• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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A Study on the Anomaly Prediction System of Drone Using Big Data


Yang-Kyoo Lee, Jun-Ki Hong, Sung-Chan Hong, Journal of Internet Computing and Services, Vol. 21, No. 2, pp. 27-37, Apr. 2020
10.7472/jksii.2020.21.2.27, Full Text:  HTML
Keywords: Big data, Drones, Risk Prediction

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.


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Cite this article
[APA Style]
Lee, Y., Hong, J., & Hong, S. (2020). A Study on the Anomaly Prediction System of Drone Using Big Data. Journal of Internet Computing and Services, 21(2), 27-37. DOI: 10.7472/jksii.2020.21.2.27.

[IEEE Style]
Y. Lee, J. Hong, S. Hong, "A Study on the Anomaly Prediction System of Drone Using Big Data," Journal of Internet Computing and Services, vol. 21, no. 2, pp. 27-37, 2020. DOI: 10.7472/jksii.2020.21.2.27.

[ACM Style]
Yang-Kyoo Lee, Jun-Ki Hong, and Sung-Chan Hong. 2020. A Study on the Anomaly Prediction System of Drone Using Big Data. Journal of Internet Computing and Services, 21, 2, (2020), 27-37. DOI: 10.7472/jksii.2020.21.2.27.