Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications.

ABSTRACT: 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

SUBMITTER: Fan H 

PROVIDER: S-EPMC9096169 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature