Gas-lab - Drift -

: A dynamic method that identifies samples away from the standard classification plane to better represent drift variations in real-time.

In the context of gas sensing and electronic noses, refers to the gradual, unpredictable shift in sensor responses over time, often caused by sensor aging, contamination, or environmental changes. Gas-Lab - Drift

: This machine learning approach treats "clean" initial data as a source domain and "drifted" data as a target domain. It uses techniques like Knowledge Distillation (KD) or Wasserstein distance to align these domains so the model remains accurate. : A dynamic method that identifies samples away