Sensors, Volume 22, Issue 20
2022 October-2 - 389 articles
Cover Story: Alternative fuel sources, such as HENG, are highly sought after by governments for lowering carbon emissions, which in turn requires highly sensitive approaches for safety monitoring. Amongst sensing methods, MOS gas sensors are strong tools for detecting lower levels of HENG elements; however, their working mechanism results in a lack of real-time identification of the exact concentrations of gases. Leveraging a microfluidic detector, we propose a Sparse Autoencoder-based Transfer Learning (SAE-TL) method for estimating HENG elements’ concentrations. Based on the collected time series data, the SAE-TL showed dominant performance compared to typical machine learning models. The framework is implementable in real-world applications for the fast adaptation of new types of MOS sensor responses. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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