Spatial resolution improvement method of RDTS assisted by small-scale thermal region length recognition model

IF 2.6 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Optical Fiber Technology Pub Date : 2024-09-13 DOI:10.1016/j.yofte.2024.103967

Honghui Wang , Tong Liu , Xiang Wang , Xike Yang , Yuhang Wang , Yiru Wang , Shangkun Zeng , Jizhou Ren

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引用次数: 0

Abstract

The Total Variation Deconvolution (TVD) algorithm plays an important role in signal reconstruction, however, when it is used to improve the spatial resolution of Raman Distributed Temperature Sensor (RDTS), there are certain challenges in parameter settings. This paper proposes to use Fully-Connected Neural Network to identify the length of small-scale thermal regions(SSTR), and based on the recognition results to set the TVD parameters automatically. We constructed training sets based on the periodic changes of SSTR signals in RDTS (which we call Thermal Region Response Modes, TRRM), to verify performance, we conducted comparative experiments between models obtained from a training set containing 100 types of TRRMs and 25 types of TRRMs, the Macro-F1 value of the former one is 0.2749 higher, reaching 0.9087, performed well in SSTR length recognition tasks. the traditional TVD assisted by this model can increase the spatial resolution of RDTS from 1.6 m to 0.4 m without manual intervention, which complements the lack of automation in applications of TVD and has practical value.

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小尺度热区长度识别模型辅助 RDTS 空间分辨率改进方法

总变异解卷积（TVD）算法在信号重建中发挥着重要作用，但在用于提高拉曼分布式温度传感器（RDTS）的空间分辨率时，参数设置存在一定的挑战。本文提出利用全连接神经网络识别小尺度热区（SSTR）的长度，并根据识别结果自动设置 TVD 参数。我们根据 RDTS 中 SSTR 信号的周期性变化构建了训练集（我们称之为热区响应模式，TRRM），为了验证性能，我们对包含 100 种 TRRM 和 25 种 TRRM 的训练集所得到的模型进行了对比实验，前者的 Macro-F1 值比后者高 0.2749，达到了0.9087，在SSTR长度识别任务中表现良好。该模型辅助的传统TVD可以将RDTS的空间分辨率从1.6米提高到0.4米，无需人工干预，补充了TVD应用中自动化程度不足的缺陷，具有实用价值。

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来源期刊

Optical Fiber Technology 工程技术-电信学

CiteScore

4.80

自引率

11.10%

发文量

327

审稿时长

63 days

期刊介绍： Innovations in optical fiber technology are revolutionizing world communications. Newly developed fiber amplifiers allow for direct transmission of high-speed signals over transcontinental distances without the need for electronic regeneration. Optical fibers find new applications in data processing. The impact of fiber materials, devices, and systems on communications in the coming decades will create an abundance of primary literature and the need for up-to-date reviews. Optical Fiber Technology: Materials, Devices, and Systems is a new cutting-edge journal designed to fill a need in this rapidly evolving field for speedy publication of regular length papers. Both theoretical and experimental papers on fiber materials, devices, and system performance evaluation and measurements are eligible, with emphasis on practical applications.