Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring

Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring

Blog Article

Recently, the emergence of low-cost sensors have allowed electronic noses to be Revisiting residential self-selection issues: A life-oriented approach considered for densifying the actual air pollution monitoring networks in urban areas.Electronic noses are affected by changes in environmental conditions and sensor drifts over time.Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different.For these reasons, the calibration process has become very expensive and time consuming.

To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative.Among them, direct standardization shows good efficiency for calibration transfer.In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint Electric Vehicle Paradise? Exploring the Value Chains of Green Extractivism x-y distances) for data selection and support vector machine regression to match between electronic noses.The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide.

Experimental results show that our method gave the highest efficiency compared to classical direct standardization.