|Table of Contents|

Protection system for communication line based on distributed interferometric fiber optic sensor network

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年06期
Page:
757-
Research Field:
Publishing date:

Info

Title:
Protection system for communication line based on distributed interferometric fiber optic sensor network
Author(s):
Hu Feng1Ma Chunxia2Cui Yian3Shi Guangshun3
1.School of Telecommunications,Nanjing College of Information Technology,Nanjing 210023,China; 2.Tianjin Engineering Teaching & Training Center,Polytechnic University of Tianjin,Tianjin 300087,China; 3.College of Computer and Control Engineering,Nan
Keywords:
optical fiber sensing network digital signal processing artificial neural network communication line security
PACS:
TN913.3
DOI:
-
Abstract:
A new communication line protection system has been proposed,which is based on the distributed optical fiber and artificial neural network discrimination.The system uses optical fiber sensors to collect the soil vibration signal around communication line.Raw signals are processed via several kind of digital signal processing methods.A hybrid classification system is applied to identify the existence of destructive behavior.An accurate mutual correlation method is designed based on Mach-Zehnder interference principle to locate the position of vibration signals.Wavelet shrinkage and Hilbert transformation method are applied to filter noise and segment the interest signal section.A two level classifier based on Support Vector Machine(SVM)and Back Propagation(BP)neural network is designed to identify the type of dangerous behavior.The system has been evaluated under a real application environment.The location deviation is less than 100 m,and the recognition accuracy rate for seven types of dangerous behavior comes to 94.35%.The test results prove the efficiency and precision of the system.

References:

[1] 曲志刚,封皓,靳世久,等.基于支持向量机的油气管道安全监测信号识别方法[J].天津大学学报,2009,42(5):465-469. Qu Zhigang,Feng Hao,Jin Shijiu,et al.A SVM-based recognition method for safety monitoring signals of oil and gas pipeline[J].Journal of Tianjin University,2009,42(5):465-469.
[2]王立,王耀辉,肖昕璐,等.基于神经网络的长距离油气管道安全预警系统[J].高技术通讯,2008,18(7):719-724. Wang Li,Wang Yaohui,Xiao Xinlu,et al.An artificial neural networks based long-distance safety monitoring system for buried oil pipelines[J].High Technology Letters,2008,18(7):719-724.
[3]刘琨,何畅,刘铁根,等.一种用于光纤周界安防系统的端点检测方法[J].光电子·激光,2014,25(11):2136-2140. Liu Kun,He Chang,Liu Tiegen,et al.An endpoint detection method for fiber perimeter security system[J].Journal of Optoelectronics·Laser,2014,25(11):2136-2140.
[4]Stephane Mallat.信号处理的小波导引:稀疏方法[M].杨力华,译.北京:机械工业出版社,2003.
[5]Yen G G,Lin K C.Wavelet packet feature extraction for vibration monitoring[J].IEEE Transactions on Industrial Electronics,2000,47(3):650-667.
[6]Percival D B,Walden A T.Wavelet methods for time series analysis[M].London:Cambridge University Press,2006:56-150.
[7]余东平,张剑峰,王聪,等.基于新阈值函数的小波阈值去噪算法[J].计算机应用,2014,34(12):1499-1502. Yu Dongping,Zhang Jianfeng,Wang Cong,et al.Wavelet-based denoising by a new thresholiding function[J].Journal of Computer Applications,2014,34(5):1499-1502.
[8]李洋,景新幸,杨海燕.基于改进小波阈值和EMD的语音去噪方法[J].计算机工程与设计,2014,35(7):2462-2466. Li Yang,Jing Xinxing,Yang Haiyan.Speech denoising method based on empirical mode decomposition and improved wavelet threshold[J].Computer Engineering and Design,2014,35(7):2462-2466.
[9]Zhang Guoliang,Song Zhanjiang.Comparison of different implementations of MFCC[J].Journal of Computer Science and Technology,2001,16(6):582-589.
[10]Hagan M T,Demuth H B,Beale M H.Neural network design[M].Boston:Pws Pub,1996.
[11]汪海燕,黎建辉,杨风雷.支持向量机理论及算法研究综述[J].计算机应用研究,2014,31(5):1281-1286. Wang Haiyan,Li Jianhui,Yang Fenglei.An overview on theory and algorithm of support vector machines[J].Application Research of Computers,2014,31(5):1281-1286.

Memo

Memo:
-
Last Update: 2014-12-31