E-mail Alert
RSS
-
摘要:
分布式光纤声波传感(DAS)技术通过接收相干瑞利散射光的相位信息来探测声波或振动信号，具有灵敏度高、动态范围广等特性，可利用线性定量测量实现对信号的高保真还原。随着实际应用的需求不断提高，光纤入侵检测领域对事件的定位和识别提出了更高的要求，表现为对入侵事件的准确分类，因此将分布式光纤声波传感技术与模式识别(PR)技术相结合是目前研究的热门，有利于推动分布式光纤传感技术的应用发展。本文总结了近年来在分布式光纤入侵检测的模式识别技术中所应用的特征提取和分类算法的研究进展，回顾了几种实现入侵事件信号识别的特征提取方法及其在不同应用场合面临的特征选择难点，同时对特定事件识别算法的优劣进行分析归纳。
Abstract:Distributed acoustic sensing (DAS) technology can detect acoustic or vibration signals with high sensitivity and wide dynamic range by receiving the phase information from coherent Rayleigh scattered light. Linear quantization is used to measure high fidelity restoration of the signals. With the increasing demand of practical applications, the optical fiber intrusion detection field has put forward higher requirements for event location and identification, which is manifested as the accurate classification of intrusion events. Therefore, the combination of distributed acoustic sensing and pattern recognition (PR) technology is a hot research topic at present. This is beneficial to promote the application and development of distributed optical fiber sensing technology. The research progress of the pattern recognition technology applied to distributed optical fiber intrusion detection in recent years is summarized in this paper, which can be used for feature extraction and classification algorithm research progress. In this paper, several feature extraction methods for realizing intrusion event signal recognition and their feature selection difficulties in different application situations are reviewed. Meanwhile, the advantages and disadvantages of specific event recognition algorithm are analyzed and summarized.
-
Overview: Developed on the basis of phase-sensitive optical time domain reflectrometer(Φ-OTDR), distributed acoustic sensing(DAS) is a new type of distributed optical fiber sensing technology. It is a hot topic that how to accurately distinguish the type of intrusion events from complex signals, which attachs greater importance to the research on the pattern recognition technology based on DAS.
Intrusion signal recognition mainly includes two parts, feature extraction and classification algorithm. This paper summarizes the currently widely used feature extraction methods. Generally speaking, the time-domain features are simple, intuitive, and fast in response, but they are susceptible to noise. The frequency-domain features can obtain the inherent spectrum characteristics of the signal, but cannot reflect the frequency changes of the signal at every moment. The time-frequency domain features can express the time-domain and frequency-domain information of the signal, and the extracted feature information is also more accurate.
Classification algorithms include two categories, unsupervised learning and supervised learning. Supervised learning need to collect a large amount of data for training and verification. Therefore, supervised learning algorithms are mostly used in the fiber intrusion detection applications. Support vector machines (SVM) and BP neural networks are relatively common models for classification. In recent years, with the development of deep learning technology, building deep neural network models is very helpful for classification recognition. Therefore, models such as convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN) are used in the field of distributed optical fiber intrusion signal recognition, which have achieved great performance.
In summary, choosing the proper feature extraction method and classification algorithm will greatly enhance the accuracy of intrusion signal recognition. Facing the increasing actual demands, the pattern recognition technology based on the DAS system will definitely play a more important role and fulfill its potential in the future.
