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Geng Z, Yang F, Chen X, et al. Gaussian process based modeling and experimental design for sensor calibration in drifting environments. Sens Actuators B Chem. 2015;216:321-331doi: 10.1016/j.snb.2015.03.071.
Geng, Z., Yang, F., Chen, X., & Wu, N. (2015). Gaussian process based modeling and experimental design for sensor calibration in drifting environments. Sensors and actuators. B, Chemical, 216321-331. https://doi.org/10.1016/j.snb.2015.03.071
Geng, Zongyu, et al. "Gaussian process based modeling and experimental design for sensor calibration in drifting environments." Sensors and actuators. B, Chemical vol. 216 (2015): 321-331. doi: https://doi.org/10.1016/j.snb.2015.03.071
Geng Z, Yang F, Chen X, Wu N. Gaussian process based modeling and experimental design for sensor calibration in drifting environments. Sens Actuators B Chem. 2015 Sep;216:321-331. doi: 10.1016/j.snb.2015.03.071. PMID: 26924894; PMCID: PMC4764506.
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Sens Actuators B Chem. 2015 Sep;216:321-331. doi: 10.1016/j.snb.2015.03.071.

Gaussian process based modeling and experimental design for sensor calibration in drifting environments.

Sensors and actuators. B, Chemical

Zongyu Geng, Feng Yang, Xi Chen, Nianqiang Wu

Affiliations

  1. Industrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV 26506, USA.
  2. Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
  3. Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA.

PMID: 26924894 PMCID: PMC4764506 DOI: 10.1016/j.snb.2015.03.071

Abstract

It remains a challenge to accurately calibrate a sensor subject to environmental drift. The calibration task for such a sensor is to quantify the relationship between the sensor's response and its exposure condition, which is specified by not only the analyte concentration but also the environmental factors such as temperature and humidity. This work developed a Gaussian Process (GP)-based procedure for the efficient calibration of sensors in drifting environments. Adopted as the calibration model, GP is not only able to capture the possibly nonlinear relationship between the sensor responses and the various exposure-condition factors, but also able to provide valid statistical inference for uncertainty quantification of the target estimates (e.g., the estimated analyte concentration of an unknown environment). Built on GP's inference ability, an experimental design method was developed to achieve efficient sampling of calibration data in a batch sequential manner. The resulting calibration procedure, which integrates the GP-based modeling and experimental design, was applied on a simulated chemiresistor sensor to demonstrate its effectiveness and its efficiency over the traditional method.

Keywords: Bootstrapping; Design of experiments; Gaussian process model; Sensor calibration; Sensor drift

References

  1. Bioinformatics. 2009 May 15;25(10):1300-6 - PubMed
  2. Sens Actuators B Chem. 2007 Aug 8;125(2):396-407 - PubMed
  3. Anal Chem. 2014 Jul 15;86(14):6753-7 - PubMed

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