Name
Abstract | ||
|---|---|---|
In this work, we present a method for detecting anomalous chemical sensors using contrastive learning-based framework. In many practical systems, an array of multiple chemical sensors are used. Some of the sensors may malfunction due to sensor drift and chemical poisoning. In standard contrastive learning, the aim is to learn representations that will have maximum agreement among data samples of the same concept while having a minimal agreement with data samples from other concepts. In this work, we adapt standard contrastive learning to learning useful representations for out-of-distribution sample detection. Furthermore, we compare the proposed framework with the cosine similarity measure and a novel similarity measure based on the ℓ
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norm. Our experimental results show that our approach achieves higher AUC scores (93.6%) than baseline methods (90.1%). |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICASSP43922.2022.9746646 | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Keywords | DocType | ISSN |
anomaly detection,deep learning,contrastive learning,sensor signal processing,chemical sensors | Conference | 1520-6149 |
ISBN | Citations | PageRank |
978-1-6654-0541-6 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diaa Badawi | 1 | 0 | 0.34 |
| Ishaan Bassi | 2 | 0 | 0.34 |
| Sule Ozev | 3 | 0 | 0.34 |
| A. Enis Çetin | 4 | 871 | 118.56 |