-
-
图 3 暴雨期间攀爬事件监测信号。
Figure 3. Signal representing a climb during torrential rain as detected. (a) Time domain representations; (b) LC vs. block number[12]
图 8 两种窗函数处理4种入侵事件的STFT图。(a)，(c)，(e)，(g) 敲击、摇晃、刮风、下雨经过汉宁窗处理后的时频图；(b)，(d)，(f)，(h) 敲击、摇晃、刮风、下雨经过凯塞窗处理后的时频图[27]
Figure 8. STFT time-frequency diagrams of two kinds of window functions for processing four intrusion events. (a), (c), (e), (g) Time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Hanning window; (b), (d), (f), (h) time-frequency diagrams of knocking, shaking, winding, and raining signals after passing through the Kaiser window[27]
图 10 4种入侵信号及其峰值特征向量。(a) 爬网; (b) 敲击; (c) 爬网特征向量; (d) 敲击特征向量; (e) 晃动; (f) 切割; (g) 晃动特征向量; (h) 切割特征向量
Figure 10. Signals and their kurtosis eigenvectors of four cases. (a) Climbing signal; (b) Knocking signal; (c) Eigenvectors of climbing; (d) Eigenvectors of knocking; (e) Waggling signal; (f) Cutting signal; (g) Eigenvectors of waggling; (h) Eigenvectors of cutting
图 11 多尺度分解树。(a) 小波分解；(b) 小波包分解
Figure 11. Multi-scale decomposition tree. (a) Wavelet decomposition; (b) Wavelet packet decomposition
表 1 DAS模式识别技术发展历程
Table 1. The development of DAS pattern recognition technology number
Time Researchers Feature extraction Classification algorithm Recognition rate/% 1 IEEE, 2009 Qi, et al. FFT+PSD PCA+SVM 88.9 2 IEEE, 2010 Mahmoud, et al. LC ANN 3 APS, 2014 Wu, et al SSA BP ＞90 4 ACPC, 2015 Cao, et al FFT SVM 92.62 5 JLT, 2015 Wu, et al WD BP 89.19 6 JLT, 2015 Liu, et al EMD RBF 85.75 7 Sensors, 2015 Sun, et al MFE RVM+GPU 97.8 8 JLT, 2016 Tejedor, et al STFT GMM ＞55 9 PS, 2017 Wu, et al WPD ANN 94.4 10 ISOP, 2017 Aktas.M, et al. STFT 2-D CNN ＞93 11 ICOFS, 2018 Shiloh, et al. RGB GAN 94 12 JLT, 2019 Wei, et al CFAR SCN 94.67 13 JLT, 2019 Wu, et al WPD 1-D CNN+SVM 96.59 14 OE, 2019 Wang, et al RGB DPN+GPU 97 15 MOTL, 2020 Chen, et al STE+ZCR+MFCC ALSTM 94.3 16 OE, 2020 Li, et al STW ConvLSTM 85.6 
下载: 导出CSV
-
[1] Juarez J C, Maier E W, Choi K N, et al. Distributed fiber-optic intrusion sensor system[J]. J Light Technol, 2005, 23(6): 2081-2087. doi: 10.1109/JLT.2005.849924
[2] Pan Z Q, Liang K Z, Ye Q, et al. Phase-sensitive OTDR system based on digital coherent detection[J]. Proc SPIE, 2011, 8311: 83110S. doi: 10.1117/12.905657
[3] Juarez J C, Taylor H F. Field test of a distributed fiber-optic intrusion sensor system for long perimeters[J]. Appl Opt, 2007, 46(11): 1968-1971. doi: 10.1364/AO.46.001968
[4] Lindsey N J, Martin E R, Dreger D S, et al. Fiber‐optic network observations of earthquake wavefields[J]. Geophys Res Lett, 2017, 44(23): 11792-11799. doi: 10.1002/2017GL075722
[5] Cedilnik G, Hunt R, Lees G. Advances in train and rail monitoring with DAS[C]//Proceedings of the 26th International Conference on Optical Fiber Sensors, 2018: ThE35.
[6] Wu H J, Chen J P, Liu X R, et al. One-dimensional CNN-based intelligent recognition of vibrations in pipeline monitoring with DAS[J]. J Light Technol, 2019, 37(17): 4359-4366. doi: 10.1109/JLT.2019.2923839
[7] Johannessen K, Drakeley B, Farhadiroushan M. Distributed acoustic sensing-a new way of listening to your well/reservoir[C]//SPE Intelligent Energy International, Utrecht, the Netherlands, 2012: 149602.
