Gaussian process based modeling and experimental design for sensor calibration in drifting environments.
Gaussian process based modeling and experimental design for sensor calibration in drifting environments.
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DOI：
10.1016/j.snb.2015.03.071
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Wu N
中科院分区：
文献类型：
--
作者：
Geng Z;Yang F;Chen X;Wu N
准确校准受环境漂移影响的传感器仍然是一个挑战。这种传感器的校准任务是量化传感器的响应与其暴露条件之间的关系，其不仅由分析物浓度而且由环境因素（例如温度和湿度）指定。这项工作开发了一个高斯过程（GP）为基础的程序，在漂移环境中的传感器的有效校准。采用GP作为校准模型，GP不仅能够捕获传感器响应与各种测量条件因素之间可能的非线性关系，而且能够为目标估计的不确定性量化提供有效的统计推断（例如，未知环境的估计分析物浓度）。建立在GP的推理能力，实验设计方法的开发，以实现有效的抽样校准数据的批量顺序的方式。由此产生的校准程序，它集成了基于GP的建模和实验设计，应用于模拟化学电阻传感器，以证明其有效性和效率超过传统方法。
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.