提出抗异常值的半参数回归方法，提升低成本传感器跨站点校准精度。
Provably Outlier-resistant Semi-parametric Regression for Transferable Calibration of Low-cost Air-quality Sensors
- 基于半参数模型设计抗异常值训练算法，提升鲁棒性。
- 在4个站点、2个季节、6种传感器上实现跨场景高精度校准。
- 模型可解释且能标记过拟合，适合真实环境部署。
我们针对印度最大规模多站点、多季节、多传感器、多污染物移动空气质量监测网络中低成本空气品质（LCAQ）一氧化碳（CO）传感器的校准问题开展案例研究。LCAQ传感器在构建密集、大范围空气质量监测网络和应对高污染水平中发挥关键作用。然而，将其与监管级监测仪校准成本高、耗时长，尤其在地理分布广泛的大规模部署中更为显著。本文提出RESPIRE方法，用于校准LCAQ传感器以检测环境中的CO浓度。相较于文献中常见的基线校准方法，RESPIRE在跨站点、跨季节和跨传感器设置下均表现出更优的预测性能。该方法具备可证明的抗异常值训练算法，以及可解释模型，能够识别模型过拟合情况。实验基于覆盖4个站点、2个季节和6个传感器套件的广泛部署数据展开。代码已开源：https://github.com/purushottamkar/respire。
原文摘要 · Abstract (English)
We present a case study for the calibration of Low-cost air-quality (LCAQ) CO sensors from one of the largest multi-site-multi-season-multi-sensor-multi-pollutant mobile air-quality monitoring network deployments in India. LCAQ sensors have been shown to play a critical role in the establishment of dense, expansive air-quality monitoring networks and combating elevated pollution levels. The calibration of LCAQ sensors against regulatory-grade monitors is an expensive, laborious and time-consuming process, especially when a large number of sensors are to be deployed in a geographically diverse layout. In this work, we present the RESPIRE technique to calibrate LCAQ sensors to detect ambient CO (Carbon Monoxide) levels. RESPIRE offers specific advantages over baseline calibration methods popular in literature, such as improved prediction in cross-site, cross-season, and cross-sensor settings. RESPIRE offers a training algorithm that is provably resistant to outliers and an explainable model with the ability to flag instances of model overfitting. Empirical results are presented based on data collected during an extensive deployment spanning four sites, two seasons and six sensor packages. RESPIRE code is available at https://github.com/purushottamkar/respire.
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