Footsteps detection and identification based on distributed optical fiber sensor and double-YOLO model.

A footstep detection and recognition method based on distributed optical fiber sensor and double-YOLO method is proposed. The sound of footsteps is detected by a phase-sensitive optical time-domain reflectometry (Φ-OTDR) and the footsteps are located and identified by double-YOLO method. The Φ-OTDR can cover a much larger sensing range than traditional sensors. Based on the stride and step frequency of the gait, the double-YOLO method can determine the walker's ID. Primary field experiment results show that this method can detect, locate and identify the footsteps of three persons, and achieve about 86.0% identification accuracy, with 12.6% accuracy improvement compared to single-YOLO method. This footstep detection and recognition method may promote the development of gait-based clinical diagnosis or person identification application.

Event recognition method based on dual-augmentation for a Φ-OTDR system with a few training samples.

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.

Multi-signal feature fusion method with an attention mechanism for the Φ-OTDR event recognition system.

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%.

Crystal structure of 1-bromo-4-methane-sulfonyl-2,3-di-methyl-benzene.

The title compound, C9H11BrO2S, is an important inter-mediate in the synthesis of the herbicide Topramezone. In the crystal, there are weak inter-molecular Br⋯O inter-actions 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 methane-sulfonyl group is 49.06 (3)°.