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        首頁 > 論文 > 激光與光電子學進展 > 56卷 > 13期(pp:130601--1)

        光纖入侵信號的特征提取與識別算法

        Feature Extraction and Recognition Algorithm for Fiber Intrusion Signals

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        摘要

        為了對分布式光纖上的入侵信號類型進行準確識別,提出了一種基于集合經驗模態分解(EEMD)結合隨機向量函數鏈接(RVFL)神經網絡的光纖入侵信號的特征提取與識別算法。算法步驟為:對采集到的光纖入侵信號作預處理操作,包括最小-最大規范化處理和利用db3小波去除信號的低頻噪聲;采用EEMD方法對入侵信號進行分解,得到5組本征模態函數(IMF);計算各IMF分量的能量占比,并依據方差分析法篩選出3組特征向量;將特征向量送入RVFL神經網絡進行訓練并對入侵信號進行識別。實驗結果顯示:該方法能正確識別不同入侵信號的類型,具有較高的準確率。

        Abstract

        A feature extraction and recognition algorithm for fiber intrusion signals is proposed based on ensemble empirical-mode decomposition (EEMD) coupled with a random vector-function linked (RVFL) neural network to accurately identify the type of intrusion signal on a distributed optical fiber. The proposed algorithm starts with the preprocessing for the collected fiber intrusion signals,including minimum-maximum normalization processing and the removal of low frequency noise using the db3 wavelet. Then, the intrusion signals are decomposed by the EEMD to obtain five groups of intrinsic mode functions (IMF). Subsequently, the energy ratio of each component of the IMF is calculated, and three feature vectors are filtered using the analysis of variance. Finally, the feature vectors are sent into the RVFL neural network to be trained for the completion of the signal recognition. The experimental results validate that the proposed algorithm can accurately distinguish between different intrusion signals with high recognition rate.

        Newport宣傳-MKS新實驗室計劃
        補充資料

        DOI:10.3788/LOP56.130601

        所屬欄目:光纖光學與光通信

        基金項目:國家自然科學基金; 北京自然科學基金;

        收稿日期:2018-12-12

        修改稿日期:2019-01-24

        網絡出版日期:2019-07-01

        作者單位    點擊查看

        曲洪權:北方工業大學電子信息學院, 北京 100144
        宮殿君:北方工業大學電子信息學院, 北京 100144
        張常年:北方工業大學電子信息學院, 北京 100144
        王彥平:北方工業大學電子信息學院, 北京 100144

        聯系人作者:宮殿君(769353964@qq.com)

        備注:國家自然科學基金; 北京自然科學基金;

        【1】Bi F K, Feng C, Qu H Q et al. Harmful intrusion detection algorithm of optical fiber pre-warning system based on correlation of orthogonal polarization signals. Photonic Sensors. 7(3), 226-233(2017).

        【2】Liang W, Lu L L and Zhang L B. Coupling relations and early-warning for “equipment chain” in long-distance pipeline. Mechanical Systems and Signal Processing. 41(1/2), 335-347(2013).

        【3】Yang Y, Feng H, Wang Z H et al. Application and development of distributed optical fiber sensing technology in pipeline detection. Electro-Optic Technology Application. 31(6), 1-9, 76(2016).
        楊洋, 封皓, 王宗和 等. 光纖傳感技術在管道檢測中的應用與發展. 光電技術應用. 31(6), 1-9, 76(2016).

        【4】An Y, Jin S J, Feng X et al. Optical fiber pipeline security pre-warning system based on coherent Rayleigh scattering. Journal of Tianjin University. 48(1), 70-75(2015).
        安陽, 靳世久, 馮欣 等. 基于相干瑞利散射的管道安全光纖預警系統. 天津大學學報. 48(1), 70-75(2015).

        【5】Sheng Z Y, Zhang X Y, Wang Y P et al. Feature extraction and linear classification for fiber vibration signals. Journal of Optoelectronics·Laser. 29(7), 760-768(2018).
        盛智勇, 張新燕, 王彥平 等. 光纖振動信號特征提取及線性分類方法. 光電子·激光. 29(7), 760-768(2018).

