Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel method for multi-position load recognition using multimode fiber (MMF) specklegram sensors. The approach achieves nearly 100% accuracy in identifying load positions and magnitudes, offering efficient distributed sensing.
Area Of Science
- Photonics and Optical Sensing
- Machine Learning for Sensor Systems
- Distributed Fiber Optic Sensing
Background
- Simultaneous measurement of multiple perturbation positions and intensities in multimode fiber (MMF) distributed sensors is challenging due to large specklegram data requirements.
- Existing methods face difficulties in recognizing multiple perturbations efficiently, hindering the development of high-resolution distributed sensing applications.
Purpose Of The Study
- To propose and validate a novel approach for recognizing multi-position loads using MMF specklegram sensors.
- To enhance the sample diversity and robustness of MMF-distributed sensor recognition models.
- To provide a cost-effective and efficient solution for high-resolution distributed measurements.
Main Methods
- Development of a multi-variable, multi-class, one-shot specklegram dataset construction method.
- Theoretical derivation of the mathematical model for total local intensity and its sensitivity to perturbations.
- Implementation of specklegram traversal occlusion data augmentation with a shallow convolutional neural network (CNN).
Main Results
- The proposed method achieves nearly 100% accuracy in simultaneously recognizing load positions and magnitudes for up to 1545 distinct load forms.
- The shallow CNN model demonstrates superior training efficiency and stability compared to existing MMF sensing models.
- Experimental validation confirms the effectiveness of the approach for distributed sensing applications.
Conclusions
- The study presents a proof of concept for a distributed MMF specklegram sensor capable of high-resolution measurements under diverse perturbations.
- The developed method significantly advances MMF-based distributed sensing by offering enhanced accuracy and efficiency.
- This approach provides a promising, cost-effective solution for various distributed sensing applications.
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