Abstract

In this paper, an effective wavelength detection approach based on long short-term memory (LSTM) network is proposed for fiber Bragg grating (FBG) sensor networks. The FBG sensor network utilizes a model-sharing mechanism, where the whole spectral wavelength is divided into several shareable regions and spectral overlap is allowed in each region. LSTM, a representative recurrent neural network in deep learning, is applied to learn the features directly from the spectra of FBGs and build the wavelength detection model. By feeding the spectra sequentially into the well-trained model, the Bragg wavelengths of FBGs can be quickly determined under overlap. The obtained LSTM model can be repeatedly used without re-training to improve the multiplexing capability. The results demonstrate that the LSTM-based method can realize high-accuracy and high-speed wavelength detection in the spectral overlapping situations. The proposed approach offers a flexible tool to enhance the sensing capacity of FBG sensor networks.

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

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References

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    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
  4. Y. Hu, W. Mo, K. Dong, F. Jin, and J. Song, “Using maximum spectrum of continuous wavelet transform for demodulation of an overlapped spectrum in a fiber bragg grating sensor network,” Appl. Opt. 55, 4670–4675 (2016).
    [Crossref] [PubMed]
  5. C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  9. C. Triana, M. Varon, and D. Pastor, “Optical code division multiplexed fiber bragg grating sensing networks,” (2015).
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    [Crossref]
  11. A. Triana, D. Pastor, and M. Varon, “A code division design strategy for multiplexing fiber bragg grating sensing networks,” Sensors 17, 2508 (2017).
    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
  14. J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
    [Crossref]
  15. H. Jiang, J. Chen, and T. Liu, “Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine,” IEEE Photon. Technol. Lett. 26, 2031–2034 (2014).
    [Crossref]
  16. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
    [Crossref] [PubMed]
  17. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks 61, 85–117 (2015).
    [Crossref]
  18. L. Zhang and P. N. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364, 146–155 (2016).
    [Crossref]
  19. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
    [Crossref] [PubMed]
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  21. H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
    [Crossref]
  22. K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
    [Crossref]

2017 (2)

A. Triana, D. Pastor, and M. Varon, “A code division design strategy for multiplexing fiber bragg grating sensing networks,” Sensors 17, 2508 (2017).
[Crossref] [PubMed]

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

2016 (4)

A. Triana, D. Pastor, and M. Varon, “Overlap-proof fiber bragg grating sensing system using spectral encoding,” IEEE Photon. Technol. Lett. 28, 744–747 (2016).
[Crossref]

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

L. Zhang and P. N. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364, 146–155 (2016).
[Crossref]

Y. Hu, W. Mo, K. Dong, F. Jin, and J. Song, “Using maximum spectrum of continuous wavelet transform for demodulation of an overlapped spectrum in a fiber bragg grating sensor network,” Appl. Opt. 55, 4670–4675 (2016).
[Crossref] [PubMed]

2015 (3)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref] [PubMed]

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks 61, 85–117 (2015).
[Crossref]

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

2014 (3)

J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine,” IEEE Photon. Technol. Lett. 26, 2031–2034 (2014).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Multi-objective design of an fbg sensor network using an improved strength pareto evolutionary algorithm,” Sens. Actuators, A 220, 230–236 (2014).
[Crossref]

2013 (2)

H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

H. Jiang, J. Chen, T. Liu, and H. Fu, “Design of an fbg sensor network based on pareto multi-objective optimization,” IEEE Photon. Technol. Lett. 25, 1450–1453 (2013).
[Crossref]

2012 (1)

S. J. Mihailov, “Fiber bragg grating sensors for harsh environments,” Sensors 12, 1898–1918 (2012).
[Crossref] [PubMed]

2011 (1)

D. Liu, K. Tang, Z. Yang, and D. Liu, “A fiber bragg grating sensor network using an improved differential evolution algorithm,” IEEE Photon. Technol. Lett. 23, 1385–1387 (2011).
[Crossref]

2006 (1)

J. J. Liang, P. N. Suganthan, C. C. Chan, and V. L. Huang, “Wavelength detection in FBG sensor network using tree search DMS-PSO,” IEEE Photon. Technol. Lett. 18, 1305–1307 (2006).
[Crossref]

2003 (1)

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

2002 (1)

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

1997 (1)

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref] [PubMed]

S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, and et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” (2001).

