首页|基于跨模型知识蒸馏的φ-OTDR模式识别

基于跨模型知识蒸馏的φ-OTDR模式识别

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针对分布式光纤模式识别高精度高效率的要求,提出一种基于跨模型知识蒸馏的相位敏感光时域反射仪模式识别方法.该方法以hierarchical token-semantic audio transformer为教师模型、broadcasting-residual network为学生模型,使得参数量较少的学生模型获得架构相异的transformer类网络的识别性能.实验中使用相敏光时域反射仪作为信号采集设备,在实际工程场景下采集攀爬防护网、背景噪声、敲击防护网和大风等4种类别的信号作为数据集.和典型的深度学习算法相比,改进后的算法准确率最优且收敛最快,识别效率较高,在工程应用上具有广阔前景.
Pattern Recognition of φ-OTDR Based on Cross-Model Knowledge Distillation
Herein,a phase-sensitive optical time domain reflectometer(φ-OTDR)pattern recognition method based on cross-model knowledge distillation is introduced to meet the demands for high precision and efficiency in distributed fiber optic pattern recognition.This method employed hierarchical token-semantic audio transformer as the teacher model and broadcasting-residual network as the student model.This setup enables the student model to achieve recognition performance comparable to transformer-like networks with disparate architectures using less parameters.For practical engineering experiments,a φ-OTDR was used as the signal acquisition device.The dataset used in practical engineering scenarios included signals from four categories,such as climbing nets,background noises,striking nets,and wind noises.Compared to typical deep learning algorithms,this improved algorithm demonstrates superior accuracy and faster convergence,resulting in higher recognition efficiency and offering considerable potential for engineering applications.

distributed fiber optic sensingcross-model knowledge distillationpattern recognitionbroadcasting-residual network

陈帅、厉小润、李东明、王晶

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浙江大学电气工程学院,浙江 杭州 310027

中国船舶集团有限公司第七一五研究所,浙江 杭州 310023

分布式光纤传感 跨模型知识蒸馏 模式识别 广播残差网络

2024

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子:1.153
ISSN:1006-4125
年,卷(期):2024.61(13)