High Precision Identification of Optic Fiber Invasion Sensor Networks Information Based on the BBS and BPNN-DS Algorithm

Zhang Yanjun; Liu Wenzhe; Fu Xinghu

doi:10.3788/aos201636.0106001

[1] Bi Weihong, Yang Xipeng, Li Jingyang, et al.. Forward and backward Raman amplification of Brillouin scattering signal in Brillouin optical time domain reflectometer system[J]. Chinese J Lasers, 2014, 41(12): 1205007.

[2] Zhengxian Zhou, Songlin Zhuang. A lateral locating method for optical fiber distributed intrusion sensing system[J]. Opt Commun, 2014, 333(15): 1-5.

[3] Yang Jiang, Zhao Jian, Yang Chunni, et al.. All-fiber permeter security based on Michelson interferometer and pattern recognition[J]. Chinese J Lasers, 2014, 41(11): 1105005.

[4] Yu Xiaomang, Luo Guangming, Zhu Zhenmin, et al.. The multi target recognition of intrusion signal of perimeter security with distributed fiber-optic sensor[J]. Opto-Electronic Engineering, 2014, 41(1): 36-41.

[5] Wang Zhaoyong, Pan Zhengqing, Ye Qing, et al.. Fast pattern recognition based on frequency spectrum analysis used for intrusion alarming in optical fiber fence[J]. Chinese J Lasers, 2015, 42(4): 0405010.

[6] S S Mahmoud, Y Visagathilagar, J Katsifolis. Real-time distributed fiber optic sensor for security systems: Performance, event classification and nuisance mitigation[J]. Photonic Sensors, 2012, 2(3): 225-236.

[7] Hwang-Ki Min, Chung-Yeo Lee, Jong-Seok Lee, et al.. Abnormal signal detection in gas pipes using neural networks[C]. Proceeding of 33rd Annual Conference of the IEEE Industrial Electronics Society, 2007: 2503-2508.

[8] Xiaoning Jia, HangYang, Siliang Ma, et al.. Quaternion higher-order spectra and their invariants for color image recognition[J]. Optics and Lasers in Engineering, 2014, 57: 28-39.

[9] B Liang, S D Iwnicki, Y Zhao. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 39(1-2): 342-360.

[10] Kuang Chua Chuaa, Vinod Chandran, U Rajendra Acharyaa, et al.. Application of higher order statistics/spectra in biomedical signals [J]. Medical Engineering & Physics, 2010, 32(7): 679-689.

[11] Qiu Su, Jin Weiqi, Song Zheng. Blind recognition technology of image tampering based on higher-order statistics biocherent theory the research[J]. Optical Technique, 2011, 37(3): 351-356.

[12] H M Al-Angari, A V Sahakian. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(10): 1900-1904.

[13] Bubathi Muruganatham, M A Sanjith, B Krishnakumar, et al.. Roller element bearing fault diagnosis using singular spectrum analysis [J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 150-166.

[14] M Claudio, S Rocco. Singular spectrum analysis and forecasting of failure time series[J]. Reliability Engineering and System Safety, 2013, 114: 126-136.

[15] C Y Yang, T Y Wu. Diagnostics of gear deterioration using EEMD approach and PCA process[J]. Measurement, 2015, 61: 75-87.

[16] Lin Wanga, Yi Zenga, Tao Chenb. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting [J]. Expert Systems with Applications, 2015, 42(2): 855-863.

[17] L N Zhang, F P Wu, L L Yu, et al.. Grey clustering evaluation model based on D-S evidence theory to evaluate the scheme of basin initial water rights allocation[J]. Open Cybernetics and Systemics Journal, 2015, 9(1): 7-16.

[18] Huifang Wang, Dongyang Lin, Jian Qiu, et al.. Research on multiobjective group decision making in condition-based maintenance for transmission and transformation equipment based on D-S evidence theory[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 1035-1045.

[19] Y W Du, C Han. Classical models and its applications in D-S evidence theory[J]. Applied Mechanics and Materials, 2012, (204-208): 4958-4961.