Practical multi-class event classification approach for distributed vibration sensing using deep dual path network
Practical multi-class event classification approach for distributed vibration sensing using deep dual path network
复制标题
使用深度双路径网络进行分布式振动传感的实用多类事件分类方法
DOI：
10.1364/oe.27.023682
复制
发表时间：
2019-08-19
期刊：
影响因子：
3.8
通讯作者：
Cai, Haiwen
中科院分区：
文献类型：
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作者：
Wang, Zhaoyong;Zheng, Hanrong;Cai, Haiwen
在边界安全、铁路安全监测、管道监控等分布式振动传感技术的长距离应用领域中，受环境噪声和非平稳干扰信号的影响，多类事件分类是一个巨大的挑战。利用DVS信号的多维信息，特别是空间域信息，建立了空间时频谱数据集。新的数据集和一个高参数效率的网络，所提出的方案具有良好的可靠性和鲁棒性。在实际的铁路安全监测现场试验中验证了其可行性，作为概念验证。七种真实生活中的干扰，其f1-分数都达到了97%的测试。该方法的性能进行了充分的评估和讨论。该方法可用于提高DVS在实际应用中的性能。(C)根据OSA开放获取出版协议的条款，2019年美国光学学会
Influenced by severe ambient noises and nonstationary disturbance signals, multi-class event classification is an enormous challenge in several long-haul application fields of distributed vibration sensing technology (DVS), including perimeter security, railway safety monitoring, pipeline surveillance, etc. In this paper, a deep dual path network is introduced into solving this problem with high learning capacity. The spatial time-frequency spectrum datasets are built by utilizing the multidimensional information of DVS signal, especially the spatial domain information. With the novel datasets and a high-parameter-efficiency network, the proposed scheme presents good reliability and robustness. The feasibility is verified in an actual railway safety monitoring field test, as a proof-of-concept. Seven types of real-life disturbances were implemented and their f1-scores all reached up to 97% in the test. The performance of this proposed approach is fully evaluated and discussed. The presented approach can be employed to improve the performance of DVS in actual applications. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement