: 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 framework, discussed in research on arXiv , integrates unique "private" features from different sensors to improve recognition accuracy across long-term data batches. Gas-Lab - Drift
Research from sources like the UCI Machine Learning Repository and Nature highlights several advanced features used to combat drift: : Modern systems extract both steady-state and transient
: A dynamic method that identifies samples away from the standard classification plane to better represent drift variations in real-time. Research from sources like the UCI Machine Learning
: 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.