[1]胡 峰,马春侠,崔毅安,等.基于分布式干涉光纤传感网络的通信线路防护系统[J].南京理工大学学报(自然科学版),2014,38(06):757.
 Hu Feng,Ma Chunxia,Cui Yian,et al.Protection system for communication line based on distributed interferometric fiber optic sensor network[J].Journal of Nanjing University of Science and Technology,2014,38(06):757.
点击复制

基于分布式干涉光纤传感网络的通信线路防护系统
分享到:

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

卷:
38卷
期数:
2014年06期
页码:
757
栏目:
出版日期:
2014-12-31

文章信息/Info

Title:
Protection system for communication line based on distributed interferometric fiber optic sensor network
作者:
胡 峰1马春侠2崔毅安3史广顺3
1.南京信息职业技术学院 通信学院,江苏 南京 210023; 2.天津工业大学 工程教学实训中心,天津 300087; 3.南开大学 计算机与控制工程学院,天津 300071
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
关键词:
光纤传感网络 数字信号处理 人工神经网络 通信线路安全
Keywords:
optical fiber sensing network digital signal processing artificial neural network communication line security
分类号:
TN913.3
摘要:
提出了一种基于分布式光纤和人工神经网络判别的通信线路防护系统。该系统利用光纤传感器收集通信线路周围的振动信号,运用数字信号处理的方法对原始信号进行处理,通过神经网络判断是否存在针对通信线路的破坏性行为并判别破坏行为的类型,实现对通信线路的防护。系统在定位阶段,基于Mach-Zehnder干涉原理,运用互相关的方法进行实时定位。在数据处理阶段对信号进行抑噪处理,有利于进一步的定位与事件识别工作。在识别阶段使用支持向量机(Support vector machine,SVM)和反向传播(Back propagation,BP)神经网络方法构建了层次化分类器。实验结果表明:信号定位精度达到100 m,系统对七类破坏行为的识别率达到94.35%。
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.

参考文献/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.

相似文献/References:

[1]郭毓,马勤弟,许春山,等.搬运机器人伺服系统的研究[J].南京理工大学学报(自然科学版),2001,(03):332.
 GuoYu MaQindi XuChunshan HuBin MaoJian.A Study on Servo System of Transport Robot[J].Journal of Nanjing University of Science and Technology,2001,(06):332.
[2]王利平,陈钱,顾国华,等.基于DSP+FPGA的红外图像锐化算法的实现[J].南京理工大学学报(自然科学版),2006,(06):764.
 WANG Li-ping,CHEN Qian,GU Guo-hua,et al.Infrared Image Sharpening Algorithm Based on DSP+FPGA[J].Journal of Nanjing University of Science and Technology,2006,(06):764.
[3]刘彦华,杨文铂,崔明月.基于时域展宽的光电混合模数转换系统关键技术研究[J].南京理工大学学报(自然科学版),2018,42(05):518.[doi:10.14177/j.cnki.32-1397n.2018.42.05.002]
 Liu Yanhua,Yang Wenbo,Cui Mingyue.Key technology research of photoelectric hybrid module conversionsystem based on time-domain broadening[J].Journal of Nanjing University of Science and Technology,2018,42(06):518.[doi:10.14177/j.cnki.32-1397n.2018.42.05.002]

备注/Memo

备注/Memo:
收稿日期:2014-11-07 修回日期:2014-12-12
基金项目:天津市科技支撑计划项目(11ZCKFGX01800)
作者简介:胡峰(1982-),男,讲师,主要研究方向:通信网络、网络安全,E-mail:xyd_hufeng@126.com。
引文格式:胡峰,马春侠,崔毅安,等.基于分布式干涉光纤传感网络的通信线路防护系统[J].南京理工大学学报,2014,38(6):757-762.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2014-12-31