Abstract

This manuscript proposes a method based on back propagation (BP) neural network and the spectral subtraction method to quickly obtain sensing information in Brillouin fiber optics sensors. BP neural network’s characteristics which can realize any complex nonlinear mapping help to determine the frequency shift section(s) information. The training function, transfer function and number of hidden layer nodes of BP neural network are determined with experimental data. The experimental results show that comparing with traditional Lorentz fitting algorithm and edge detection with Sobel operator, the BP neural network is about 1/12 in terms of time complexity with the Lorentz algorithm, about 1/9 with the edge detection based on Sobel operator; while the respective accuracy on determine the frequency shifted section(s) has improved by 79.4% and 27.9%.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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  1. F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
    [Crossref]
  2. J. Fang, P. Xu, Y. Dong, and W. Shieh, “Single-shot distributed Brillouin optical time domain analyzer,” Opt. Express 25(13), 15188–15198 (2017).
    [Crossref] [PubMed]
  3. W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
    [Crossref]
  4. Y. J. Rao, “Recent progress in applications of in-fibre Bragg grating sensors,” Opt. Lasers Eng. 31(4), 297–324 (1999).
    [Crossref]
  5. H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
    [Crossref]
  6. X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
    [Crossref] [PubMed]
  7. X. Bao and L. Chen, “Recent progress in Brillouin scattering based fiber sensors,” Sensors (Basel) 11(4), 4152–4187 (2011).
    [Crossref] [PubMed]
  8. F. Peng, H. Wu, X.-H. Jia, Y.-J. Rao, Z.-N. Wang, and Z.-P. Peng, “Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines,” Opt. Express 22(11), 13804–13810 (2014).
    [Crossref] [PubMed]
  9. Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
    [Crossref]
  10. M. A. Farahani, M. T. V. Wylie, E. Castillo-Guerra, and B. G. Colpitts, “Reduction in the number of averages required in BOTDA sensors using wavelet denoising techniques,” J. Lightwave Technol. 30(8), 1134–1142 (2012).
    [Crossref]
  11. X. Qian, X. Jia, Z. Wang, B. Zhang, N. Xue, W. Sun, Q. He, and H. Wu, “Noise level estimation of BOTDA for optimal non-local means denoising,” Appl. Opt. 56(16), 4727–4734 (2017).
    [Crossref] [PubMed]
  12. Y. Muanenda, M. Taki, and F. D. Pasquale, “Long-range accelerated BOTDA sensor using adaptive linear prediction and cyclic coding,” Opt. Lett. 39(18), 5411–5414 (2014).
    [Crossref] [PubMed]
  13. M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
    [Crossref] [PubMed]
  14. T. Zhu, X. Xiao, Q. He, and D. Diao, “Enhancement of SNR and spatial resolution in φ-OTDR system by using two-dimensional edge detection method,” J. Lightwave Technol. 31(17), 2851–2856 (2013).
    [Crossref]
  15. F. Wang, W. Zhan, X. Zhang, and Y. Lu, “Improvement of spatial resolution for BOTDR by iterative subdivision method,” J. Lightwave Technol. 31(23), 3663–3667 (2013).
    [Crossref]
  16. A. K. Azad, F. N. Khan, W. H. Alarashi, N. Guo, A. P. T. Lau, and C. Lu, “Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition,” Opt. Express 25(14), 16534–16549 (2017).
    [Crossref] [PubMed]
  17. Z. Qin, L. Chen, and X. Bao, “Continuous wavelet transform for non-stationary vibration detection with phase-OTDR,” Opt. Express 20(18), 20459–20465 (2012).
    [Crossref] [PubMed]
  18. A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
    [Crossref] [PubMed]
  19. M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation,” Opt. Lett. 36(21), 4275–4277 (2011).
    [Crossref] [PubMed]
  20. M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A detailed evaluation of the correlation-based method used for estimation of the Brillouin frequency shift in BOTDA sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
    [Crossref]
  21. K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).
  22. N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).
  23. I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods 43(1), 3–31 (2000).
    [Crossref] [PubMed]
  24. X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
    [Crossref]
  25. Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
    [Crossref]
  26. N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.
  27. L. S. H. Ngia, J. Sjoberg, and M. Viberg, “Adaptive neural nets filter using a recursive levenberg-marquardt search direction,” in Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998 IEEE (1998), pp. 697–701.
    [Crossref]
  28. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
    [Crossref]
  29. R. Rastegar and A. Hariri, “A step forward in studying the compact genetic algorithm,” Evol. Comput. 14(3), 277–289 (2006).
    [Crossref] [PubMed]
  30. R. Hecht-Nielsen, “Kolmogorov’s mapping neural network existence theorem,” in Proceedings of the IEEE International Conference on Neural Networks III,1987IEEE (1987), pp. 11–13.