Chan, C.

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

Chan, C. C.

J. J. Liang, P. N. Suganthan, C. C. Chan, and V. L. Huang, “Wavelength detection in FBG sensor network using tree search DMS-PSO,” IEEE Photon. Technol. Lett. 18, 1305–1307 (2006).
[Crossref]

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

Chen, J.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Multi-objective design of an fbg sensor network using an improved strength pareto evolutionary algorithm,” Sens. Actuators, A 220, 230–236 (2014).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine,” IEEE Photon. Technol. Lett. 26, 2031–2034 (2014).
[Crossref]

J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
[Crossref]

H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

H. Jiang, J. Chen, T. Liu, and H. Fu, “Design of an fbg sensor network based on pareto multi-objective optimization,” IEEE Photon. Technol. Lett. 25, 1450–1453 (2013).
[Crossref]

Demokan, M.

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

Deng, L.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Dong, K.

Frasconi, P.

S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, and et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” (2001).

Fu, H.

H. Jiang, J. Chen, T. Liu, and H. Fu, “Design of an fbg sensor network based on pareto multi-objective optimization,” IEEE Photon. Technol. Lett. 25, 1450–1453 (2013).
[Crossref]

Fu, X.

J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
[Crossref]

Gao, J.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Gong, J. M.

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

Greff, K.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

He, X.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref] [PubMed]

Hochreiter, S.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, and et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” (2001).

Hu, Y.

Huang, V. L.

J. J. Liang, P. N. Suganthan, C. C. Chan, and V. L. Huang, “Wavelength detection in FBG sensor network using tree search DMS-PSO,” IEEE Photon. Technol. Lett. 18, 1305–1307 (2006).
[Crossref]

Huang, W.

H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

Jiang, H.

H. Jiang, J. Chen, and T. Liu, “Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine,” IEEE Photon. Technol. Lett. 26, 2031–2034 (2014).
[Crossref]

J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Multi-objective design of an fbg sensor network using an improved strength pareto evolutionary algorithm,” Sens. Actuators, A 220, 230–236 (2014).
[Crossref]

H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

H. Jiang, J. Chen, T. Liu, and H. Fu, “Design of an fbg sensor network based on pareto multi-objective optimization,” IEEE Photon. Technol. Lett. 25, 1450–1453 (2013).
[Crossref]

Jin, F.

Jin, W.

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

Koutnik, J.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref] [PubMed]

Liang, J. J.

J. J. Liang, P. N. Suganthan, C. C. Chan, and V. L. Huang, “Wavelength detection in FBG sensor network using tree search DMS-PSO,” IEEE Photon. Technol. Lett. 18, 1305–1307 (2006).
[Crossref]

Liao, Y.

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

Liao, Y. B.

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

Liu, D.

D. Liu, K. Tang, Z. Yang, and D. Liu, “A fiber bragg grating sensor network using an improved differential evolution algorithm,” IEEE Photon. Technol. Lett. 23, 1385–1387 (2011).
[Crossref]

D. Liu, K. Tang, Z. Yang, and D. Liu, “A fiber bragg grating sensor network using an improved differential evolution algorithm,” IEEE Photon. Technol. Lett. 23, 1385–1387 (2011).
[Crossref]

Liu, T.

