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 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
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. : Modern systems extract both steady-state and transient
: This framework, discussed in research on arXiv , integrates unique "private" features from different sensors to improve recognition accuracy across long-term data batches.
This is a perfect use-case for a Makefile – see https://github.com/brunns/cheatsheets/blob/master/Makefile for an example of the kind of thing I mean.
Also, don’t forget the –reference-doc flag if you want to automate some of the styling .
For a moment there I thought “Pandoc? Org-mode exports directly to Word, after all, with a decent template feature to boot.”
Will this work if I have figures and equations?