NGOUNE, Bernard Bobby; HALLIL, Hamida; GEORGE, Julien; DEJOUS, Corinne; CLOUTET, ERIC; BONDU, Benoit; BILA, Stephane; BAILLARGCAR, Dominique

doi:10.1109/SENSORS52175.2022.9967126

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NGOUNE, Bernard Bobby
Laboratoire de l'intégration, du matériau au système [IMS]

HALLIL, Hamida
Laboratoire de l'intégration, du matériau au système [IMS]

GEORGE, Julien

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2022 IEEE Sensors, 2022 IEEE Sensors, 2022-10-30, Dallas. 2022-10p. 1-4

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This 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.< Réduire

Mots clés

Temperature sensors

Support vector machines

Sensitivity

Humidity

Microwave sensors

Predictive models

Feature extraction

Microwave sensor

Humidity

Machine learning approach

Polymer sensitive material

Passive resonator

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

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