[8] Bao X Y, Zhou D P, Baker C, et al. Recent development in the distributed fiber optic acoustic and ultrasonic detection[J]. J Light Technol, 2017, 35(16): 3256-3267. doi: 10.1109/JLT.2016.2612060
[9] Muanenda Y. Recent advances in distributed acoustic sensing based on phase-sensitive optical time domain reflectometry[J]. J Sens, 2018, 2018: 3897873. http://www.researchgate.net/publication/325124962_Recent_Advances_in_Distributed_Acoustic_Sensing_Based_on_Phase-Sensitive_Optical_Time_Domain_Reflectometry
[10] Adeel M, Shang C, Zhu K, et al. Nuisance alarm reduction: using a correlation based algorithm above differential signals in direct detected phase-OTDR systems[J]. Opt Express, 2019, 27(5): 7685-7698. doi: 10.1364/OE.27.007685
[11] 饶云江, 吴敏, 冉曾令, 等. 基于准分布式FBG传感器的光纤入侵报警系统[J]. 传感技术学报, 2007, 20(5): 998-1002. doi: 10.3969/j.issn.1004-1699.2007.05.011
Rao Y J, Wu M, Ran Z L, et al. A fiber-optic intrusion alarm system based on quasi-distributed FBG sensors[J]. Chin J Sens Actuators, 2007, 20(5): 998-1002. doi: 10.3969/j.issn.1004-1699.2007.05.011
[12] Mahmoud S S, Katsifolis J. Elimination of rain-induced nuisance alarms in distributed fiber optic perimeter intrusion detection systems[J]. Proc SPIE, 2009, 7316: 731604. doi: 10.1117/12.818096
[13] 吴红艳, 贾波, 卞庞. 光纤周界安防系统端点检测技术的研究[J]. 仪器仪表学报, 2013, 34(4): 743-748. doi: 10.3969/j.issn.0254-3087.2013.04.004
Wu H Y, Jia B, Bian P. Study on endpoint detection technology based on fiber perimeter security system[J]. Chin J Sci Instrum, 2013, 34(4): 743-748. doi: 10.3969/j.issn.0254-3087.2013.04.004
[14] 王思远, 娄淑琴, 梁生, 等. M-Z干涉仪型光纤分布式扰动传感系统模式识别方法[J]. 红外与激光工程, 2014, 43(8): 2613-2618. doi: 10.3969/j.issn.1007-2276.2014.08.036
Wang S Y, Lou S Q, Liang S, et al. Pattern recognition method of fiber distributed disturbance sensing system based on M-Z interferometer[J]. Infrared Laser Eng, 2014, 43(8): 2613-2618. doi: 10.3969/j.issn.1007-2276.2014.08.036
[15] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201411014.htm
Liu K, He C, Liu T G, et al. An endpoint detection method for fiber perimeter security system[J]. J Opto Laser, 2014, 25(11): 2136-2140. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201411014.htm
[16] 朱程辉, 瞿永中, 王建平. 基于时频特征的光纤周界振动信号识别[J]. 光电工程, 2014, 41(1): 16-22. doi: 10.3969/j.issn.1003-501X.2014.01.004
Zhu C H, Qu Y Z, Wang J P. The vibration signal recognition of optical fiber perimeter based on time-frequency features[J]. Opto-Electron Eng, 2014, 41(1): 16-22. doi: 10.3969/j.issn.1003-501X.2014.01.004
[17] 王建平, 郝钊, 朱程辉. 基于相空间重构的光纤周界信号识别算法研究[J]. 合肥工业大学学报(自然科学版), 2017, 40(5): 643-648. doi: 10.3969/j.issn.1003-5060.2017.05.014
Wang J P, Hao Z, Zhu C H. Research on vibration signal recognition of optical fiber perimeter based on phase space reconstruction[J]. J Hefei Univ Technol (Nat Sci), 2017, 40(5): 643-648. doi: 10.3969/j.issn.1003-5060.2017.05.014
[18] 刘琨, 翁凌锋, 江俊峰, 等. 基于过零率的光纤周界安防系统入侵事件高效识别[J]. 光学学报, 2019, 39(11): 1106002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201911009.htm
Liu K, Weng L F, Jiang J F, et al. Zero-crossing rate based efficient identification of intrusion events in fiber perimeter security systems[J]. Acta Opt Sin, 2019, 39(11): 1106002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201911009.htm
