标题： 基于极端轻量模型和跨域蒸馏的实时分布式光纤振动识别 显示英文标题

标题： Real-Time Distributed Optical Fiber Vibration Recognition via Extreme Lightweight Model and Cross-Domain Distillation

摘要： 分布式光纤振动传感（DVS）系统为大规模监测和入侵事件识别提供了一个有前景的解决方案。 然而，其实际部署仍受到两个主要挑战的阻碍：动态条件下的识别精度下降，以及大规模传感数据实时处理的计算瓶颈。 本文通过一种FPGA加速的极端轻量级模型以及一种新提出的知识蒸馏框架，提出了针对这些挑战的新解决方案。 所提出的三层深度可分离卷积网络仅包含4141个参数，这是该领域目前最紧凑的架构，并实现了每个样本在0.256秒内覆盖12.5米光纤的最大处理速度0.019毫秒。这种性能对应于对长达168.68公里的传感光纤实现实时处理能力。 为了在变化的环境中提高泛化能力，这里使用了由物理先验引导的跨域蒸馏框架，以将频域洞察嵌入到时域模型中。 这使得可以在不增加复杂度的情况下进行时频表示学习，并在未见过的环境条件下将识别准确率从51.93%提升至95.72%。 所提出的方法提供了包括将可解释信号处理技术与深度学习结合的框架，以及DVS系统中实时处理和边缘计算的参考架构，以及更广泛的分布式光纤传感（DOFS）领域的关键进展。 它缓解了感知范围与实时能力之间的权衡，弥合了理论能力和实际部署需求之间的差距。 此外，这项工作揭示了构建更高效、稳健和可解释的人工智能系统的新方向，用于DOFS技术。 显示英文摘要

摘要： Distributed optical fiber vibration sensing (DVS) systems offer a promising solution for large-scale monitoring and intrusion event recognition. However, their practical deployment remains hindered by two major challenges: degradation of recognition accuracy in dynamic conditions, and the computational bottleneck of real-time processing for mass sensing data. This paper presents a new solution to these challenges, through a FPGA-accelerated extreme lightweight model along with a newly proposed knowledge distillation framework. The proposed three-layer depthwise separable convolution network contains only 4141 parameters, which is the most compact architecture in this field to date, and achieves a maximum processing speed of 0.019 ms for each sample covering a 12.5 m fiber length over 0.256 s. This performance corresponds to real-time processing capabilities for sensing fibers extending up to 168.68 km. To improve generalizability under changing environments, the proposed cross-domain distillation framework guided by physical priors is used here to embed frequency-domain insights into the time-domain model. This allows for time-frequency representation learning without increasing complexity and boosts recognition accuracy from 51.93% to 95.72% under unseen environmental conditions. The proposed methodology provides key advancements including a framework combining interpretable signal processing technique with deep learning and a reference architecture for real-time processing and edge-computing in DVS systems, and more general distributed optical fiber sensing (DOFS) area. It mitigates the trade-off between sensing range and real-time capability, bridging the gap between theoretical capabilities and practical deployment requirements. Furthermore, this work reveals a new direction for building more efficient, robust and explainable artificial intelligence systems for DOFS technologies.