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基于迁移学习策略的压板开关状态识别.docx

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基于迁移学习策略的压板开关状态识别 Title:TransferLearningforRecognitionofPressurePlateSwitchState Abstract: Inrecentyears,theadvancementofInternetofThings(IoT)deviceshasledtothewidespreaduseofpressureplateswitchesinvariousapplications,suchassmarthomesandindustrialautomation.Theaccuraterecognitionofthepressureplateswitchstate(on/off)playsacrucialroleinenablingseamlessinteractionbetweenhumansandthesesmartsystems.However,duetoenvironmentalvariationsandinherentnoise,accuratelydetectingthestateofapressureplateswitchisachallengingtask. Thispaperproposesatransferlearningapproachtoaddressthechallengeofpressureplateswitchstaterecognition.Transferlearningleveragestheknowledgelearnedfromasourcedomaintoachievebetterperformanceonatargetdomain.Byutilizingpre-trainedmodels,theproposedtransferlearningframeworkaimstoovercomethelimitationsoflimitedlabeleddataanddomainshiftbetweendifferentenvironments. Introduction: Thepressureplateswitchisacommonlyuseddevicethatdetectsmechanicalpressurechanges.Itiswidelyutilizedinmanyapplications,includinghomeautomationsystems,securitysystems,andindustrialmachinery.Theaccuraterecognitionofthepressureplateswitchstate(on/off)isessentialforprovidingtimelyresponsesandappropriateactionsintheseapplications.However,accuratelydetectingthestateofapressureplateswitchischallengingduetofactorssuchasnoise,varyingenvironmentalconditions,anddifferentinstallationsetups. MethodsandFramework: Theproposedtransferlearningframeworkconsistsofthefollowingsteps: 1.DataCollection:Gatheradatasetofpressureplateswitchstates,includingbothonandoffstates.Thisdatasetshouldbediverse,coveringvariousenvironmentalconditionsandinstallationsetups. 2.Pre-training:Utilizeapre-trainedmodel,suchasaconvolutionalneuralnetwork(CNN),onarelateddatasetortask.Thisallowsthemodeltolearngeneralfeaturesthatcanbeusefulforswitchstaterecognition. 3.Fine-tuning:Adaptthepre-trainedmodeltothespecificpressureplateswitchrecognitiontaskusingthecollecteddataset.Thelayersofthepre-trainedmodelaremodifiedandre-trainedusingtransferl