Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich...

Full description

Bibliographic Details
Main Authors: Hamed Mirzaei, Milad Ramezankhani, Emily Earl, Nishat Tasnim, Abbas S. Milani, Mina Hoorfar
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Sensors
Subjects:
hydrogen detection
HENG
transfer learning
sparse autoencoder
microfluidic gas sensor
Online Access:https://www.mdpi.com/1424-8220/22/20/7696

Internet

https://www.mdpi.com/1424-8220/22/20/7696

Similar Items