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Computational Neuroscience Applied in Surface Roughness Fiber Optic Sensor.

Wei He1

  • 1School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.

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Summary
This summary is machine-generated.

This study presents a novel method using a radial basis function neural network to process surface roughness fiber optic sensor signals. This approach enhances accuracy and reduces laser intensity effects for improved surface measurements.

Keywords:
Computational NeuroscienceOptical fiberRadial basis functionSensorSurface roughness

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Area of Science:

  • Computational Neuroscience
  • Optical Sensing Technologies

Background:

  • Fiber optic sensors are crucial for signal output in various applications.
  • Processing sensor signals, especially from surface roughness sensors, presents challenges due to non-linearities and environmental factors like laser intensity fluctuations.

Purpose of the Study:

  • To introduce an effective method for processing surface roughness fiber optic sensor output signals.
  • To achieve non-linear compensation and reduce the impact of laser intensity variations simultaneously.
  • To enhance the performance and applicability of surface roughness fiber optic sensors.

Main Methods:

  • Utilized a radial basis function (RBF) neural network for signal processing.
  • Integrated both the sensor output signal and the laser intensity signal as inputs to the RBF neural network.
  • Leveraged the RBF neural network's capability for non-linear function approximation.

Main Results:

  • The developed method demonstrated effective non-linear compensation for the sensor.
  • Successfully reduced the influence of changes in laser output light intensity.
  • The Surface Roughness Fiber Optic Sensor showed a large measuring range, high accuracy, and good repeatability.
  • Enabled measurements on challenging surfaces like small areas and the bottom surfaces of holed objects.

Conclusions:

  • The RBF neural network approach offers a robust solution for surface roughness fiber optic sensor signal processing.
  • This method lowers the requirement for laser output power stability, making the sensor more practical.
  • The technique significantly improves sensor performance, accuracy, and versatility for specialized surface measurements.