: 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.
Research from sources like the UCI Machine Learning Repository and Nature highlights several advanced features used to combat drift: Gas-Lab - Drift
: Modern systems extract both steady-state and transient features from the sensor's response. The relationship between these two can be used to adjust drifted readings back to a "month 1" baseline. : This machine learning approach treats "clean" initial
: A signal processing technique that removes components of the sensor response that are not correlated with the target gas, effectively filtering out "drift noise". The relationship between these two can be used
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.