2018 (1)

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

2017 (3)

2016 (2)

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
[Crossref] [PubMed]

A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
[Crossref] [PubMed]

2014 (2)

2013 (3)

2012 (5)

Z. Qin, L. Chen, and X. Bao, “Continuous wavelet transform for non-stationary vibration detection with phase-OTDR,” Opt. Express 20(18), 20459–20465 (2012).
[Crossref] [PubMed]

Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
[Crossref]

M. A. Farahani, M. T. V. Wylie, E. Castillo-Guerra, and B. G. Colpitts, “Reduction in the number of averages required in BOTDA sensors using wavelet denoising techniques,” J. Lightwave Technol. 30(8), 1134–1142 (2012).
[Crossref]

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

2011 (2)

2010 (1)

W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
[Crossref]

2006 (1)

R. Rastegar and A. Hariri, “A step forward in studying the compact genetic algorithm,” Evol. Comput. 14(3), 277–289 (2006).
[Crossref] [PubMed]

2000 (1)

I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods 43(1), 3–31 (2000).
[Crossref] [PubMed]

1999 (1)

Y. J. Rao, “Recent progress in applications of in-fibre Bragg grating sensors,” Opt. Lasers Eng. 31(4), 297–324 (1999).
[Crossref]

1989 (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

1988 (1)

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).

Alarashi, W. H.

Azad, A. K.

Baker, R. L.

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).

Bao, X.

Z. Qin, L. Chen, and X. Bao, “Continuous wavelet transform for non-stationary vibration detection with phase-OTDR,” Opt. Express 20(18), 20459–20465 (2012).
[Crossref] [PubMed]

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
[Crossref]

X. Bao and L. Chen, “Recent progress in Brillouin scattering based fiber sensors,” Sensors (Basel) 11(4), 4152–4187 (2011).
[Crossref] [PubMed]

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Basheer, I. A.

I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods 43(1), 3–31 (2000).
[Crossref] [PubMed]

Cao, Z.

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

Castillo-Guerra, E.

Chen, L.

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Z. Qin, L. Chen, and X. Bao, “Continuous wavelet transform for non-stationary vibration detection with phase-OTDR,” Opt. Express 20(18), 20459–20465 (2012).
[Crossref] [PubMed]

Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
[Crossref]

X. Bao and L. Chen, “Recent progress in Brillouin scattering based fiber sensors,” Sensors (Basel) 11(4), 4152–4187 (2011).
[Crossref] [PubMed]

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Colpitts, B. G.

Diao, D.

Dong, Y.

Fang, J.

Farahani, M. A.

Geng, X.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Gui, T.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Guo, N.

A. K. Azad, F. N. Khan, W. H. Alarashi, N. Guo, A. P. T. Lau, and C. Lu, “Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition,” Opt. Express 25(14), 16534–16549 (2017).
[Crossref] [PubMed]

A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
[Crossref] [PubMed]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Hajmeer, M.

I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods 43(1), 3–31 (2000).
[Crossref] [PubMed]

Hariri, A.

