語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Analyzing and Optimizing an Array of...
~
Herod, Kris Karl.
FindBook
Google Book
Amazon
博客來
Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning./
作者:
Herod, Kris Karl.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
88 p.
附註:
Source: Masters Abstracts International, Volume: 58-01.
Contained By:
Masters Abstracts International58-01(E).
標題:
Chemical engineering. -
電子資源:
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.
LDR
:02016nmm a2200313 4500
001
2199724
005
20181029135739.5
008
201008s2018 ||||||||||||||||| ||eng d
020
$a
9780438183735
035
$a
(MiAaPQ)AAI10689895
035
$a
(MiAaPQ)toronto:17051
035
$a
AAI10689895
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Herod, Kris Karl.
$3
3426469
245
1 0
$a
Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2018
300
$a
88 p.
500
$a
Source: Masters Abstracts International, Volume: 58-01.
500
$a
Adviser: Greg J. Evans.
502
$a
Thesis (M.A.S.)--University of Toronto (Canada), 2018.
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.
590
$a
School code: 0779.
650
4
$a
Chemical engineering.
$3
560457
650
4
$a
Computational chemistry.
$3
3350019
650
4
$a
Environmental engineering.
$3
548583
690
$a
0542
690
$a
0219
690
$a
0775
710
2
$a
University of Toronto (Canada).
$b
Chemical Engineering & Applied Chemistry.
$3
3190728
773
0
$t
Masters Abstracts International
$g
58-01(E).
790
$a
0779
791
$a
M.A.S.
792
$a
2018
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10689895
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9376273
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入