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Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network

Sunil Kumar Somnath Sengupta
Opto-Electronics Review | 2022 | 30 | 4 | e144227 | DOI: 10.24425/opelre.2022.144227
Keywords fibre Bragg grating generative model discriminative model loss function
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Abstract

An adaptive and precise peak wavelength detection algorithm for fibre Bragg grating using generative adversarial network is proposed. The algorithm consists of generative model and discriminative model. The generative model generates a synthetic signal and is sampled for training using a deep neural network. The discriminative model predicts the real fibre Bragg grating signal by the calculation of the loss functions. The maxima of loss function of the discriminative signal and the minima of loss function of the generative signal are matched and the desired peak wavelength of fibre Bragg grating is determined. The proposed algorithm is verified theoretically and experimentally for a single fibre Bragg grating peak. The accuracy has been obtained as ±0.2 pm. The proposed algorithm is adaptive in the sense that any random fibre Bragg grating peak can be identified within a short wavelength range.
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Bibliography

  1. Chen, G. Y. & Brambilla, G. Optical Microfiber Physical Sensors. in Optical Fiber Sensors: Advanced Techniques and Applications (ed. Rajan, G.) chapter 8 (CRC Press, 2017).
  2. Fiber optic bio and chemical sensors. in Fiber optic sensors (eds. Yin, Sh., Ruffin, P. B. & Yu, F. T. S.) 435–457 (CRC Press, 2008).
  3. Ma, Z. & Chen, X. Fiber Bragg gratings sensors for aircraft wing shape measurement: Recent applications and technical analysis. Sensors 19, 55 (2018). https://doi.org/10.3390/s19010055
  4. Jinachandran, S. et al. Fabrication and characterization of a magnetized metal-encapsulated FBG sensor for structural health monitoring. IEEE Sensor J. 18, 8739–8746 (2018). https://doi.org/10.1109/JSEN.2018.2866803
  5. Gautam, A., Kumar, A. & Priya, V. Microseismic wave detection in coal mines using differential optical power measurement. Opt. Eng. 58 056111 (2019). https://doi.org/10.1117/1.OE.58.5.056111
  6. Kinjalk, K., Kumar, A. & Gautam, A. High-resolution FBG-based inclination sensor using eigen decomposition of reflection spectrum. IEEE Trans. Instrum. Meas. 69, 9124–9131 (2020). https://doi.org/10.1109/TIM.2020.2999116
  7. Vickers, N. J. Animal communication: when I’m calling you, will you answer too. Curr. Biol. 27, R713–R715 (2017). https://doi.org/10.1016/j.cub.2017.05.064
  8. An, Y., Wang, X., Qu, Zh., Liao, T. & Nan, Zh. Fiber Bragg grating temperature calibration based on BP neural network. Optik 172, 753–759 (2018). https://doi.org/10.1016/j.ijleo.2018.07.064
  9. Chen, Z.-J. et al. Optimization and comparison of the peak-detection algorithms for the reflection spectrum of fiber Bragg grating. Acta Photon. Sin. 44, 1112001 (2015). [in Chinese]
  10. Trita, A. et al. Simultaneous interrogation of multiple fiber Bragg grating sensors using an arrayed Waveguide grating filter fabricated in SOI platform. IEEE Photon. J. 7, 1–11 (2015). https://doi.org/10.1109/JPHOT.2015.2499546
  11. Junfeng, J. et al. Distortion-tolerated high speed FBG demodulation method using temporal response of high-gain photodetector. Opt. Fiber Technol. 45, 399–404 (2018). https://doi.org/10.1016/j.yofte.2018.08.019
  12. Kumar, S. et al. Efficient detection of multiple FBG wavelength peaks using matched filtering technique. Opt. Quantum Electron. 54, 1–14 (2022). https://doi.org/10.1007/s11082-021-03460-3
  13. Liu, F. et al. Multi-peak detection algorithm based on the Hilbert transform for optical FBG sensing. Opt. Fiber Technol. 45, 47–52 (2018). https://doi.org/10.1016/j.yofte.2018.06.003
  14. Theodosiou, A. et al. Accurate and fast demodulation algorithm for multipeak FBG reflection spectra using a combination of cross-correlation and Hilbert transform. J. Light. Technol. 35, 3956–3962 (2017). https://doi.org/10.1109/JLT.2017.2723945
  15. Chen, Y., Yang, K. & Liu, H.-L. Self-adaptive multi-peak detection algorithm for FBG sensing signal. IEEE Sensors J. 16 2658–2665 (2016). https://doi.org/10.1109/JSEN.2016.2516038
  16. Guo, Y., Yu, C., Yi, N. & Wu, H. Accurate demodulation algorithm for multi-peak FBG sensor based on invariant moments retrieval. Opt. Fiber Technol. 54, 102129 (2020). https://doi.org/10.1016/j.yofte.2019.102129
  17. Li, Hong, et al. Recognition and classification of FBG reflection spectrum under non-uniform field based on support vector machine. Opt. Fiber Technol. 60, 102371 (2020). https://doi.org/10.1016/j.yofte.2020.102371
  18. Nascimento, K. P., Frizera-Neto, A., Marques, C. & Leal-Junior, A. G. Machine learning techniques for liquid level estimation using FBG temperature sensor array. Opt. Fiber Technol. 65, 102612 (2021). https://doi.org/10.1016/j.yofte.2021.102612
  19. Jiang, H., Cheng, J. & Liu, T. Wavelength detection in spectrally overlapped FBG sensor network using extreme learning machine. IEEE Photon. Technol. Lett. 26, 2031–2034 (2014). https://doi.org/10.1109/LPT.2014.2345062
  20. Leal-Junior, A. G.A machine learning approach for simultaneous measurement of magnetic field position and intensity with fiber Bragg grating and magnetorheological fluid. Opt. Fiber Technol. 56, 102184 (2020). https://doi.org/10.1016/j.yofte.2020.102184
  21. Ee, Y.-J. et al. Lithium-ion battery state of charge (SoC) estimation with non-electrical parameter using uniform fiber Bragg grating (FBG). J. Energy Storage 40, 102704 (2021). https://doi.org/10.1016/j.est.2021.102704
  22. Kokhanovskiy, A., Shabalov, N., Dostovalov, A. & Wolf, A. Highly dense FBG temperature sensor assisted with deep learning algorithms. Sensors 21, 6188 (2021). https://doi.org/10.3390/s21186188
  23. Cao, Z., Zhang, S., Liu, Z. & Li, Z. Spectral demodulation of fiber Bragg grating sensor based on deep convolutional neural networks. J. Light Technol. 40, 4429–4435 (2022). https://doi.org/10.1109/JLT.2022.3155253
  24. Manie, Y. Ch. et al. Using a machine learning algorithm integrated with data de-noising techniques to optimize the multipoint sensor network. Sensors 20, 1070, (2020). https://doi.org/10.3390/s20041070
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Authors and Affiliations

