This project leverages machine learning to classify six gases using a sensor array dataset spanning 36 months, tackling sensor drift. By analyzing steady-state and dynamic features from 16 sensors, it improves classification accuracy and resilience over time, advancing drift compensation and reliable gas discrimination in varying conditions.
☆10Jun 16, 2025Updated 10 months ago
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