| Autor: |
Luong HV; AP Sensing GmbH, Herrenberger Str. 130, 71034 Böblingen, Germany., Deligiannis N; Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium.; Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, B-3001 Leuven, Belgium., Wilhelm R; AP Sensing GmbH, Herrenberger Str. 130, 71034 Böblingen, Germany., Drapp B; AP Sensing GmbH, Herrenberger Str. 130, 71034 Böblingen, Germany. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Dec 21; Vol. 24 (1). Date of Electronic Publication: 2023 Dec 21. |
| DOI: |
10.3390/s24010049 |
| Abstrakt: |
This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processing methods for DAS data and (ii) the implementation of a neural network model based on meta-learning to learn a representation of the processed data. In the context of urban infrastructure monitoring, we develop a few-shot classification framework that classifies query samples with only a limited number of support samples. The model consists of an embedding network trained on a meta dataset for feature extraction and is followed by a classifier for performing few-shot classification. This research thoroughly explores three types of data pre-processing, that is, decomposed phase, power spectral density, and frequency energy band, as inputs to the neural network. Experimental results show the efficient learning capabilities of the embedding model when working with various pre-processed data, offering a range of pre-processing options. Furthermore, the results demonstrate outstanding few-shot classification performance across a large number of event classes, highlighting the framework's potential for urban infrastructure monitoring applications. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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