• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.477-485
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• 2019

As the drones industry evolved, the security of the drone network has been important. In this paper, MTD (Moving Target Detection) technique is applied to the drone network for improving security. The existing MTD scheme has a risk that the hash value is exposed during the wireless communication process, and it is restricted to apply the one-to-many network. Therefore, we proposed PHT (Preposition Hash Table) scheme to prevent exposure of hash values during wireless communication. By reducing the risk of cryptographic key exposure, the use time of the cryptographic key can be extended and the security of the drone network will be improved. In addition, the cryptographic key exchange is not performed during flight, it is advantageous to apply PHT for a swarm drone network. Through simulation, we confirmed that the proposed scheme can contribute to the security of the drone network.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.967-978
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• 2023

This study proposed and validated a combined segmentation network (CSN) designed to effectively train on multiple drone image datasets and enhance the accuracy of semantic segmentation. CSN shares the entire encoding domain to accommodate the diversity of three drone datasets, while the decoding domains are trained independently. During training, the segmentation accuracy of CSN was lower compared to U-Net and the pyramid scene parsing network (PSPNet) on single datasets because it considers loss values for all dataset simultaneously. However, when applied to domestic autonomous drone images, CSN demonstrated the ability to classify pixels into appropriate classes without requiring additional training, outperforming PSPNet. This research suggests that CSN can serve as a valuable tool for effectively training on diverse drone image datasets and improving object recognition accuracy in new regions.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.38-44
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• 2017

Currently, wired gables have been installed or portable storage devices have been installed for data acquisition of flying drone. In this paper, we propose a technology to transmit data wirelessly by sensing information such as battery discharge value, acceleration, and temperature by attaching RF sensor to a drone. The purpose of this paper is to design and develop the monitoring technology of agriculture drone battery usage in real time using RF sensor. In this paper, we propose a monitoring system that can check real time data of battery changed value, temperature, and acceleration during pesticide control activity of agricultural drone.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.8
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• pp.915-920
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• 2015

Recently, many companies try to offer various services using drones. Especially, the drone delivery system is a good example. However, the drone delivery service has some problems since the heavy parcels flies over the people walking down streets, so many things must be considered such as dropping mails by collision of drones. To resolve the problem, in this paper, a inter-drone network communication will be used to design the topology and to simulate in the Opnet simulator for the performance evaluation. Additionally, the topology with random mobility of trajectory of drones is also designed and simulated for the result.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.77-81
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• 2018

Many studies that improve the efficiency of data collection and a network's lifetime by using a mobile sink have been conducted using wireless sensor networks. If a drone is used as a mobile sink, the drone can collect data more efficiently than can existing mobile sinks operating on the ground because the drone can minimize the effects of obstacles and the terrain. In this paper, we propose a rendezvous node selection scheme which considers estimated drone's trajectory and data collection latency of sensor networks for reliable data collection, when a drone whose trajectory is not predetermined works with terrestrial wireless sensor networks. A selected rendezvous node on the ground collects data from the entire network and it sends then collected data to the drone via direct communication. We also verify that the proposed scheme is more reliable than previous schemes without considering the drone's trajectory and data collection latency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.936-942
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• 2019

Autonomous driving of drone indoor must move along a narrow path and overcome other factors such as lighting, topographic characteristics, obstacles. In addition, it is difficult to operate the drone in the hallway because of insufficient texture and the lack of its diversity comparing with the complicated environment. In this paper, we study an autonomous drone navigation using Convolution Neural Network(CNN) in indoor environment. The proposed method receives an image from the front camera of the drone and then steers the drone by predicting the next path based on the image. As a result of a total of 38 autonomous drone navigation tests, it was confirmed that a drone was successfully navigating in the indoor environment by the proposed method without hitting the walls or doors in the hallway.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.461-466
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• 2021

Drone can be controlled by the method such as Bluetooth communication for close distance and can be controlled through network communication for long distance. Especially, the coordinate is set using GPS and drone is controlled using network communication and video communication when the activity range is long distance. However, the drone should be controlled by receiving control authority accordingly in response about it appropriately when the drone leaves the control area after arriving at the destination if there is a problem with network communication and video communication. So, this study proposes a method to control a drone with a simple mutually promised simple gesture and the drone can be controlled in the proposed method even if the drone leaves from the control authority in above situation. The reference posture was established for mutually promised simple gesture algorithm and automatically handed over the control authority of drone to a person who takes the reference posture when the drone recognizes it to implement this. And all the movements of the drone could be controlled by starting the beginning of all commands from the reference posture (The hovering posture of the drone). Lastly, the control authority of the drone should be returned after achieving the purpose, and the algorithm was implemented to make the drone can perform next action of its own, and it was confirmed that the drone was operating normally by the mapped instruction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.156-164
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• 2019

In this paper, we propose an efficient dynamic slot re-assignment scheme for swarming drone networks in which networks members merged and split frequently. The leader drone of each network recognizes the slot allocation information of neighbor networks by periodic information exchange among between the drones. Using the information, the leader drone makes a decision how to reallocate the slots between members in case of network merge. The non-competitive method in the proposed scheme can re-assign the slots without any slot collision and shows always superior performance than competitive scheme. The competitive method in the proposed scheme reduces the number of slots that should be re-assigned in case of network merge. The experimental performance analysis shows that the proposed scheme performs better or at least equal to the performance of the competitive scheme in a swarming drone network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.167-172
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• 2017

Mobile nodes are used for prolonging the life-time of the entire wireless sensor networks and many studies that use drones to collected data have been actively conducted with the development of drone related technology. In case of associating a drone and tactical wireless sensor networks, real-time feature and efficiency are improved. The previous studies so focus on reducing drone's flight distance that the energy consumption of sensor nodes is unbalanced. This unbalanced energy consumption accelerates the network partition and increases drone's flight distance. In this paper, we proposed a new selection scheme considered drone's flight distance and nodes' life-time to solve this problem when rendezvous nodes that collect data from their cluster and directly communicate with a drone are selected.