R. Rastegar and A. Hariri, “A step forward in studying the compact genetic algorithm,” Evol. Comput. 14(3), 277–289 (2006).
[Crossref] [PubMed]

He, J.

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

He, Q.

He, Z.

W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
[Crossref]

Hecht-Nielsen, R.

R. Hecht-Nielsen, “Kolmogorov’s mapping neural network existence theorem,” in Proceedings of the IEEE International Conference on Neural Networks III,1987IEEE (1987), pp. 11–13.

Hornik, K.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Hotate, K.

W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
[Crossref]

Jia, L.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Jia, X.

Jia, X.-H.

F. Peng, H. Wu, X.-H. Jia, Y.-J. Rao, Z.-N. Wang, and Z.-P. Peng, “Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines,” Opt. Express 22(11), 13804–13810 (2014).
[Crossref] [PubMed]

F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
[Crossref]

Jia, Y.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Jiang, M.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Jin, C.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Kanopoulos, N.

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).

Khan, F. N.

Lau, A. P. T.

Li, W.

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Liang, H.

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Linze, N.

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Lou, S.

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

Lu, C.

A. K. Azad, F. N. Khan, W. H. Alarashi, N. Guo, A. P. T. Lau, and C. Lu, “Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition,” Opt. Express 25(14), 16534–16549 (2017).
[Crossref] [PubMed]

A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
[Crossref] [PubMed]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Lu, S.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Lu, Y.

Lv, S.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Mao, Y.

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

Muanenda, Y.

Ngia, L. S. H.

L. S. H. Ngia, J. Sjoberg, and M. Viberg, “Adaptive neural nets filter using a recursive levenberg-marquardt search direction,” in Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998 IEEE (1998), pp. 697–701.
[Crossref]

Pasquale, F. D.

Peng, F.

F. Peng, H. Wu, X.-H. Jia, Y.-J. Rao, Z.-N. Wang, and Z.-P. Peng, “Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines,” Opt. Express 22(11), 13804–13810 (2014).
[Crossref] [PubMed]

F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
[Crossref]

Peng, Z.-P.

Qian, X.

Qin, Z.

Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
[Crossref]

Z. Qin, L. Chen, and X. Bao, “Continuous wavelet transform for non-stationary vibration detection with phase-OTDR,” Opt. Express 20(18), 20459–20465 (2012).
[Crossref] [PubMed]

Ramírez, J. A.

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
[Crossref] [PubMed]

Rao, Y. J.

Y. J. Rao, “Recent progress in applications of in-fibre Bragg grating sensors,” Opt. Lasers Eng. 31(4), 297–324 (1999).
[Crossref]

Rao, Y.-J.

F. Peng, H. Wu, X.-H. Jia, Y.-J. Rao, Z.-N. Wang, and Z.-P. Peng, “Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines,” Opt. Express 22(11), 13804–13810 (2014).
[Crossref] [PubMed]

F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
[Crossref]

Rastegar, R.

R. Rastegar and A. Hariri, “A step forward in studying the compact genetic algorithm,” Evol. Comput. 14(3), 277–289 (2006).
[Crossref] [PubMed]

Shieh, W.

Sjoberg, J.

L. S. H. Ngia, J. Sjoberg, and M. Viberg, “Adaptive neural nets filter using a recursive levenberg-marquardt search direction,” in Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998 IEEE (1998), pp. 697–701.
[Crossref]

Soto, M. A.

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
[Crossref] [PubMed]

Stinchcombe, M.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Sui, Q.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Sun, W.

Taki, M.

Tam, H. Y.

A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
[Crossref] [PubMed]

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Thévenaz, L.

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
[Crossref] [PubMed]

Vasanthavada, N.

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).

Viberg, M.

L. S. H. Ngia, J. Sjoberg, and M. Viberg, “Adaptive neural nets filter using a recursive levenberg-marquardt search direction,” in Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998 IEEE (1998), pp. 697–701.
[Crossref]

Wang, F.

Wang, L.

