Related Experiment Video
Updated: Jan 10, 2026
09:43
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
Published on: March 20, 2017
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Multi-Feature Fusion for Fiber Optic Vibration Identification Based on Denoising Diffusion Probabilistic Models.
Keju Zhang1,2, Tingshuo Wang1,2, Jianwei Wu3
1College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|November 27, 2025
Summary
A new DR-LSTM method enhances fiber optic vibration recognition by combining image and temporal features. This approach improves accuracy, especially for imbalanced datasets, crucial for structural health assessment and security surveillance.
Area of Science:
- Engineering
- Signal Processing
- Artificial Intelligence
Background:
- Current fiber optic vibration identification methods struggle to integrate both image and temporal features.
- This limitation hinders performance, particularly with uneven data distributions, impacting applications like structural health assessment.
Purpose of the Study:
- To propose a novel fiber optic vibration recognition method, DR-LSTM, that effectively integrates image and temporal features.
- To ensure high recognition accuracy for both balanced and imbalanced data distributions.
Main Methods:
- Integration of residual neural networks, long short-term memory networks, and diffusion denoising probabilistic models.
- Extraction of Mel spectrum image features and temporal characteristics.
- Development of specialized neural network models for data augmentation in scarce data categories.
Main Results:
- The DR-LSTM model demonstrated improved classification accuracy on balanced datasets (0.67% and 7.4% increase).
- On imbalanced datasets, DR-LSTM achieved significant accuracy gains (minimum 18.79% and 2.4% increase).
Conclusions:
- The DR-LSTM method is effective and feasible for enhancing fiber optic vibration recognition accuracy.
- The proposed approach successfully addresses challenges posed by imbalanced data distributions in vibration identification.
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