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An intrusion recognition method based on the combination of One-dimensional CNN and DenseNet with DAS system

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Abstract

An intrusion recognition method based on the combination of one-dimensional convolution neural network (1-D CNN) and DenseNet network is proposed and demonstrated. Assisted with fiber optic distributed acoustic sensing (DAS) system, the average recognition accuracy of 98.4% for six types of events and 2ms processing time were achieved in the field test.

© 2021 The Author(s)

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