[19] 王照勇, 潘政清, 叶青, 等. 用于光纤围栏入侵告警的频谱分析快速模式识别[J]. 中国激光, 2015, 42(4): 0405010. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201504024.htm
Wang Z Y, Pan Z Q, Ye Q, et al. Fast pattern recognition based on frequency spectrum analysis used for intrusion alarming in optical fiber fence[J]. Chin J Lasers, 2015, 42(4): 0405010. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201504024.htm
[20] Cao C, Fan X Y, Liu Q W, et al. Practical pattern recognition system for distributed optical fiber intrusion monitoring system based on phase-sensitive coherent OTDR[C]//Asia Communications and Photonics Conference 2015, 2015: ASu2A. 145.
[21] 黄翔东, 张皓杰, 刘琨, 等. 基于综合特征的光纤周界安防系统高效入侵事件识别[J]. 物理学报, 2017, 66(12): 124206. doi: 10.7498/aps.66.124206
Huang X D, Zhang H J, Liu K, et al. High-efficiency intrusion recognition by using synthesized features in optical fiber perimeter security system[J]. Acta Phys Sin, 2017, 66(12): 124206. doi: 10.7498/aps.66.124206
[22] 邹东伯, 刘海, 赵亮, 等. 分布式光纤振动传感信号识别的研究[J]. 激光技术, 2016, 40(1): 86-89. https://www.cnki.com.cn/Article/CJFDTOTAL-JGJS201601020.htm
Zou D B, Liu H, Zhao L, et al. Research of signal recognition of distributed optical fiber vibration sensors[J]. Laser Technol, 2016, 40(1): 86-89. https://www.cnki.com.cn/Article/CJFDTOTAL-JGJS201601020.htm
[23] 帅师, 王翦, 吴红艳, 等. 一种分布式光纤传感系统的信号识别方法[J]. 复旦学报(自然科学版), 2018, 57(5): 611-618. https://www.cnki.com.cn/Article/CJFDTOTAL-FDXB201805009.htm
Shuai S, Wang J, Wu H Y, et al. A signal recognition method for distributed optical fiber sensor system[J]. J Fudan Univ (Nat Sci), 2018, 57(5): 611-618. https://www.cnki.com.cn/Article/CJFDTOTAL-FDXB201805009.htm
[24] Tejedor J, Martins H F, Piote D, et al. Toward prevention of pipeline integrity threats using a smart fiber-optic surveillance system[J]. J Light Technol, 2016, 34(19): 4445-4453. doi: 10.1109/JLT.2016.2542981
[25] Tejedor J, Macias-Guarasa J, Martins H F, et al. A novel fiber optic based surveillance system for prevention of pipeline integrity threats[J]. Sensors, 2017, 17(2): 355. doi: 10.3390/s17020355
[26] 李志辰, 刘琨, 江俊峰, 等. 光纤周界安防系统的高准确度事件识别方法[J]. 红外与激光工程, 2018, 47(9): 0922002. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201809024.htm
Li Z C, Liu K, Jiang J F, et al. A high-accuracy event discrimination method in optical fiber perimeter security system[J]. Infrared Laser Eng, 2018, 47(9): 0922002. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201809024.htm
[27] 陈沛超, 游赐天, 丁攀峰. 光纤周界防区入侵事件的模式识别研究[J]. 中国激光, 2019, 46(10): 1006001. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201910033.htm
Chen P C, You C T, Ding P F. Pattern recognition of intrusion events in perimeter defense areas of optical fiber[J]. Chin J Lasers, 2019, 46(10): 1006001. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201910033.htm
[28] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc Math Phys Eng Sci, 1998, 454(1971): 903-995. doi: 10.1098/rspa.1998.0193
