Gas-lab - Drift 【Recommended】

: 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 from the standard classification plane to better represent drift variations in real-time. 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