Overall
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received December 9, 2024
Revised March 21, 2025
Accepted April 3, 2025
Available online August 25, 2025
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Most Cited
Validation of Subway Indoor Air Quality (IAQ) Data Using Memory‑Augmented Autoencoders with Learned Normal Prototypes
https://doi.org/
Abstract
Indoor air quality (IAQ) monitoring in subway stations depends on sensors prone to failures due to confined spaces, cyberattacks,
and prolonged use. Soft sensor validation frameworks using statistical or machine learning models can detect,
diagnose, and reconstruct faulty data but struggle with complex fault patterns. This study introduces a memory-augmented
autoencoder-based framework for reliable IAQ sensor validation, leveraging memorized normal prototypes. To the best of
our knowledge, this is the first validation method that utilizes normal prototypes for reconciling corrupted measurements.
Tested on real IAQ data from Seoul Metro's C-station, the method achieved a 97.03% detection rate, a 4.33% false alarm
rate, and demonstrated potential for 10.25% energy savings while maintaining healthy IAQ.