A. K. Azad, L. Wang, N. Guo, H. Y. Tam, and C. Lu, “Signal processing using artificial neural network for BOTDA sensor system,” Opt. Express 24(6), 6769–6782 (2016).
[Crossref] [PubMed]

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Wang, Z.

X. Qian, X. Jia, Z. Wang, B. Zhang, N. Xue, W. Sun, Q. He, and H. Wu, “Noise level estimation of BOTDA for optimal non-local means denoising,” Appl. Opt. 56(16), 4727–4734 (2017).
[Crossref] [PubMed]

F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
[Crossref]

Wang, Z.-N.

White, H.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Wu, H.

Wylie, M. T. V.

Xiao, H.

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Xiao, X.

Xu, P.

Xue, N.

Yu, C.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Yu, K.

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

Yu, K. L.

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

Yuan, J.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Zhan, W.

Zhang, B.

Zhang, X.

F. Wang, W. Zhan, X. Zhang, and Y. Lu, “Improvement of spatial resolution for BOTDR by iterative subdivision method,” J. Lightwave Technol. 31(23), 3663–3667 (2013).
[Crossref]

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

Zhong, K.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Zhou, X.

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

Zhu, T.

Zou, W.

W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
[Crossref]

Appl. Opt. (1)

Evol. Comput. (1)

R. Rastegar and A. Hariri, “A step forward in studying the compact genetic algorithm,” Evol. Comput. 14(3), 277–289 (2006).
[Crossref] [PubMed]

IEEE J. Solid-St. Circulation (1)

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-St. Circulation 23(2), 358–367 (1988).

IEEE Photonics J. (1)

Y. Mao, N. Guo, K. L. Yu, H. Y. Tam, and C. Lu, “1-cm-spatial-resolution Brillouin optical time-domain analysis based on bright pulse Brillouin gain and complementary code,” IEEE Photonics J. 4(6), 2243–2248 (2012).
[Crossref]

IEEE Photonics Technol. Lett. (2)

W. Zou, Z. He, and K. Hotate, “Demonstration of Brillouin distributed discrimination of strain and temperature using a polarization-maintaining optical fiber,” IEEE Photonics Technol. Lett. 22(8), 526–528 (2010).
[Crossref]

Z. Qin, L. Chen, and X. Bao, “Wavelet denoising method for improving detection performance of distributed vibration sensor,” IEEE Photonics Technol. Lett. 24(7), 542–544 (2012).
[Crossref]

IEEE Sens. J. (1)

M. A. Farahani, E. Castillo-Guerra, and B. G. Colpitts, “A detailed evaluation of the correlation-based method used for estimation of the Brillouin frequency shift in BOTDA sensors,” IEEE Sens. J. 13(12), 4589–4598 (2013).
[Crossref]

J. Lightwave Technol. (3)

J. Microbiol. Methods (1)

I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” J. Microbiol. Methods 43(1), 3–31 (2000).
[Crossref] [PubMed]

Nat. Commun. (1)

M. A. Soto, J. A. Ramírez, and L. Thévenaz, “Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration,” Nat. Commun. 7(1), 10870 (2016).
[Crossref] [PubMed]

Neural Netw. (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Opt. Express (5)

Opt. Lasers Eng. (1)

Y. J. Rao, “Recent progress in applications of in-fibre Bragg grating sensors,” Opt. Lasers Eng. 31(4), 297–324 (1999).
[Crossref]

Opt. Lett. (2)

Photonic Sens. (1)

X. Geng, S. Lu, M. Jiang, Q. Sui, S. Lv, H. Xiao, Y. Jia, and L. Jia, “Research on FBG-based CFRP structural damage identification using BP neural network,” Photonic Sens. 8(2), 168–175 (2018).
[Crossref]

Sensors (Basel) (2)

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

X. Bao and L. Chen, “Recent progress in Brillouin scattering based fiber sensors,” Sensors (Basel) 11(4), 4152–4187 (2011).
[Crossref] [PubMed]

