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Analyzing and Optimizing an Array of...

Herod, Kris Karl.

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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.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning./
Author:	Herod, Kris Karl.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	88 p.
Notes:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
Subject:	Chemical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10689895
ISBN:	9780438183735

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

Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 88 p.

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2018.

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.

ISBN: 9780438183735Subjects--Topical Terms:

560457
Chemical engineering.

Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning.
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520 $a 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.
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