Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197921" target="_blank" >RIV/00216305:26220/26:0197921 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41597-025-05088-4" target="_blank" >https://www.nature.com/articles/s41597-025-05088-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41597-025-05088-4" target="_blank" >10.1038/s41597-025-05088-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

Popis výsledku v původním jazyce

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to detect acoustic signals over long distances, offering high-resolution data critical for applications such as seismic monitoring, structural health monitoring, and security. A significant challenge in DAS systems is the accurate classification of detected events, which is crucial for their reliability. Traditional signal processing methods often struggle with the high-dimensional, noisy data produced by DAS systems, making advanced machine learning techniques essential for improved event classification. However, the lack of large, high-quality datasets has hindered progress. In this study, we present a comprehensive labeled dataset of DAS measurements collected around a university campus, featuring events such as walking, running, and vehicular movement, as well as potential security threats. This dataset provides a valuable resource for developing and validating machine learning models, enabling more accurate and automated event classification. The quality of the dataset is demonstrated through the successful training of a Convolutional Neural Network (CNN).

Název v anglickém jazyce

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems

Popis výsledku anglicky

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to detect acoustic signals over long distances, offering high-resolution data critical for applications such as seismic monitoring, structural health monitoring, and security. A significant challenge in DAS systems is the accurate classification of detected events, which is crucial for their reliability. Traditional signal processing methods often struggle with the high-dimensional, noisy data produced by DAS systems, making advanced machine learning techniques essential for improved event classification. However, the lack of large, high-quality datasets has hindered progress. In this study, we present a comprehensive labeled dataset of DAS measurements collected around a university campus, featuring events such as walking, running, and vehicular movement, as well as potential security threats. This dataset provides a valuable resource for developing and validating machine learning models, enabling more accurate and automated event classification. The quality of the dataset is demonstrated through the successful training of a Convolutional Neural Network (CNN).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01030121" target="_blank" >VK01030121: Distribuovaný optický-vláknový senzorický systém středního dosahu pro monitorování akustických vibrací a teploty na kritických infrastrukturách</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Data

ISSN

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e-ISSN

2052-4463

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1-8

Kód UT WoS článku

001488255400004

EID výsledku v databázi Scopus

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