About this document
Oe 28 3 2925 by soniafranco is a document available to read on EtoBox.
This research article introduces a novel framework for distributed acoustic sensing (DAS) using a convolutional long short-term memory (ConvLSTM) network to detect and classify intrusion threats in high-speed railway environments. The framework effectively combines spatial feature extraction and temporal analysis, achieving an intrusion detection rate of 85.6% with a low false alarm rate of 8.0% in real field tests. The study highlights the importance of high detection accuracy and rapid response times in e
- Author
- soniafranco
- Language
- EN