使用极端随机树算法对低成本二氧化碳传感器进行校准评估
Tiago Araújo1,2, Lígia Silva3, Ana Aguiar4
1Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Parnamirim 59124-455, Brazil.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究提高了低成本的二氧化碳 (CO2) 传感器的准确性,使用极端随机树算法. 机器学习显著提高了室内和室外空气质量监测的二氧化碳传感器数据质量.
科学领域:
- 环境科学 环境科学
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 精确的二氧化碳 (CO2) 监测对于评估室内和室外空气质量至关重要.
- 低成本的二氧化碳传感器经常受到精度下降的影响,影响数据可靠性.
- 现有的校准方法可能无法完全解决低成本传感器技术的局限性.
研究的目的:
- 开发和验证一种机器学习方法,以提高低成本二氧化碳传感器的准确性.
- 评估拟议方法在不同传感器类型 (MOS,NDIR) 和环境 (室内,室外) 中的有效性.
- 调查影响校准性能的因素,如传感器曝光时间和数据多样性.
主要方法:
- 使用极端随机树算法进行传感器校准.
- 在室内环境中收集金属氧化物半导体 (MOS) 和非分散性红外 (NDIR) CO2传感器的实验数据.
- 分析了第三方数据集,其中包括地理分布的低成本NDIR传感器,用于室外验证.
主要成果:
- 实现了对NDIR传感器的平均绝对误差 (MAE) 的显著降低,高达90%的室内和98%的室外.
- 在室内实验中使用MOS传感器证明了出色的线性 (r^2值为0.994).
- 确定传感器曝光时间和数据多样性是有效校准的重要因素.
结论:
- 机器学习,特别是极端随机树算法,有效地提高了低成本二氧化碳传感器的准确性.
- 拟议的校准方法改善了室内和室外空气质量监测应用的数据质量.
- 这种方法是多功能性的,适用于各种低成本的气体传感器技术和环境条件.
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