[29] Liu K, Tian M, Liu T G, et al. A high-efficiency multiple events discrimination method in optical fiber perimeter security system[J]. J Light Technol, 2015, 33(23): 4885-4890. doi: 10.1109/JLT.2015.2494158
[30] 蒋立辉, 盖井艳, 王维波, 等. 基于总体平均经验模态分解的光纤周界预警系统模式识别方法[J]. 光学学报, 2015, 35(10): 1006002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201510007.htm
Jiang L H, Gai J Y, Wang W B, et al. Ensemble empirical mode decomposition based event classification method for the fiber-optic intrusion monitoring system[J]. Acta Opt Sin, 2015, 35(10): 1006002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201510007.htm
[31] 李静云, 安博文, 陈元林, 等. 基于时频特征的光纤振动模式识别研究[J]. 光通信技术, 2018, 42(7): 55-59. https://www.cnki.com.cn/Article/CJFDTOTAL-GTXS201807017.htm
Li J Y, An B W, Chen Y L, et al. Research on optical fiber vibration pattern recognition based on time-frequency characteristics[J]. Opt Commun Technol, 2018, 42(7): 55-59. https://www.cnki.com.cn/Article/CJFDTOTAL-GTXS201807017.htm
[32] 朱程辉, 朱睿, 王建平, 等. 基于自适应EMD的光纤安防系统入侵信号识别[J]. 传感器与微系统, 2020, 39(4): 26-30. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202004008.htm
Zhu C H, Zhu R, Wang J P, et al. Intrusion signal recognition of optical fiber security & protection system based on adaptive EMD[J]. Transducer and Microsystem Technologies, 2020, 39(4): 26-30. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202004008.htm
[33] 张景川, 曾周末, 赖平, 等. 基于小波能谱和小波信息熵的管道异常振动事件识别方法[J]. 振动与冲击, 2010, 29(5): 1-4. doi: 10.3969/j.issn.1000-3835.2010.05.001
Zhang J C, Zeng Z M, Lai P, et al. A recognition method with wavelet energy spectrum and wavelet information entropy for abnormal vibration events of a petroleum pipeline[J]. J Vib Shock, 2010, 29(5): 1-4. doi: 10.3969/j.issn.1000-3835.2010.05.001
[34] 李彦, 梁正桃, 李立京, 等. 基于小波和支持向量机的光纤微振动传感器模式识别[J]. 传感器与微系统, 2013, 32(2): 43-45, 49. doi: 10.3969/j.issn.1000-9787.2013.02.013
Li Y, Liang Z T, Li L J, et al. Pattern recognition of fiber-optic micro vibration sensor based on wavelet and SVM[J]. Transducer Microsyst Technol, 2013, 32(2): 43-45, 49. doi: 10.3969/j.issn.1000-9787.2013.02.013
[35] Wu H J, Xiao S K, Li X Y, et al. Separation and determination of the disturbing signals in phase-sensitive optical time domain reflectometry (Φ-OTDR)[J]. J Light Technol, 2015, 33(15): 3156-3162. doi: 10.1109/JLT.2015.2421953
[36] 喻骁芒, 罗光明, 朱珍民, 等. 分布式光纤传感器周界安防入侵信号的多目标识别[J]. 光电工程, 2014, 41(1): 36-41. doi: 10.3969/j.issn.1003-501X.2014.01.007
Yu X M, Luo G M, Zhu Z M, et al. The multi target recognition of intrusion signal of perimeter security with distributed fiber-optic sensor[J]. Opto-Electron Eng, 2014, 41(1): 36-41. doi: 10.3969/j.issn.1003-501X.2014.01.007
[37] 李凯彦, 赵兴群, 孙小菡, 等. 一种用于光纤链路振动信号模式识别的规整化复合特征提取方法[J]. 物理学报, 2015, 64(5): 054304. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201505033.htm
Li K Y, Zhao X Q, Sun X H, et al. A regular composite feature extraction method for vibration signal pattern recognition in optical fiber link system[J]. Acta Phys Sin, 2015, 64(5): 054304. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201505033.htm
[38] Wu H J, Qian Y, Zhang W, et al. Feature extraction and identification in distributed optical-fiber vibration sensing system for oil pipeline safety monitoring[J]. Photonic Sens, 2017, 7(4): 305-310. doi: 10.1007/s13320-017-0360-1