Sunil Kumar
1
ORCID: ORCID
Somnath Sengupta
1
  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

Modelling of synchronous generators for the analysis of frequency transients above 1 Hz/s

Alf Assenkamp
Archives of Electrical Engineering | 2022 | vol. 71 | No 3 | 687-700 | DOI: 10.24425/aee.2022.141679
Keywords control system distributed energy systems frequency support generator model RoCoF synchronous generators synchronous machines
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Abstract

Large synchronous generators are of high importance for the stability of power systems. They generate the frequency of the system and stabilize it in case of severe grid faults like trips of large in-feeders or loads. In distributed energy systems, in-feed via inverters will replace this generation in large parts. Modern inverters are capable of supporting grid frequency during severe faults by different means on the one hand. On the other hand, higher Rates of Change of Frequency (RoCoF) after incidents need to be accustomed by future systems. To be able to analyse the RoCoF withstand capability of synchronous or induction generators, suitable models need to be developed. Especially the control and excitation system model need enhancements compared to models proposed in standards like IEEE Std 421.5. This paper elaborates on the necessary modelling depth and validates the approach with example results.
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Authors and Affiliations

Alf Assenkamp
1
  1. Bureau Veritas CPS Germany GmbH, Germany

Construction of the passenger rail traffic generation model

Andrzej Brzeziński Andrzej Waltz
Archives of Civil Engineering | 2022 | vol. 68 | No 3 | 107-123 | DOI: 10.24425/ace.2022.141876
Keywords railway traffic traffic generation model regression analysis traffic forecasting passenger traffic
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Abstract

The article presents a new approach to building a passenger rail traffic generation model. It uses data on the number of passengers at stations and railway stops obtained from the databases of operators on the rail transport market through the Office of Rail Transport – market regulator – combined with data on the model of the area around the station built based on population, number of beds, individual motorization and gross domestic product (GDP). This enabled analyzing the potential of railway traffic generation at a very detailed level. The article presents a methodology for building a passenger rail traffic generation model and verification of this model based on limited variables describing railway stations and stops as well as traffic zones and available statistical data. The model takes into account three segments of the railway market: regional, interregional and inter-agglomeration transport. The results of these analyzes can be used to increase the accuracy and the reliability of rail traffic models used in the analysis of transport networks.
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Authors and Affiliations

Andrzej Brzeziński
1
Andrzej Waltz
2
ORCID: ORCID
  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  2. independent consultant

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