H. Jiang, J. Chen, and T. Liu, “Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine,” IEEE Photon. Technol. Lett. 26, 2031–2034 (2014).
[Crossref]

J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
[Crossref]

H. Jiang, J. Chen, and T. Liu, “Multi-objective design of an fbg sensor network using an improved strength pareto evolutionary algorithm,” Sens. Actuators, A 220, 230–236 (2014).
[Crossref]

H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

H. Jiang, J. Chen, T. Liu, and H. Fu, “Design of an fbg sensor network based on pareto multi-objective optimization,” IEEE Photon. Technol. Lett. 25, 1450–1453 (2013).
[Crossref]

MacAlpine, J. M. K.

J. M. Gong, J. M. K. MacAlpine, C. C. Chan, W. Jin, M. Zhang, and Y. B. Liao, “A novel wavelength detection technique for fiber bragg grating sensors,” IEEE Photon. Technol. Lett. 14, 678–680 (2002).
[Crossref]

Mihailov, S. J.

S. J. Mihailov, “Fiber bragg grating sensors for harsh environments,” Sensors 12, 1898–1918 (2012).
[Crossref] [PubMed]

Mo, W.

Palangi, H.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Pastor, D.

A. Triana, D. Pastor, and M. Varon, “A code division design strategy for multiplexing fiber bragg grating sensing networks,” Sensors 17, 2508 (2017).
[Crossref] [PubMed]

A. Triana, D. Pastor, and M. Varon, “Overlap-proof fiber bragg grating sensing system using spectral encoding,” IEEE Photon. Technol. Lett. 28, 744–747 (2016).
[Crossref]

C. Triana, M. Varon, and D. Pastor, “Optical code division multiplexed fiber bragg grating sensing networks,” (2015).

Peters, K.

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

Schmidhuber, J.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks 61, 85–117 (2015).
[Crossref]

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, and et al., “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” (2001).

Schultz, S.

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

Shen, Y.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Shi, C.

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
[Crossref]

Song, J.

Song, X.

H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval,” IEEE-ACM Transactions on Audio Speech Lang. Process. 24, 694–707 (2016).
[Crossref]

Srivastava, R. K.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

Steunebrink, B. R.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017).
[Crossref]

Stewart, W.

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

Suganthan, P. N.

L. Zhang and P. N. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364, 146–155 (2016).
[Crossref]

J. J. Liang, P. N. Suganthan, C. C. Chan, and V. L. Huang, “Wavelength detection in FBG sensor network using tree search DMS-PSO,” IEEE Photon. Technol. Lett. 18, 1305–1307 (2006).
[Crossref]

Tang, K.

D. Liu, K. Tang, Z. Yang, and D. Liu, “A fiber bragg grating sensor network using an improved differential evolution algorithm,” IEEE Photon. Technol. Lett. 23, 1385–1387 (2011).
[Crossref]

Triana, A.

A. Triana, D. Pastor, and M. Varon, “A code division design strategy for multiplexing fiber bragg grating sensing networks,” Sensors 17, 2508 (2017).
[Crossref] [PubMed]

A. Triana, D. Pastor, and M. Varon, “Overlap-proof fiber bragg grating sensing system using spectral encoding,” IEEE Photon. Technol. Lett. 28, 744–747 (2016).
[Crossref]

Triana, C.

C. Triana, M. Varon, and D. Pastor, “Optical code division multiplexed fiber bragg grating sensing networks,” (2015).

Van Hoe, B.

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

Van Steenberge, G.

W. Stewart, B. Van Hoe, G. Van Steenberge, S. Schultz, and K. Peters, “Spectral profile tracking of multiplexed fiber bragg grating sensors,” Opt. Commun. 357, 113–119 (2015).
[Crossref]

Varon, M.

A. Triana, D. Pastor, and M. Varon, “A code division design strategy for multiplexing fiber bragg grating sensing networks,” Sensors 17, 2508 (2017).
[Crossref] [PubMed]

A. Triana, D. Pastor, and M. Varon, “Overlap-proof fiber bragg grating sensing system using spectral encoding,” IEEE Photon. Technol. Lett. 28, 744–747 (2016).
[Crossref]

C. Triana, M. Varon, and D. Pastor, “Optical code division multiplexed fiber bragg grating sensing networks,” (2015).

Ward, R.

