计算机科学
入侵检测系统
电信
分布式声传感
事件(粒子物理)
深度学习
入侵
计算机网络
人工智能
光纤
地质学
物理
光纤传感器
地球化学
量子力学
作者
Shaobo Han,Ming-Fang Huang,Tingfeng Li,Jian Fang,Zhuocheng Jiang,Ting Wang
标识
DOI:10.1109/jlt.2024.3401244
摘要
Our study introduces two pioneering applications leveraging Distributed Fiber Optic Sensing (DFOS) and Machine Learning (ML) technologies. These innovations offer substantial benefits for fortifying telecom infrastructures and public safety. By harnessing existing telecom cables, our solutions excel in perimeter intrusion detection via buried cables and impulsive event classification through aerial cables. To achieve comprehensive intrusion detection, we introduce a label encoding strategy for multitask learning and rigorously evaluate the generalization performance of the proposed approach across various domain shifts. For accurate recognition of impulsive acoustic events, we compare several standard choices of representations for raw waveform data and neural network architectures, including convolutional neural networks (ConvNets) and vision transformers (ViT). We also study the effectiveness of the build-in inductive biases under both high- and low-fidelity sensing conditions and varying amounts of labeled training data. All computations are executed locally through edge computing, ensuring real-time detection capabilities. Moreover, our proposed system can be seamlessly integrated with cameras for video analytics, significantly enhancing overall situation awareness of the surrounding environment.
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