概括
这项研究证明了使用萨格纳克干扰仪循环传感器和深度神经网络 (DNN) 进行长周边监测的精确多事件定位. 机器学习方法精确地识别光纤传感系统中的事件位置.
科学领域:
- 有光学传感器的感应器.
- 光纤传感器 光纤传感器
- 机器学习 机器学习
背景情况:
- 精确的事件定位对于管道监控和入侵检测至关重要.
- 现有的方法在长距离的多事件本地化方面面临挑战.
研究的目的:
- 通过实验来证明用于长周边监控的多事件本地化.
- 应用机器学习技术,特别是深度神经网络 (DNN),用于精确的事件位置.
- 为了应对在光纤传感器中区分和定位多个同时发生的事件的挑战.
主要方法:
- 使用了萨格纳克干扰仪循环传感器,拥有超过100公里的单模光纤.
- 处理多事件本地化作为250个光纤段的多标签多分类问题.
- 开发了一个基于模拟数据训练的深度神经网络 (DNN) 模型,使用离散等号变换 (DCT) 降低了复杂性.
主要成果:
- 在模拟中实现了99%的准确性,用于在一个段误差内定位单个事件.
- 证明了95%的准确性定位两个事件中的一个和78%的准确性定位两个事件在一个段误差内.
- 实验结果验证了模拟结果,证实了该模型在高精度事件定位方面的有效性.
结论:
- 提出的基于DNN的方法有效地实现了在长周边光学传感中精确的多事件定位.
- 该方法对管道监控和入侵检测等应用具有重大前景.
- 未来的工作可以扩展模型,以检测和定位两个以上的同时发生的事件.
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