PetrifyisatoolforthesynthesisofboundedPetrinetsandlogicsynthesisofasynchronouscontrollers.PetrifyinitiallyperformsatokenflowanalysisofthePetrinetandproducesafinitetransitionsystem(TS).IntheinitialTS,alltransitionswiththesamelabelareconsideredasoneevent.TheTSisthentransformedandtransitionsrelabeledtofulfiltheconditionsrequiredtoobtainaPetrinetwithbisimilarortrace-equivalentbehavior.SomepropertiesforthesynthesizedPetrinetcanbeimposed(e.g.free-choice,uniquechoice,pure,state-machinedecomposable,etc.).Additionally,petrifycaninterpretthePetrinetasaSignalTransitionGraph(STG),inwhicheventsrepresentrising/fallingtransitionsofdigitalsignals.FromanSTG,petrifycansynthesizeaspeed-independentcircuitbysolvingtheproblemsofstateencoding,logicsynthesis,logicdecompositionandtechnologymappingontoagatelibrary.Petrifycanalsosynthesizecircuitundertimingassumptionsspecifiedbythedesignerorautomaticallygeneratedbythetool.PetrifyreadstheinputdescriptionfromstdinandwritestheresultingSTGtostdoutunlessotherwisespecified

带modbuscrc校验串口工具，可自动附带crc校验值，。
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unity混淆工具

nmanydataanalysistasks,oneisoftenconfrontedwithveryhighdimensionaldata.Featureselectiontechniquesaredesignedtofindtherelevantfeaturesubsetoftheoriginalfeatureswhichcanfacilitateclustering,classificationandretrieval.Thefeatureselectionproblemisessentiallyacombinatorialoptimizationproblemwhichiscomputationallyexpensive.Traditionalfeatureselectionmethodsaddressthisissuebyselectingthetoprankedfeaturesbasedoncertainscorescomputedindependentlyforeachfeature.Theseapproachesneglectthepossiblecorrelationbetweendifferentfeaturesandthuscannotproduceanoptimalfeaturesubset.InspiredfromtherecentdevelopmentsonmanifoldlearningandL1-regularizedmodelsforsubsetselection,weproposehereanewapproach,called{\emMulti-Cluster/ClassFeatureSelection}(MCFS),forfeatureselection.Specifically,weselectthosefeaturessuchthatthemulti-cluster/classstructureofthedatacanbebestpreserved.Thecorrespondingoptimizationproblemcanbeefficientlysolvedsinceitonlyinvolvesasparseeigen-problemandaL1-regularizedleastsquaresproblem.ItisimportanttonotethatMCFScanbeappliedinsuperised,unsupervisedandsemi-supervisedcases.Ifyoufindthesealgoirthmsuseful,weappreciateitverymuchifyoucanciteourfollowingworks:PapersDengCai,ChiyuanZhang,XiaofeiHe,"UnsupervisedFeatureSelectionforMulti-clusterData",16thACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining(KDD'10),July2010.BibtexsourceXiaofeiHe,DengCai,andParthaNiyogi,"LaplacianScoreforFeatureSelection",AdvancesinNeuralInformationProcessingSystems18(NIPS'05),Vancouver,Canada,2005Bibtexsource

韦尔科姆3WelkomopdeEmmauscollegeinformatica网站运营站点可访问Emmauscollege（不提供服务，我们将继续努力，我们将继续为您提供服务）DeSiteBevatDrieonderdeleneenoverzichtvanhet法定刑Opdrachten工具的死因我们死了

Successinyourcalculuscoursestartshere!JamesStewart'sCALCULUStextsareworld-widebest-sellersforareason:theyareclear,accurate,andfilledwithrelevant,real-worldexamples.WithCALCULUS,SeventhEdition,Stewartconveysnotonlytheutilityofcalculustohelpyoudeveloptechnicalcompetence,butalsogivesyouanappreciationfortheintrinsicbeautyofthesubject.Hispatientexamplesandbuilt-inlearningaidswillhelpyoubuildyourmathematicalconfidenceandachieveyourgoalsinthecourse!

