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Humidity Monitoring Using a Flexible Polymer- based Microwave Sensor and Machine Learning

dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorNGOUNE, Bernard Bobby
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorHALLIL, Hamida
dc.contributor.authorGEORGE, Julien
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorDEJOUS, Corinne
dc.contributor.authorCLOUTET, ERIC
dc.contributor.authorBONDU, Benoit
dc.contributor.authorBILA, Stephane
dc.contributor.authorBAILLARGCAR, Dominique
dc.date.accessioned2024-06-04T09:40:57Z
dc.date.available2024-06-04T09:40:57Z
dc.date.issued2022-10
dc.date.conference2022-10-30
dc.identifier.issn2473-2001en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/200249
dc.description.abstractEnThis work presents humidity monitoring using a highly sensitive flexible microwave sensor associated with polyethyleneimine sensitive material with high endurance against temperature by a machine learning approach. A climatic chamber was used to generate humidity at different temperatures and a commercialized humidity and temperature sensor was used as a reference. The sensor showed a high frequency sensitivity (−3.65 and -7.69 MHz/%RH in a range of 30 - 50 %RH and 50 - 70%RH respectively), low hysteresis, good reversibility and repeatability. Moreover, the extracted sensing features were associated to linear regression, support vector machine, random forest and k-nearest neighbours regression algorithms for humidity prediction. The performance of the different models was evaluated and random forest (MAE: 1.63 %RH, R2: 0.970, pred time: 0.44s) and k-nearest neighbours ((MAE: 1.52 %RH, R2: 0.971, pred time: 0.12s) showed the best results on prediction on the test data set.
dc.language.isoENen_US
dc.subjectTemperature sensors
dc.subjectSupport vector machines
dc.subjectSensitivity
dc.subjectHumidity
dc.subjectMicrowave sensors
dc.subjectPredictive models
dc.subjectFeature extraction
dc.subjectMicrowave sensor
dc.subjectHumidity
dc.subjectMachine learning approach
dc.subjectPolymer sensitive material
dc.subjectPassive resonator
dc.title.enHumidity Monitoring Using a Flexible Polymer- based Microwave Sensor and Machine Learning
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.1109/SENSORS52175.2022.9967126en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.page1-4en_US
bordeaux.hal.laboratoriesIMS : Laboratoire de l'Intégration du Matériau au Système - UMR 5218en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.conference.title2022 IEEE Sensorsen_US
bordeaux.countryusen_US
bordeaux.title.proceeding2022 IEEE Sensorsen_US
bordeaux.teamWAVES-MDAen_US
bordeaux.teamWAVES-DEVICESen_US
bordeaux.conference.cityDallasen_US
hal.invitedouien_US
hal.proceedingsouien_US
hal.conference.end2022-11-02
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2022-10&rft.spage=1-4&rft.epage=1-4&rft.eissn=2473-2001&rft.issn=2473-2001&rft.au=NGOUNE,%20Bernard%20Bobby&HALLIL,%20Hamida&GEORGE,%20Julien&DEJOUS,%20Corinne&CLOUTET,%20ERIC&rft.genre=unknown


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