A temporal-based SVM approach for the detection and identification of pollutant gases in a gas mixture

Air toxicity and pollution phenomena are on the rise across the planet. Thus, the detection and control of gas pollution are nowadays major economic and environmental challenges. There exists a wide variety of sensors that can detect gas pollution events. However, they are either gas-specific or wea...

Full description

Saved in:
Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-04, Vol.52 (6), p.6065-6078
Main Authors: Djeziri, Mohand A., Djedidi, Oussama, Morati, Nicolas, Seguin, Jean-Luc, Bendahan, Marc, Contaret, Thierry
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Chemical Sciences
Computer Science
Decision making
Engineering Sciences
False alarms
Gas mixtures
Gases
Kernel functions
Machine Learning
Machines
Manufacturing
Mechanical Engineering
Micro and nanotechnologies
Microelectronics
Nitrogen dioxide
Other
Pollutants
Pollution detection
Processes
Statistics
Support vector machines
Toxicity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Air toxicity and pollution phenomena are on the rise across the planet. Thus, the detection and control of gas pollution are nowadays major economic and environmental challenges. There exists a wide variety of sensors that can detect gas pollution events. However, they are either gas-specific or weak in the presence of gas mixtures. This paper handles this issue by presenting method based on a Temporal-based Support Vector Machine for for the detection and identification of several toxic gases in a gas mixture. The considered gases are carbon monoxide (CO), ozone (O 3 ) and nitrogen dioxide (NO 2 ). Furthermore, an incremental algorithm is proposed in this paper for the selection of the best performing kernel function in terms of accuracy and simplicity of implementation. Then, a decision-making algorithm based on the rate of appearance of a class on a moving window is proposed to improve decision making in presence of uncertainties. This algorithm allows the user to master the false-alarms and no-detection dilemma, and quantify the level of confidence attributed to the decision. Experimental results, obtained with different gas mixtures, show the effectiveness of the proposed approach with 100% of accuracy in the learning and testing stages.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02761-0