首页|A deep learning model enabled multi-event recognition for distributed optical fiber sensing
A deep learning model enabled multi-event recognition for distributed optical fiber sensing
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万方数据
维普
Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades.However,these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed.Traditional processing schemes are no longer adequate,so the development of novel signal processing methods is critical.Phase-sensitive optical time domain reflectometry(Φ-OTDR)is now applied in various applications for multi-event recognition,and it would usually be difficult,sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature.To fully take advantage of the information contained within the large number of unlabeled samples,which were formerly not utilized and hence wasted,we propose a semi-supervised model to boost the event classification performance of Φ-OTDR.The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism.Its classification accuracy has been improved up to 96.9％with only 1230 labeled samples.In addition,our model shows significant advantages when the number of labeled samples is reduced.Importantly,our method improves the accuracy of multi-event classification without any modification to the optical setup.
万方数据
维普