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L. Zhang and P. N. Suganthan, “A survey of randomized algorithms for training neural networks,” Inf. Sci. 364, 146–155 (2016).
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J. Chen, H. Jiang, T. Liu, and X. Fu, “Wavelength detection in FBG sensor networks using least squares support vector regression,” J. Opt. 16, 045402 (2014).
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H. Jiang, J. Chen, T. Liu, and W. Huang, “A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network,” Sens. Actuators, A 198, 31–34 (2013).
[Crossref]

C. Shi, C. Chan, W. Jin, Y. Liao, Y. Zhou, and M. Demokan, “Improving the performance of a FBG sensor network using a genetic algorithm,” Sens. Actuators, A 107, 57–61 (2003).
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H. Jiang, J. Chen, and T. Liu, “Multi-objective design of an fbg sensor network using an improved strength pareto evolutionary algorithm,” Sens. Actuators, A 220, 230–236 (2014).
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C. Triana, M. Varon, and D. Pastor, “Optical code division multiplexed fiber bragg grating sensing networks,” (2015).

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Figures (11)

Fig. 1
Fig. 1 Schematic diagram of the proposed FBG sensor network. OSA: optical spectrum analyzer; PC: personal computer.
Fig. 2
Fig. 2 Architecture of shareable LSTM model.
Fig. 3
Fig. 3 Three gate layers of a LSTM memory block.
Fig. 4
Fig. 4 RMS error, training time and testing time of LSTM models with different number of hidden units.
Fig. 5
Fig. 5 RMS error, training time and testing time of LSTM models with different training sample sizes
Fig. 6
Fig. 6 RMS errors during training process of LSTM.
Fig. 7
Fig. 7 Four testing cases of the FBG pair.
Fig. 8
Fig. 8 RMS errors for different Δλ
Fig. 9
Fig. 9 Combined spectrum and separated spectrum of 10-FBG sensor network.
Fig. 10
Fig. 10 Combined spectrum and separated spectrum of 60-FBG sensor network.
Fig. 11
Fig. 11 Comparisons of cumulative percentile of RMS errors for different methods

Tables (2)

Tables Icon

Table 1 Results of Four Testing Cases

Tables Icon

Table 2 Mean RMS Error and Testing Time of the Wavelength Detection for Different Numbers of FBGs.

Equations (16)

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R ( λ ) = j = 1 2 n γ ( λ , λ B j ) .
R ( λ ) = j = 1 n R j ( λ ) = j = 1 n Γ j ( λ , λ B 1 , λ B 2 ) .
( λ B 1 , λ B 2 ) j = Γ j 1 ( R j ( λ ) ) .
( λ B 1 , λ B 2 ) k = a k + ( Θ ( R k ( λ ) ) a j ) * ( b k a k ) ( b j a j ) .
D = ( X 1 , Y 1 ) , , ( X k , Y k ) , ( X N , Y N )
h m = H ( W x h x m + W h h h m 1 + b h )
Y = W h y h m + b y
f t = σ ( W f x x t + W f h h t 1 + b f )
σ ( z ) = 1 1 + exp ( z )
i t = σ ( W i x x t + W i h h t 1 + b i )
g t = ϕ ( W g x x t + W g h h t 1 + b g )
ϕ ( z ) = exp ( z ) exp ( z ) exp ( z ) + exp ( z )
s t = f t s t 1 + i t g t
o t = σ ( W ox x t + W oh h t 1 + b o )
h t = o t ϕ ( s t )
R ( λ , λ Bi ) = I peak exp [ 4 ln 2 * ( λ λ Bi Δ λ Bi ) 2 ]

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