[39] 彭宽, 冯诚, 王森懋, 等. 基于时/频域综合特征提取的分布式光纤入侵监测系统事件识别方法[J]. 光学学报, 2019, 39(6): 0628002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201906041.htm
Peng K, Feng C, Wang S M, et al. Event discrimination method for distributed optical fiber intrusion sensing system based on integrated time/frequency domain feature extraction[J]. Acta Opt Sin, 2019, 39(6): 0628002. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201906041.htm
[40] Sun Q, Feng H, Yan X Y, et al. Recognition of a phase-sensitivity OTDR sensing system based on morphologic feature extraction[J]. Sensors, 2015, 15(7): 15179-15197. doi: 10.3390/s150715179
[41] Aslangul S A. Detecting tunnels for border security based on fiber optical distributed acoustic sensor data using DBSCAN[C]//Proceedings of the 9th International Conference on Sensor Networks, 2020: 78-84.
[42] Cortes C, Vapnik V. Support-vector networks[J]. Mach Learn, 1995, 20(3): 273-297.
[43] Qi X X, Ji J W, Han X W, et al. An Approach of passive vehicle type recognition by acoustic signal based on SVM[C]//Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing, 2009: 545-548.
[44] King D, Lyons W B, Flanagan C, et al. A multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition techniques[J]. Sens Actuator A Phys, 2004, 115(2-3): 293-302. doi: 10.1016/j.sna.2004.03.068
[45] Lewis E, Sheridan C, O'Farrell M, et al. Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals[J]. Sens Actuator A Phys, 2007, 136(1): 28-38. doi: 10.1016/j.sna.2007.02.012
[46] 张俊楠, 娄淑琴, 梁生. 基于SVM算法的φ-OTDR分布式光纤扰动传感系统模式识别研究[J]. 红外与激光工程, 2017, 46(4): 0422003. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201704033.htm
Zhang J N, Lou S Q, Liang S. Study of pattern recognition based on SVM algorithm for φ-OTDR distributed optical fiber disturbance sensing system[J]. Infrared Laser Eng, 2017, 46(4): 0422003. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201704033.htm
[47] Tipping M E. The relevance vector machine[C]//Advances in Neural Information Processing Systems, 2000: 652-658.
[48] 朱永利, 尹金良. 组合核相关向量机在电力变压器故障诊断中的应用研究[J]. 中国电机工程学报, 2013, 33(22): 68-74. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201322010.htm
Zhu Y L, Yin J L. Study on application of multi-kernel learning relevance vector machines in fault diagnosis of power transformers[J]. Proc IEEE Inst Electr Electron Eng, 2013, 33(22): 68-74. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201322010.htm
[49] 孙茜, 曾周末, 李健. 相关向量机在光纤预警系统模式识别中的应用[J]. 天津大学学报(自然科学与工程技术版), 2014, 47(12): 1115-1120. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201412012.htm
Sun Q, Zeng Z M, Li J. Application of relevance vector machine in pattern recognition of optical fiber pre-warning system[J]. J Tianjin Univ (Sci Technol), 2014, 47(12): 1115-1120. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201412012.htm
[50] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[M]//Parallel Distributed Processing: Explorations in the Microstructure Of Cognition, Vol. 1: Foundations. Cambridge: MIT Press, 1986: 318-362.
