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http://dx.doi.org/10.7472/jksii.2020.21.2.27

A Study on the Anomaly Prediction System of Drone Using Big Data  

Lee, Yang-Kyoo (Division of Information and Telecommunication, Hanshin University)
Hong, Jun-Ki (Department of Computer Engineering, Paichai University)
Hong, Sung-Chan (Division of Information and Telecommunication, Hanshin University)
Publication Information
Journal of Internet Computing and Services / v.21, no.2, 2020 , pp. 27-37 More about this Journal
Abstract
Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.
Keywords
Big Data; Drones; Risk Prediction;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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