        【6】Shang Y, Wang C, Wang C et al. Distributed vibration sensing of perimeter security based on space difference of Rayleigh backscattering. Infrared and Laser Engineering. 47(5), (2018).
        尚盈, 王晨, 王昌 等. 采用后向瑞利散射空間差分的周界安防分布式振動監測. 紅外與激光工程. 47(5), (2018).

        【7】Pang F F, Liu H H and Wang T Y. A review of distributed fiber sensors based on phase-sensitive optical time domain reflectometer. Journal of Nanjing University of Information Science & Technology (Natural Science Edition). 9(2), 130-136(2017).
        龐拂飛, 劉奐奐, 王廷云. 相位敏感光時域反射光纖傳感技術的研究綜述. 南京信息工程大學學報(自然科學版). 9(2), 130-136(2017).

        【8】Qu H Q, Ren X C, Bi F K et al. Two-level detection algorithm of two-dimensional for vibration signals detected by optical fiber. Acta Optica Sinica. 35(10), (2015).
        曲洪權, 任學叢, 畢福昆 等. 光纖振動信號的二維二級檢測算法. 光學學報. 35(10), (2015).

        【9】Qu H Q, Wang T Q, Bi F K et al. Harmful intrusion detection method based on two-dimensional K-S test in reconfigured background for optical fiber pre-warning system. Journal of Jishou University (Natural Science Edition). 38(1), 19-23(2017).
        曲洪權, 王天琦, 畢福昆 等. 基于重構背景二維K-S檢驗的有害入侵光纖預警. 吉首大學學報(自然科學版). 38(1), 19-23(2017).

        【10】Qu H Q, Zheng T, Bi F K et al. Vibration detection method for optical fibre pre-warning system. IET Signal Processing. 10(6), 692-698(2016).

        【11】Sha Y Y, Xi L X, Zhang X G et al. Polarization mode dispersion measurement based on wavelet threshold denoising. Chinese Journal of Lasers. 45(11), (2018).
        沙宇洋, 席麗霞, 張曉光 等. 基于小波閾值去噪的偏振模色散測量. 中國激光. 45(11), (2018).

        【12】Pan P, Xi L X, Zhang X G et al. Experimental research on polarization mode dispersion measurement based on empirical mode decomposition. Chinese Journal of Lasers. 45(1), (2018).
        潘潘, 席麗霞, 張曉光 等. 基于經驗模態分解的偏振模色散測量實驗研究. 中國激光. 45(1), (2018).

        【13】Qu H Q, Wang X X, Bi F K et al. Optical fiber vibration recognition based on wavelet reconstruction and time-space features. Journal of Jishou University (Natural Science Edition). 38(2), 36-41(2017).
        曲洪權, 王笑笑, 畢福昆 等. 基于小波重構與時空二維特征的光纖振動識別. 吉首大學學報(自然科學版). 38(2), 36-41(2017).

        【14】Wang J P, Hao Z and Zhu C H. Research on vibration signal recognition of optical fiber perimeter based on phase space reconstruction. Journal of Hefei University of Technology (Natural Science). 40(5), 643-648(2017).
        王建平, 郝釗, 朱程輝. 基于相空間重構的光纖周界信號識別算法研究. 合肥工業大學學報(自然科學版). 40(5), 643-648(2017).

        【15】Zhang Y J, Liu W Z, Fu X H et al. An extraction and recognition method of the distributed optical fiber vibration signal based on EMD-AWPP and HOSA-SVM algorithm. Spectroscopy and Spectral Analysis. 36(2), 577-582(2016).
        張燕君, 劉文哲, 付興虎 等. 基于EMD-AWPP和HOSA-SVM算法的分布式光纖振動入侵信號的特征提取與識別. 光譜學與光譜分析. 36(2), 577-582(2016).

        【16】Igelnik B and Pao Y H. Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Transactions on Neural Networks. 6(6), 1320-1329(1995).

        引用該論文

        Hongquan Qu, Dianjun Gong, Changnian Zhang, Yanping Wang. Feature Extraction and Recognition Algorithm for Fiber Intrusion Signals[J]. Laser & Optoelectronics Progress, 2019, 56(13): 130601

        曲洪權, 宮殿君, 張常年, 王彥平. 光纖入侵信號的特征提取與識別算法[J]. 激光與光電子學進展, 2019, 56(13): 130601

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