Abstract
A system to process seismic signals of vehicles passing between two sensor stations had been developed and experimented. To evaluate the feasibility of the system before field test with a real vehicle and to support the classification model with artificial data later, the input seismic data were simulated from Green’s method function that accounts only for Rayleigh surface wave. The system using the Machine Learning Classification method SVM to classify data collected from two stations at any time have the state of passed or not. By processing the signal, the system could detect whether the vehicle had passed the crossing line or not with the accuracy of 99.10% for simulated data and 94.22% for experiment data. The experiment and results suggested that processing seismic signals to monitor control lines is feasible.
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Acknowledgment
We would like to express our gratitude to the staff members in the Department of Precision Mechanical and Optical Engineering, Hanoi University of Science and Technology, Vietnam for supporting the research. We also would like to show our gratitude toward Mr. Anh Nguyen, Mr. Binh Nguyen and Mr. Phuong Le for their help in building the hardware for the experiment.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Duong Nhat, T., Nguyen Thi Phuong, M. (2019). Analyzing Seismic Signal Using Support Vector Machine for Vehicle Motion Detection. In: Duong, T., Vo, NS. (eds) Industrial Networks and Intelligent Systems. INISCOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-05873-9_15
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DOI: https://doi.org/10.1007/978-3-030-05873-9_15
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