P34 - Optimisation of Convolutional Neural Networks for MOS Gas Sensors
- Event
- 17. Dresdner Sensor-Symposium 2024
2024-11-25 - 2024-11-27
Dresden - Band
- Poster
- Chapter
- 14. Smart Sensors/Künstliche Intelligenz in der Sensorik
- Author(s)
- J. Petry, D. Schu, T. Mertin, D. Arendes - Universität des Saarlandes, Saarbrücken/D
- Pages
- 193 - 198
- DOI
- 10.5162/17dss2024/P34
- ISBN
- 978-3-910600-04-1
- Price
- free
Abstract
Metal oxide semiconductor (MOS) gas sensors are widely used in applications fields ranging from indoor air quality (IAQ) monitoring and control [1] to fire detection [2]. These sensors offer a low-cost in situ alternative to gas chromatography [3]. Temperature-cycled operating (TCO) modes are proven to enhance selectivity as well as sensitivity [4]. However, MOS sensors still require calibration against a known reference, which is often provided by a gas mixing apparatus [5]. Using the measured conductivity of the sensors exposed to a unique gas mixture (UGM), machine learning (ML) algorithms can be trained to classify or quantify gases.