Other (6)

H. Liang, W. Li, N. Linze, L. Chen, X. Bao, and X. Zhang, “Comparison of return-to-zero and non-return-to-zero coded pulses for BOTDA,” in 9th International Conference on Optical Communications and Networks (ICOCN),2010IET (2010), pp. 31–35.
[Crossref]

F. Peng, Z. Wang, Y.-J. Rao, and X.-H. Jia, “106km fully-distributed fiber-optic fence based on P-OTDR with 2nd-order Raman amplification,” in Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC),2013IEEE (2013), pp. JW2A.22.
[Crossref]

K. Yu, N. Guo, Z. Cao, S. Lou, and J. He, “Fast Brillouin optical time domain analyzer sensing information acquisition with spectra subtraction,” (submitted).

N. Guo, L. Wang, C. Jin, T. Gui, K. Zhong, X. Zhou, J. Yuan, C. Yu, H. Y. Tam, and C. Lu, “Coherent-detection-assisted BOTDA system without averaging using single-sideband modulated local oscillator signal,” in 25th Optical Fiber Sensors Conference (OFS),2017IEEE (2017), pp. 1–4.

L. S. H. Ngia, J. Sjoberg, and M. Viberg, “Adaptive neural nets filter using a recursive levenberg-marquardt search direction,” in Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998 IEEE (1998), pp. 697–701.
[Crossref]

R. Hecht-Nielsen, “Kolmogorov’s mapping neural network existence theorem,” in Proceedings of the IEEE International Conference on Neural Networks III,1987IEEE (1987), pp. 11–13.

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Figures (12)

Fig. 1
Fig. 1 Schematic diagram of spectral subtraction method [21], (a): Original spectrum, (b): measured spectrum, (c): subtracted spectrum.
Fig. 2
Fig. 2 Topology diagram of BP neural network.
Fig. 3
Fig. 3 Training and testing phases of the BP neural network for extracting the frequency shifted section(s).
Fig. 4
Fig. 4 The BOTDA experimental setup, EOM: electro-optic modulator, PS: polarization scrambler, EDFA: Erbium-doped fiber amplifier, PD: photodetector, FUT: fiber under test.
Fig. 5
Fig. 5 (a): subtracted spectrum after 2D median filtering, (b): noise. The filter parameter is 15.
Fig. 6
Fig. 6 Deviation versus the median filter parameter on the position of the starting point of (a) the original spectrum, (b) the measurement spectrum, (c) Deviation based on the subtracted spectrum of the above two schemes, (d) opening circles parameters and deviation, 1D: One Dimension, 2D: Two Dimension.
Fig. 7
Fig. 7 (a): Mean and variance of hidden layer nodes in BP neural network, (b): Deviation for different training functions (Inset: Details for the training functions of trainlm, traingdm and traingdx).
Fig. 8
Fig. 8 Comparison of deviation of functions for the hidden layer with the logsig, (a); the tansig, (b) (Insets: Detailed deviation with the output layer using the satlins, purelin and poslin functions).
Fig. 9
Fig. 9 Deviation for 11 to 20 hidden layer nodes (from (a) to (j)).
Fig. 10
Fig. 10 Mean and variance relationship with the hidden layer nodes number.
Fig. 11
Fig. 11 Comparisons of the starting position of BFS and the BFS obtained with the LFM, the edge detection algorithm and the BP neural network algorithm.
Fig. 12
Fig. 12 Determine Deviation for the LCF and the BP algorithms.

Tables (2)

Tables Icon

Table 1 Comparison of 3 algorithms (LFM(i), edge detection(ii) and BP neural network(iii)) and the starting position of BFS (Unit: m)

Tables Icon

Table 2 In terms of time complexity and deviation, comparison of three algorithms (LFM(i), edge detection(ii) and BP neural network(iii)) and the starting position of BFS

Equations (2)

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Deviation= | L Left - L Start |+| L Right - L End | L Left - L Right
L= m+t +a

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