TY - JOUR
T1 - Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
AU - Fan, Han
AU - Schaffernicht, Erik
AU - Lilienthal, Achim J.
N1 - Publisher Copyright:
Copyright © 2022 Fan, Schaffernicht and Lilienthal.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.
AB - Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.
KW - electronic nose
KW - ensemble learning
KW - gas detection
KW - gas sensing
KW - metal oxide semiconductor sensor
KW - open sampling systems
KW - robotic olfaction
UR - http://www.scopus.com/inward/record.url?scp=85130296086&partnerID=8YFLogxK
U2 - 10.3389/fchem.2022.863838
DO - 10.3389/fchem.2022.863838
M3 - Article
AN - SCOPUS:85130296086
SN - 2296-2646
VL - 10
JO - Frontiers in Chemistry
JF - Frontiers in Chemistry
M1 - 863838
ER -