Paper
26 July 2022 Pattern recognition using multi-dimensional hybrid feature extraction scheme in optical fiber distributed vibration sensing system
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Proceedings Volume 12279, 2021 International Conference on Optical Instruments and Technology: Optical Sensors and Applications; 122790B (2022) https://doi.org/10.1117/12.2616437
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only
Abstract
A multi-dimensional hybrid feature extraction scheme for optical fiber distributed vibration sensing system has been proposed in this work. Firstly, the hybrid features are extracted through using zero-crossing rate, wavelet packet energy entropy, empirical mode decomposition based kurtosis, skewness and multiscale permutation entropy. Secondly, an effective feature vector are built based on all the above-mentioned features. Finally, the established features are classified and recognized by a radial basis foundation neural network. A series of experiments have been implemented to validate the effectiveness of the proposed scheme. Results show that the proposed scheme can efficiently and accurately identify five common sensing patterns. Specifically, the average recognition accuracy of 99.2 % is achieved and the response time can be limited less than 1 s. Therefore, it is believed that the proposed pattern recognition scheme has a good potential in the application of the optical fiber distributed vibration sensing system.
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Kang Xue, Kun Liu, Junfeng Jiang, Zhenshi Sun, Yuelang Huang, Sichen Li, Xibo Jin, and Tiegen Liu "Pattern recognition using multi-dimensional hybrid feature extraction scheme in optical fiber distributed vibration sensing system", Proc. SPIE 12279, 2021 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, 122790B (26 July 2022); https://doi.org/10.1117/12.2616437
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KEYWORDS
Feature extraction

Sensing systems

Optical fibers

Wavelet packet decomposition

Wavelets

Sensors

Fiber optics

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