Probability machine-learning-based communication and operation optimization for cloud-based UAVs
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Probability machine‑learning‑based communication and operation optimization for cloud‑based UAVs Hyeok‑June Jeong1 · Suh‑Yong Choi1 · Sung‑Su Jang1 · Young‑Guk Ha1
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract This paper proposes a smart machine-learning-based unmanned aerial vehicle (UAV) control system that optimizes UAV operations in real time. The purpose of this system is to increase the efficiency of UAVs that need to operate with limited resources. This can be accomplished by allowing the UAVs in flight to identify their current state and respond appropriately. The proposed system, which is developed based on “cloud robotics,” benefits from the powerful computational capabilities of cloud computing and can therefore calculate many types of information received from various sensors in real time to maximize the performance of the UAV control system. The system learn about normal situations when creating models. That is, preprocessing data that is correlated with a particular situation and modeling it with a “multivariate Gaussian distribution.” Once the model is created, the UAV can be used to analyze the current situation in real time during flight. Of course, it is possible to recognize the situation based on the traditional RULE or the latest LSTM. However, this is not an appropriate solution for UAV situations where irregularities are severe and unpredictable. In this paper, we succeeded in recognizing the UAV flight status in real time by the proposed method and succeeded in optimizing it by adjusting communication cycle based on a recognized situation. Based on the results of this study, we expect to be able to stabilize and optimize systems that are highly irregular and unpredictable. In other words, this system will be extended to learn about various situations and create a model. A reliable and efficient smart system can be designed by judging the situation comprehensively. Keywords UAV security · Probability machine learning · Anomaly detection · Safety critical system
* Young‑Guk Ha [email protected]; [email protected] Extended author information available on the last page of the article
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1 Introduction Lately, unmanned aerial vehicles (UAVs) have been used in various activities such as hobbies, video recording, reconnaissance, and surveillance. The popularity of UAVs has increased as a result of their small size and the fact that they can be produced cost effectively. This is because they do not require cockpit space or highly reliable extra equipment to ensure the survival and safety of their pilots. This is because the pilot can control the UAV from the ground for a variety of purposes as a result of the development of sensors and communication technology. In general, however, UAVs are considered to be less reliable than manned aircraft such as airplanes and helicopters. One of the main reasons for this is the fact that UAV pilots cannot directly observe the state of the vehicle during flight. Consequently, it is
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