththeleft-inverseofthechannel'sleftsingularmatrix.demodulate.m:Performanearestneighborsearchknowingthetransmitconstellationused.

InverseDistancetoaPower（反距离加权插值法）Kriging（克里金插值法）MinimumCurvature（最小曲率）ModifiedShepard"sMethod（改进谢别德法）NaturalNeighbor（自然邻点插值法）NearestNeighbor（最近邻点插值法）PolynomialRegression（多元回归法）RadialBasisFunction（径向基函数法）TriangulationwithLinearInterpolation（线性插值三角网法）MovingAverage（移动平均法）LocalPolynomial（局部多项式法）"＞InverseDistancetoaPower（反距离加权插值法）Kriging（克里金插值法）MinimumCurvature（最小曲率）ModifiedShepard"sMethod（改进谢别德法）NaturalNeighbor（自然邻点插值法）NearestNeighbor（最近邻点插值法）PolynomialRegression（?[更多]

为了提高光伏发电功率预测的精度，本文在结合灰色预测算法（GM）与神经络预测算法优点的基础上，提出一种基于灰色径向基函数（RadicalBasisFunction，RBF）和神经网络光伏发电功率预测模型。
该预测模型综合了灰色预测算法所需历史数据少以及RBF神经网络预测算法自学习能力强的优点。
最后，运用南昌地区夏季和冬季晴天、阴天、雨天光伏发电历史数据在MATLAB应用平台编程实现对GM-RBF神经网络预测模型的预测精度进行验证，得出基于GM-RBF神经网络光伏发电预测模型在夏季晴天预测误差为6.495%、夏季阴天预测误差为12.146%、夏季雨天预测误差为21.531%、冬季晴天预测误差为8.457%、冬季阴天预测误差14.379%、冬季雨天预测误差为18.495%，其预测精度均高于灰色预测算法和RBF神经网络预测算法

Thisfunctionplotsa3D-Cube.Youcanchooseorientation,size,rotaion,colorsandtransparency.TheZIP-filecontainssomeexamplesofusingthisfunctiontocreateanimationsorobjects.

Matlabsimulink流水灯模型slx文件简单的模型供初学者学习sfunction.m文件

Deeplearningsimplifiedbytakingsupervised,unsupervised,andreinforcementlearningtothenextlevelusingthePythonecosystemTransferlearningisamachinelearning(ML)techniquewhereknowledgegainedduringtrainingasetofproblemscanbeusedtosolveothersimilarproblems.Thepurposeofthisbookistwo-fold;firstly,wefocusondetailedcoverageofdeeplearning(DL)andtransferlearning,comparingandcontrastingthetwowitheasy-to-followconceptsandexamples.Thesecondareaoffocusisreal-worldexamplesandresearchproblemsusingTensorFlow,Keras,andthePythonecosystemwithhands-onexamples.ThebookstartswiththekeyessentialconceptsofMLandDL,followedbydepictionandcoverageofimportantDLarchitecturessuchasconvolutionalneuralnetworks(CNNs),deepneuralnetworks(DNNs),recurrentneuralnetworks(RNNs),longshort-termmemory(LSTM),andcapsulenetworks.Ourfocusthenshiftstotransferlearningconcepts,suchasmodelfreezing,fine-tuning,pre-trainedmodelsincludingVGG,inception,ResNet,andhowthesesystemsperformbetterthanDLmodelswithpracticalexamples.Intheconcludingchapters,wewillfocusonamultitudeofreal-worldcasestudiesandproblemsassociatedwithareassuchascomputervision,audioanalysisandnaturallanguageprocessing(NLP).Bytheendofthisbook,youwillbeabletoimplementbothDLandtransferlearningprinciplesinyourownsystems.WhatyouwilllearnSetupyourownDLenvironmentwithgraphicsprocessingunit(GPU)andCloudsupportDelveintotransferlearningprincipleswithMLandDLmodelsExplorevariousDLarchitectures,includingCNN,LSTM,andcapsulenetworksLearnaboutdataandnetworkrepresentationandlossfunctionsGettogripswithmodelsandstrategiesintransferlearningWalkthroughpotentialchallengesinbuildingcomplextransferlearningmodelsfromscratchExplorereal-worldresearchproblemsrelatedtocompute