这是用XS128驱动OLED的程序。
------------------------------------CodeWarrior5.0/1Target:MC9S12XS128Crystal:16.000Mhzbusclock:16.000MHzpllclock:32.000MHz============================================使用说明：OLED电源使用5V。
----------------G电源地3.3V接5V电源，电源跟模块之间串接100欧姆电阻，并加3.3V钳位二极管D0PORTE_PE2单片机跟模块之间串接2k-3.3k电阻D1 PORTE_PE3单片机跟模块之间串接2k-3.3k电阻RSTPORTE_PE4单片机跟模块之间串接2k-3.3k电阻DCPORTE_PE5单片机跟模块之间串接2k-3.3k电阻CS已接地，不用接============================================如果用户使用的是5V单片机，请看用户手册，切勿烧毁模块！============================================*/#include"derivative.h"#include#include#include"OLED12864.h"//PLL初始化子程序BUSClock=16MvoidSetBusCLK_48M(void){CLKSEL=0X00;//disengagePLLtosystemPLLCTL_PLLON=1;//turnonPLLSYNR=0xc0|0x05;REFDV=0x80|0x01;POSTDIV=0x00;//pllclock=2*osc*(1+SYNR)/(1+REFDV)=96MHz;_asm(nop);//BUSCLOCK=48M_asm(nop);while(!(CRGFLG_LOCK==1));//whenpllissteady,thenuseit;CLKSEL_PLLSEL=1;//engagePLLtosystem;}voidDly_ms(intms){intii,jj;if(ms＜1)ms=1;for(ii=0;ii＜ms;ii++)for(jj=0;jj＜1335;jj++);//16MHz--1ms//for(jj=0;jj＜4006;jj++);//48MHz--1ms//for(jj=0;jj＜5341;jj++);//64MHz--1ms}//============================MAIN()===========================/*********************主函数************************************/voidmain(void){bytei=0;SetBusCLK_48M();DDRB=0XFF;DDRE=0XFF;PORTB=0XFF;LCD_Init();DisableInterrupts;for(;;){//LCD_Fill(0xff);//Dly_ms(100);//LCD_Fill(0x00);//Dly_ms(2000);//LCD_CLS();//LCD_Print(12,0,"广州Beyond科技");//LCD_Print(15,2,"飞思卡尔智能车");//LCD_Print(43,4,"专营店");//LCD_Print(15,6,"智能车首选液晶");//LCD_P8x16Str(48,4,"OLED");//LCD_P6x8Str(16,6,"b

Abstract—MotordrivetechnologiesarebeingincorporatedintotheEVandHEVautomobileswitheverincreasingspeed.Withtheoutputtorquetobethemaincontroltarget,variouscontrolalgorithmsaredevelopedthataimtoachievehightorqueaccuracywhilemaximizingthemachineenergyefficiency.Mostofsuchcontrolalgorithmsentailprocedurestocharacterizethemachineundercontrol.Thispaperdocumentsadetailed,industriallypracticalprocedureofcharacterizingthetorquelookuptableforinteriorpermanentmagnet(IPM)synchronousmachines.Thecharacterizedlookuptablecanbeutilizedinopen-looptorquecontrolalgorithms,andisverifiedtoachievehightorquecontrolaccuracyandsuitablefortorquecontrolofIPMinautomotiveapplications.

常用接口的FPGA程序，用ISE打开工程文件即可，包括CAN,ETH,IIC,IWBBUSCHANGE,UART,USB,VGA.

LearnhowtomodelandtrainadvancedneuralnetworkstoimplementavarietyofComputerVisiontasksKeyFeaturesTraindifferentkindsofdeeplearningmodelfromscratchtosolvespecificproblemsinComputerVisionCombinethepowerofPython,Keras,andTensorFlowtobuilddeeplearningmodelsforobjectdetection,imageclassification,similaritylearning,imagecaptioning,andmoreIncludestipsonoptimizingandimprovingtheperformanceofyourmodelsundervariousconstraintsBookDescriptionDeeplearninghasshownitspowerinseveralapplicationareasofArtificialIntelligence,especiallyinComputerVision.ComputerVisionisthescienceofunderstandingandmanipulatingimages,andfindsenormousapplicationsintheareasofrobotics,automation,andsoon.Thisbookwillalsoshowyou,withpracticalexamples,howtodevelopComputerVisionapplicationsbyleveragingthepowerofdeeplearning.Inthisbook,youwilllearndifferenttechniquesrelatedtoobjectclassification,objectdetection,imagesegmentation,captioning,imagegeneration,faceanalysis,andmore.YouwillalsoexploretheirapplicationsusingpopularPythonlibrariessuchasTensorFlowandKeras.Thisbookwillhelpyoumasterstate-of-the-art,deeplearningalgorithmsandtheirimplementation.WhatyouwilllearnSetupanenvironmentfordeeplearningwithPython,TensorFlow,andKerasDefineandtrainamodelforimageandvideoclassificationUsefeaturesfromapre-trainedConvolutionalNeuralNetworkmodelforimageretrievalUnderstandandimplementobjectdetectionusingthereal-worldPedestrianDetectionscenarioLearnaboutvariousproblemsinimagecaptioningandhowtoovercomethembytrainingimagesandtexttogetherImplementsimilaritymatchingandtrainamodelforfacerecognitionUnderstandtheconceptofgenerativemodelsandusethemforimagegenerationDeployyourdeeplearningmodelsandoptimizethemforhighperformanceWhoThisBookIsForThisbookistargeted

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。