A Deep CNN-Based Ground Vibration Monitoring Scheme for MEMS Sensed Data

Ground vibration monitoring with microelectromechanical systems (MEMS) sensors is very effective and promising for alerting geological disasters. In this letter, explicitly considering and effectively addressing several specific issues related to practical MEMS sensors, we develop a novel ground vib...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-02, Vol.17 (2), p.347-351
Main Authors: Kang, Jae-Mo, Kim, Il-Min, Lee, Sangho, Ryu, Dong-Woo, Kwon, Jihoe
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Convolution
Convolutional neural network (CNN)
Data
Datasets
deep learning
Disasters
Feature extraction
Geological hazards
Ground motion
ground vibration
Microelectromechanical systems
microelectromechanical systems (MEMS)
Micromechanical devices
Monitoring
Neural networks
sensed data
Sensor phenomena and characterization
Sensors
System effectiveness
Vibration
Vibration monitoring
Vibrations
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Summary:Ground vibration monitoring with microelectromechanical systems (MEMS) sensors is very effective and promising for alerting geological disasters. In this letter, explicitly considering and effectively addressing several specific issues related to practical MEMS sensors, we develop a novel ground vibration monitoring scheme for MEMS sensed data based on a deep convolutional neural network (CNN). Experiments are then conducted on the synthetic and real data sets. Experimental results on both data sets demonstrate that the proposed scheme significantly outperforms the other comparable schemes. For the synthetic data set, the proposed scheme achieves a very high overall accuracy of 98.82%. Also, for the real data set, the proposed scheme achieves a high overall accuracy of 81.64%, which is about 7% higher than that reported in the literature.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2918641