• Corpus ID: 212725258

Using context to make gas classifiers robust to sensor drift

@article{Warner2020UsingCT,
  title={Using context to make gas classifiers robust to sensor drift},
  author={J. Warner and Anand Devaraj and Risto Miikkulainen},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.07292}
}
The interaction of a gas particle with a metal-oxide based gas sensor changes the sensor irreversibly. The compounded changes, referred to as sensor drift, are unstable, but adaptive algorithms can sustain the accuracy of odor sensor systems. This paper shows how such a system can be defined without additional data acquisition by transfering knowledge from one time window to a subsequent one after drift has occurred. A context-based neural network model is used to form a latent representation… 

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