Variational Autoencoder for Calibration: A New Approach
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Abstract (arXiv)
In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent space as a calibration output. We discuss this new approach and show a proof-of-concept using an existing multi-sensor gas dataset. We show the performance of the proposed calibration VAE and found that it was capable of performing as calibration model while performing as an autoencoder simultaneously. Additionally, these models have shown that they are capable of creating statistically similar outputs from both the calibration output as well as the reconstruction output to their respective truth data. We then discuss the methods of future testing and planned expansion of this work.
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References · 5
- 2020Dynamical Variational Autoencoders: A Comprehensive Reviewcited by 127
- 2017A Deep Learning Approach for Blind Drift Calibration of Sensor Networkscited by 1
- 2023A Propagation-model Empowered Solution for Blind-Calibration of Sensorscited by 1
- 2025Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Pointcited by 1
- 2025Statistical Study of Sensor Data and Investigation of ML-based Calibration Algorithms for Inexpensive Sensor Modules: Experiments from Cape Pointcited by 1
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