TY - GEN
T1 - Discriminant analysis of industrial gases for electronic nose applications
AU - Rehman, Atiq Ur
AU - Bermak, Amine
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/17
Y1 - 2018/8/17
N2 - This work is a part of ongoing research project for optimization of the Electronic Nose System (ENS) for its applications related to the identification of industrial gases. Two different experimental datasets of several gases are collected in a laboratory setup using two different sensor arrays. A dataset of six different gases (C3H8, Cl2, CO, CO2, SO2 and NO2 is collected using a commercially available array of seven Figaro gas sensors. Another dataset of three gases (C2H6O, CH4 and CO) is collected using a 4 × 4 tin-oxide sensors array which is built in the In-house foundry. In this paper some of the existing state of the art classification models are tested for the classification of experimentally acquired datasets. The existing classification models are used to analyze the behavior of the data acquired. The models that are tested for identification of gases are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA), and K-Nearest Neighbor (KNN). Besides testing these classification models, fuzzy C means (FCM) clustering is also tested for the separation of clusters of gases.
AB - This work is a part of ongoing research project for optimization of the Electronic Nose System (ENS) for its applications related to the identification of industrial gases. Two different experimental datasets of several gases are collected in a laboratory setup using two different sensor arrays. A dataset of six different gases (C3H8, Cl2, CO, CO2, SO2 and NO2 is collected using a commercially available array of seven Figaro gas sensors. Another dataset of three gases (C2H6O, CH4 and CO) is collected using a 4 × 4 tin-oxide sensors array which is built in the In-house foundry. In this paper some of the existing state of the art classification models are tested for the classification of experimentally acquired datasets. The existing classification models are used to analyze the behavior of the data acquired. The models that are tested for identification of gases are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA), and K-Nearest Neighbor (KNN). Besides testing these classification models, fuzzy C means (FCM) clustering is also tested for the separation of clusters of gases.
KW - Cluster analysis
KW - Electronic nose system
KW - Industrial gases
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85053106742&partnerID=8YFLogxK
U2 - 10.1109/CIVEMSA.2018.8439969
DO - 10.1109/CIVEMSA.2018.8439969
M3 - Conference contribution
AN - SCOPUS:85053106742
SN - 9781538646182
T3 - CIVEMSA 2018 - 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings
BT - CIVEMSA 2018 - 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd Annual IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2018
Y2 - 12 June 2018 through 13 June 2018
ER -