Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions
Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaining good results with underperforming sensors that, at first glance, would be discarded. For this aim, we characterized chemical gas sensors with low repeatability and random drift towards both dangerous and...
Saved in:
| Main Authors: | Guillem Domènech-Gil, Donatella Puglisi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/97/1/87 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning
by: Guillem Domènech-Gil, et al.
Published: (2024-03-01) -
The Use of Low-Cost Gas Sensors for Air Quality Monitoring with Smartphone Technology: A Preliminary Study
by: Domenico Suriano, et al.
Published: (2025-05-01) -
Virtual MOS Sensor Array Design for Ammonia Monitoring in Pig Barns
by: Raphael Parsiegel, et al.
Published: (2025-04-01) -
Chemical Sensor Technologies for Sustainable Development: Recent Advances, Classification, and Environmental Monitoring
by: Abel Inobeme, et al.
Published: (2024-12-01) -
Indoor Air Quality Assessment Through IoT Sensor Technology: A Montreal–Qatar Case Study
by: Zhihan Wang, et al.
Published: (2025-05-01)