Abstract
Intrusion recognition based on the fiber-optic sensing perimeter security system is a significant method in security technology. Nevertheless, it is of great challenge to distinguish among multitudinous intrusion signals. Many studies have been conducted to solve this problem, which are absolutely dependent on the handcrafted features, and the process of feature extraction is time-consuming and unreliable. In this paper, we present an adaptive intrusion recognition method for ultra-weak FBG signals of perimeter monitoring based on convolutional neural networks. The advantage of the proposed method is its ability to extract optimal vibration features automatically from the raw sensing vibration signals. A fiber-optic sensing perimeter security system was developed to evaluate the computational efficiency of the proposed recognition method. The experiment results demonstrated that the proposed method could recognize the intrusion in the perimeter security system effectively with the best recognition accuracy among all of the comparative methods.
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This work is supported by National Natural Science Foundation of China under grant number 61735013 and 61402345.
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Liu, F., Li, S., Yu, Z., Ju, X., Wang, H., Qi, Q. (2018). Adaptive Intrusion Recognition for Ultraweak FBG Signals of Perimeter Monitoring Based on Convolutional Neural Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_32
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DOI: https://doi.org/10.1007/978-3-030-04221-9_32
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