Huijuan Wu, Xinlei Wang, Haibei Liao, Xiben Jiao, Yiyu Liu, Xinjian Shu, Jinglun Wang, Yunjiang Rao. Signal Processing in Smart Fiber-Optic Distributed Acoustic Sensor[J]. Acta Optica Sinica, 2024, 44(1): 0106009
- Acta Optica Sinica
- Vol. 44, Issue 1, 0106009 (2024)
Fig. 1. Foundation of the next-generation fiber optic Internet of Things—fiber-optic distributed acoustic sensor based on optical communication cable sensing
Fig. 2. Typical DAS system structure and vibration/sound sensing mechanism
Fig. 3. Smart fiber-optic DAS and its signal processing architecture in smart city monitoring applications
Fig. 4. One-dimensional convolution neural network based supervised recognition model[58]
Fig. 5. MS-1D CNN based supervised recognition model[65]
Fig. 6. Recognition model combining multi-scale depth features with temporal relationships[67]
Fig. 7. Temporal sequential relationship among multi-scale deep features based on HMM[67]
Fig. 8. 2D-CNN recognition model with time-frequency input of MFCC[69]
Fig. 9. 2D spectrograms of different types of signals via STFT[59]. (a) Digging; (b) walking; (c) vehicle-passing; (d) damaging
Fig. 10. 2D images of different types of signals via GAF transform[71]. (a) Window partition signal; (b) schematic diagram of Gramian angular difference field (GADF) coding; (c) schematic diagram of Gramian angular summation field (GASF) coding
Fig. 11. Supervised recognition model based on attention mechanism and ResNet-CBAM[72]
Fig. 12. Time-space waterfall figures of vibration signals due to various third-party interference[73]. (a) Excavator; (b) electrical hammer; (c) shovel; (d) hammer; (e) pickaxe; (f) metro
Fig. 13. Recognition result fusion correction based on time-space label matrix[73]
Fig. 14. Supervised recognition model combining convolutional neural network and bi-directional long short term memory[75]
Fig. 15. Supervised recognition model based on dual path network[80]. (a) Dual path architecture; (b) equivalent block of RP; (c) equivalent block of DCP
Fig. 16. Supervised recognition model based on attention-based long short-term memory network[81]
Fig. 17. DAS recognition model based on fusion of manual features and deep features[83]
Fig. 18. SNN-based DAS unsupervised learning network[87]
Fig. 19. DAS transfer learning network based on AlexNet+SVM[89]
Fig. 20. ROC curves for different models[72]
Fig. 21. Multi-source aliasing phenomenon in complex urban environment[93]
Fig. 22. DAS multi-source separation method based on Fast ICA[93]
Fig. 23. Multi- radial-distance event classification method based on deep learning[100]
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Table 1. Confusion matrix of binary classification
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Table 2. Comparison of key DAS signal recognition algorithms and their performance
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