This paper proposes a multi-radial-distance event classification method based on deep learning. To the best of our knowledge, this is the first time that the -OTDR can tell how far the target event from the sensing fiber is through deep learning approach. The temporal-spatial data matrix collected by the system is filtered by three different band-pass filters to form RGB images as the input of the Inception_V3 network trained by ImageNet dataset. The passband of three band-pass filters is selected by searching the maximum Euclidean distance in the frequency domain. Three kinds of filters with different frequency bands enhance the effective features of data samples in advance. The simulated annealing (SA) algorithm is applied to search the maximum Euclidean distance. Field experiment includes five kinds of events with four different radial distances, where there are 17 subclasses in total, has been carried out. The classification results show that the classification accuracy reaches 86% and the method can tell both the event type and radial distance.
Thanks to the development of machine learning and deep learning, data-driven pattern recognition based on neural network is a trend for Φ-OTDR system intrusion event recognition. The data-driven pattern recognition needs a large number of samples for training. However, in some scenarios, intrusion signals are difficult to collect, resulting in the lack of training samples. At the same time, labeling a large number of samples is also a very time-consuming work. This paper presents a few-shot learning classification method based on time series transfer and cycle generative adversarial network (CycleGAN) data augmentation for Φ-OTDR system. By expanding the rare samples based on time series transfer and CycleGAN, the number of samples in the dataset can finally meet the requirement of network training. The experimental result shows that even when the training set has two minor classes with only two samples, the average accuracy of the validation set with 5 classification tasks can still reach 90.84%, and the classification accuracy of minor classes can reach 79.28% with the proposed method.
The title compound, C9H11BrO2S, is an important intermediate in the synthesis of the herbicide Topramezone. In the crystal, there are weak intermolecular Br⋯O interactions of 3.286 (4) Å. The dihedral angle between the plane of the benzene ring and that defined by the O—S—O atoms of the methanesulfonyl group is 49.06 (3)°.
Different signal representations show different unique features for classification. In this paper, a feature fusion method with attention mechanism based on multiple signal representations is proposed for Φ-OTDR event classification with buried optical fiber. Each signal representation is fused after feature extraction to get richer and better features. With the help of a layer pruning method based on attention mechanism, the network size can be kept and avoid computation increase. Experiment results show that this method with 3 signal representations can improve the recognition accuracy to 97.93%, with 3.52% improvement compared to single representation approach. It also shows higher recognition accuracy than the tradition multiple signal representations fusion methods at the input stage. Furthermore, when it is used to fuse four representations, the recognition accuracy can be further improved to 99.11%.
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