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Predictive analytics system for gas ...

Al-Nasser, Lubna.

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Predictive analytics system for gas detection and concentration estimation using gas sensor arrays.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive analytics system for gas detection and concentration estimation using gas sensor arrays./
Author:	Al-Nasser, Lubna.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	134 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Contained By:	Dissertation Abstracts International78-05B(E).
Subject:	Industrial engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10250117
ISBN:	9781369461121

Predictive analytics system for gas detection and concentration estimation using gas sensor arrays.
Al-Nasser, Lubna.

Predictive analytics system for gas detection and concentration estimation using gas sensor arrays. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 134 p.

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2016.

Sensor array systems are excellent analytical tools for gas detection and estimation of their concentrations and are extensively used in fields such as manufacturing, homeland security, healthcare, and medical use. The main components of a sensor array are the sensor component or the hardware and the analytical component which is related to the pattern recognition component. This research focuses on developing a systematic approach to optimize the detection of gases and concentration estimation using data mining techniques including signal processing, features extraction and selection, and sensor selection optimization.

ISBN: 9781369461121Subjects--Topical Terms:

526216
Industrial engineering.

Predictive analytics system for gas detection and concentration estimation using gas sensor arrays.
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100 1 $a Al-Nasser, Lubna. $3 3281146
245 1 0 $a Predictive analytics system for gas detection and concentration estimation using gas sensor arrays.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 134 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
500 $a Adviser: Susan S. Lu.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2016.
520 $a Sensor array systems are excellent analytical tools for gas detection and estimation of their concentrations and are extensively used in fields such as manufacturing, homeland security, healthcare, and medical use. The main components of a sensor array are the sensor component or the hardware and the analytical component which is related to the pattern recognition component. This research focuses on developing a systematic approach to optimize the detection of gases and concentration estimation using data mining techniques including signal processing, features extraction and selection, and sensor selection optimization.
520 $a The research provides an integrated framework to improve the performance of sensor array. The systematic research mainly involves the development of a new feature selection methodology to find a near-optimal subset of sensors from a pool of sensors using hierarchical clustering and correct recognition rate of sensors which improves the classification performance and significantly reduce the number of sensors included in the array. Moreover, a mathematical model is proposed to select the significant features that map the sensor response and can be used to best discriminate between the classes of gases.
520 $a The framework proposes a comprehensive multi-objective optimization problem to find the optimal set of sensors which maximizes the accuracy of classification and minimize the error of concentration estimation. The experimental results show a significant and promising approach to enhance the overall performance of a sensor array system.
590 $a School code: 0792.
650 4 $a Industrial engineering. $3 526216
650 4 $a Engineering. $3 586835
650 4 $a Analytical chemistry. $3 3168300
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710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $3 2104041
773 0 $t Dissertation Abstracts International $g 78-05B(E).
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792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10250117