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Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning

机译：分析和优化一系列低成本气体传感器，以用于具有机器学习功能的空气质量测量设备

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摘要

Low-cost gas sensors have been proposed in place of conventional expensive instruments however they have issues due to cross-sensitivity with other pollutants. Several different types of metal oxide and electrochemical sensors and machine learning methods were evaluated. The objectives were to determine which type of sensor, metal oxide or electrochemical, is better at measuring traffic-related air pollution and whether deep neural networks (DNN) and recurrent neural networks (RNN) improve sensor performance. Three devices were deployed across three sites, two in Toronto and one in Beijing to evaluate the performance of calibration. Calibration was performed with two weeks of data from only one site and evaluated with the remaining data. The combination of metal oxide and electrochemical sensors were more accurate when measuring NOx. When targets were normalized, the RNN performed better than DNN and linear calibration, however, not when applied to measuring data well outside the range for calibration.
机译：已经提出了低成本的气体传感器来代替常规的昂贵的仪器，但是由于与其他污染物的交叉敏感性，它们存在问题。评估了几种不同类型的金属氧化物和电化学传感器以及机器学习方法。目的是确定哪种类型的传感器（金属氧化物或电化学传感器）更适合测量与交通相关的空气污染，以及深度神经网络（DNN）和递归神经网络（RNN）是否可以提高传感器性能。在三个地点部署了三台设备，其中两个位于多伦多，一个位于北京，以评估校准的性能。仅使用一个站点的两周数据进行校准，并使用剩余数据进行评估。在测量NOx时，金属氧化物和电化学传感器的结合更为精确。将目标标准化后，RNN的性能优于DNN和线性校准，但是，当应用于远远超出校准范围的数据测量时，RNN的性能则不佳。

著录项

  • 作者

    Herod, Kris Karl.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Chemical engineering.;Computational chemistry.;Environmental engineering.
  • 学位 M.A.S.
  • 年度 2018
  • 页码 88 p.
  • 总页数 88
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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