AI in EE

AI IN DIVISIONS

AI in Communication Division

AI in EE

AI IN DIVISIONS

AI in Communication Division ​

AI in Communication Division

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach

Authors : Sangseok Yun, Jae-Mo Kang, Jeongseok Ha, Sangho Lee, Dong-Woo Ryu, Jihoe Kwon and Il-Min Kim

Journal : IEEE Transactions on Geoscience and Remote Sensing Letters (published: March 2021)

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

Fig. 1 Structure of the proposed ground vibration classification scheme.

Fig. 2. Performance of the proposed deep reinforcement learning-based energy management scheme.

Fig. 3. Performances of the proposed ground vibration classification scheme and the end-to-end ground vibration monitoring system.

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

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Copyright ⓒ 2015 KAIST Electrical
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