GeneralDescriptionTheMAX96705isacompactserializerwithfeaturesespeciallysuitedforautomotivecameraapplications.ItisfunctionandpincompatiblewiththeMAX9271.Inhighbandwidthmode,theparallel-clockmaximumis116MHzfor12-bitlinearorcombinedHDRdatatypes.Theembeddedcontrolchanneloperatesat9.6kbpsto1MbpsinUART,I2C,andmixedUART/I2Cmodes,allowingprogrammingofserializer,deserializer,andcameraregistersindependentofvideotiming.Fordrivinglongercables,theIChasprogrammablepre/deemphasis.Programmablespreadspectrumisavailableontheserialoutput.TheserialoutputmeetsISO10605andIEC61000-4-2ESDstandards.Thecoresupplyrangeis1.7Vto1.9V,andtheI/Osupplyrangeis1.7Vto3.6V.TheMAX96705isavailableina32-pin(5mmx5mm)TQFNpackagewith0.5mmleadpitch,andoperatesoverthe-40°Cto+115°Ctemperaturerange

Despitetheincreasinguseofcomputers,thebasicneedformathematicaltablescontinues.Tablesserveavitalroleinpreliminarysurveysofproblemsbeforeprogrammingformachineoperation,andtheyareindispensabletothousandsofengineersandscientistswithoutaccesstomachines.Becauseofautomaticcomputers,however,andbecauseofrecentscientificadvances,agreatervarietyoffunctionsandahigheraccuracyoftabulationthanhavebeenavailableuntilnowarerequired.In1954,aconferenceonmathematicaltables,sponsoredbyM.I.T.andtheNationalScienceFoundation,mettodiscussamodernizationandextensionofJahnkeandEmde'sclassicaltablesoffunctions.Thisvolume,published10yearslaterbytheU.S.DepartmentofCommerce,istheresult.Designedtoincludeamaximumofinformationandtomeettheneedsofscientistsinallfields,itisamonumentalpieceofwork,acomprehensiveandself-containedsummaryofthemathematicalfunctionsthatariseinphysicalandengineeringproblems.Thebookcontains29setsoftables,sometoashighas20places:mathematicalconstants;physicalconstantsandconversionfactors(6tables);exponentialintegralandrelatedfunctions(7);errorfunctionandFresnelintegrals(12);Besselfunctionsofinteger(12)andfractional(13)order;integralsofBesselfunctions(2);Struveandrelatedfunctions(2);confluenthypergeometricfunctions(2);Coulombwavefunctions(2);hypergeometricfunctions;Jacobianellipticandthetafunctions(2);ellipticintegrals{9);Weierstrassellipticandrelatedfunctions;paraboliccylinderfunctions{3);Mathieufunctions(2);spheroidalwavefunctions(5);orthogonalpolynomials(13);combinatorialanalysis(9);numericalinterpolation,differentiationandintegration(11);probabilityfunctions(ll);scalesofnotation(6);miscellaneousfunctions(9);Laplacetransforms(2);andothers.Eachofthesesectionsisprefacedbyalistofrelatedformulasandgraph

C#inDepth,FourthEditionisyourkeytounlockingthepowerfulnewfeaturesaddedtothelanguageinC#5,6,and7.FollowingtheexpertguidanceofC#legendJonSkeet,you’llmasterasynchronousfunctions,expression-bodiedmembers,interpolatedstrings,tuples,andmuchmore.Thepowerful,flexibleC#programminglanguageisthefoundationof.NETdevelopment.Evenaftertwodecadesofsuccess,it’sstillgettingbetter!ExcitingnewfeaturesinC#6and7makeiteasierthanevertotakeonbigdataapplications,cloud-centricwebdevelopment,andcross-platformsoftwareusing.NETCore.There’sneverbeenabettertimetolearnC#indepth.C#inDepth,FourthEditionisarevisededitionofthebestsellerwrittenbyC#legendJonSkeet.Thisauthoritativeandengagingguideisyourkeytounlockingthispowerfullanguage,includingthenewfeaturesofC#6and7.Init,Jonintroducesexpression-bodiedmembers,interpolatedstrings,patternmatching,andmore.Real-worldexamplesdriveitallhome.Bytheendofthisawesomebook,you’llbewritingC#codewithskill,style,andconfidence.

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。