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Related Experiment Video

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition.

Hongquan Qu1, Tingliang Feng2, Yuan Zhang1

  • 1School of Information Science and Technology, North China University of Technology, Beijing 100144, China.

Sensors (Basel, Switzerland)
|July 31, 2019
PubMed
Summary
This summary is machine-generated.

Stochastic configuration networks (SCN) were enhanced with ensemble methods to improve optical fiber vibration signal recognition in noisy conditions. AdaBoost-SCN and AdaBoost-Bootstrap-SCN demonstrated superior performance in identifying these critical signals.

Keywords:
AdaBoostbootstrap samplingnoisy optical fiber vibration signal recognitionoptical fiber pre-warning systemstochastic configuration network

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Area of Science:

  • * Sensor Technology
  • * Signal Processing
  • * Machine Learning

Background:

  • * Optical fiber pre-warning systems (OFPS) are crucial for infrastructure protection, like oil and gas pipelines.
  • * Accurate recognition of fiber vibration signals is essential for OFPS effectiveness.
  • * Environmental and mechanical noise significantly degrades vibration signal recognition.

Purpose of the Study:

  • * To evaluate the impact of various noise types on vibration signal recognition using stochastic configuration networks (SCN).
  • * To develop and compare ensemble learning methods (Bootstrap, AdaBoost) combined with SCN for enhanced noisy signal recognition.

Main Methods:

  • * Implementation of stochastic configuration network (SCN) for vibration signal recognition.
  • * Superimposition of multiple noise types (Gaussian, uniform, Rayleigh, exponential) onto vibration signals.
  • * Development and comparison of Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN models.

Main Results:

  • * AdaBoost-based classifiers consistently outperformed other methods across all noise levels.
  • * AdaBoost-Bootstrap-SCN exhibited the best recognition performance for noisy vibration signals.
  • * Ensemble learning significantly improved the robustness of SCN against noise interference.

Conclusions:

  • * Ensemble learning, particularly AdaBoost, is highly effective in enhancing SCN for noisy vibration signal recognition.
  • * AdaBoost-Bootstrap-SCN offers a robust solution for improving the reliability of optical fiber pre-warning systems in challenging environments.