[51] 李小玉, 吴慧娟, 彭正谱, 等. 基于时间序列奇异谱特征的Φ-OTDR扰动检测方法[J]. 光子学报, 2014, 43(4): 0428001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201404031.htm
Li X Y, Wu H J, Peng Z P, et al. A novel time sequence singular spectrum analysis method for Φ-OTDR disturbance detection system[J]. Acta Photo Sin, 2014, 43(4): 0428001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201404031.htm
[52] 谢鑫, 吴慧娟, 饶云江. 一种基于光纤布喇格光栅振动传感器的光纤围栏入侵监测系统及其模式识别[J]. 光子学报, 2014, 43(5): 0506005. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201405006.htm
Xie X, Wu H J, Rao Y J. A fiber-optical perimeter intrusion detection system based on the fiber Bragg grating vibration sensors and its identification method[J]. Acta Photo Sin, 2014, 43(5): 0506005. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201405006.htm
[53] 沈隆翔, 封皓, 沙洲, 等. 基于下变频和IQ解调的外差型相位敏感光时域反射技术的模式识别[J]. 光学学报, 2017, 37(8): 0806005. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201708010.htm
Shen L X, Feng H, Sha Z, et al. Pattern recognition of heterodyne phase-sensitive optical time-domain reflection technique based on down conversion and IQ demodulation[J]. Acta Opt Sin, 2017, 37(8): 0806005. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201708010.htm
[54] Aktas M, Akgun T, Demircin M U, et al. Deep learning based threat classification in distributed acoustic sensing systems[C]//Proceedings of the 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017.
[55] Xu C J, Guan J J, Bao M, et al. Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR[J]. Opt Eng, 2018, 57(1): 016103. 10.1117/1.OE.57.1.016103
[56] Shi Y, Wang Y Y, Zhao L, et al. An event recognition method for Φ-OTDR sensing system based on deep learning[J]. Sensors (Basel), 2019, 19(15): 3421. doi: 10.3390/s19153421
[57] 吴俊, 管鲁阳, 鲍明, 等. 基于多尺度一维卷积神经网络的光纤振动事件识别[J]. 光电工程, 2019, 46(5): 180493. doi: 10.12086/oee.2019.180493
Wu J, Guan L Y, Bao M, et al. Vibration events recognition of optical fiber based on multi-scale 1-D CNN[J]. Opto-Electron Eng, 2019, 46(5): 180493. doi: 10.12086/oee.2019.180493
[58] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 2672-2680.
[59] Shiloh L, Eyal A, Giryes R. Deep learning approach for processing fiber-optic DAS seismic data[C]//Proceedings of the 26th International Conference on Optical Fiber Sensors, 2018: ThE22.
[60] Li W, Zeng Z Q, Qu H Q, et al. A novel fiber intrusion signal recognition method for ofps based on SCN with dropout[J]. J Light Technol, 2019, 37(20): 5221-5230. doi: 10.1109/JLT.2019.2930624
[61] Wang Z Y, Zheng H R, Li L C, et al. Practical multi-class event classification approach for distributed vibration sensing using deep dual path network[J]. Opt Express, 2019, 27(17): 23682-23692. doi: 10.1364/OE.27.023682
[62] Chen X, Xu C J. Disturbance pattern recognition based on an ALSTM in a long‐distance φ‐OTDR sensing system[J]. Microw Opt Technol Lett, 2020, 62(1): 168-175. doi: 10.1002/mop.32025
[63] Li Z Q, Zhang J W, Wang M N, et al. Fiber distributed acoustic sensing using convolutional long short-term memory network: a field test on high-speed railway intrusion detection[J]. Opt Express, 2020, 28(3): 2925-2938. doi: 10.1364/OE.28.002925
-
点击扫一扫
图(25)
表(1)
计量
- 文章访问数: 15254
- PDF下载数: 2586
- 施引文献: 0
