• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.211-219
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• 2020

One of the major advancements of the Fourth Industrial Revolution is the use of Internet of Drones (IoD), which combines the Internet of Things (IoT) and drone technology. IoD technology is especially important for efficiently and economically operating C4ISR operations in actual battlefields supporting various combat situations. The purpose of this study is to solve the problems of limited battery capacity of drones and lack of budgeting criteria for military drone transcription, introduction, and operation. If the mission area is defined and corresponding multi-drone hovering check points and mission completion time limits are set, then an energy and time co-optimized scheduling and operation control scheme is needed. Because such a scheme does not exist, in this paper, a Drone Force Deployment Optimization (DFDO) scheme is proposed to help schedule multi-drone operation scheduling and networked based remote multi-drone control.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.163-169
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• 2021

In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.31-37
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• 2019

In this paper, we propose an approach to get the requirement of drone acquisition for the efficient dronebot combat system using brute force algorithm. We define parameters, such as width, depth, and important surveillance area for the surveillance mission in the Army battalion and company units based on real military operation environment and brute force algorithm with 4 steps including first, next, valid, output is applied to get the requirement of drone acquisition and each drone's path planning using computer simulation. As a result, we could get the requirement of drone acquisition and each drone's path planning, the Army could utilize our proposed approach in the Army dronebot combat system. In the future research, we will study on the reliability of our proposed approach to get the requirement of drone acquisition for the efficient dronebot combat system.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.33-38
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• 2024

In this paper, we propose the implementation of a drone delivery system using artificial intelligence in a situation where the use of drones is rapidly increasing and human errors are occurring. This system requires the implementation of an accurate control algorithm, assuming that last-mile delivery is delivered to the apartment veranda. To recognize the delivery location, a recognition system using the YOLO algorithm was implemented, and a delivery system was installed on the drone to measure the distance to the object and increase the delivery distance to ensure stable delivery even at long distances. As a result of the experiment, it was confirmed that the recognition system recognized the marker with a match rate of more than 60% at a distance of less than 10m while the drone hovered stably. In addition, the drone carrying a 500g package was able to withstand the torque applied as the rail lengthened, extending to 1.5m and then stably placing the package down on the veranda at the end of the rail.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.23-39
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• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.825-836
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• 2019

The drone-type base station will be an optimal platform as a way of information sharing for efficient operation of the military force due to their high network flexibility. It is expected that the characteristics of the drone-type base station which would freely adjust the altitude can be used to offset the propagation attenuation characteristics of the millimeter-wave frequency band by securing the stable Line of Sight. In this paper, we proposed a framework for evaluation drone-type base station that can be utilized as a future military communication network by performing modeling for performance analysis that can reflect various factors.

• Smart Media Journal
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• v.5 no.2
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• pp.51-58
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• 2016

Recently, the unmanned aerial vehicle Drone technology is attracting new interest around the world. The versatilities in science, military, marketing, sports, and entertainment fields are the driving force of the drone fever. Thus, the potential power of future industrial is expected as the application range is extensive. In this paper, we design and propose the prototype of unmanned aerial vehicle-based bigdata processing system.

• Smart Media Journal
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• v.8 no.4
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• pp.38-45
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• 2019

When there is a spray flow such as from a pesticide nozzle, winds affect the droplet flow of a rotary-wing drone accompanied by a strong wake, with a severe oscillation. Especially, during forwarding flights or when winds come from the side, compare to a simple hovering flight as the droplet is in the effect of aerodynamic drag force, the effect of spraying region becomes even larger. For this reason, the spraying of pesticides using drones may cause a greater risk of scattering or a difference in droplet dispersion between locations, resulting in a decrease in efficiency. Therefore, through proper numerical modeling and its applied simulation, an indication tool is required applicable for the various flight and atmospheric conditions. In this research, we completed both experiment and numerical analysis for the strong downwash from the rotor and flight velocity of the drone by comparing the probability density function of droplet distribution to build a spraying system that can improve the efficiency when spraying droplets in the pesticide spray drone.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.285-291
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• 2023

In this paper, in order to secure the safety and productivity of pine cone harvest, modeling and structural analysis of the hammering system for pine cone harvest drone that can easily access pine cone of Pinus koraiensis and collide with them to harvest them was performed. It calculate the equivalent stress for the structure of the hammering system and the yield strength of the applied material by applying the shear force of the stalk at which the pine cone is separated from the branch, and it is to verify the safety of the structure and propose an optimal design through appropriate factor of safety and design change. The shear force of the stalk at which the pine cone was separated from the branch was 468 N, and was applied to both ends of the hammering system. The yield strength of SS400 steel used in the hammering system is 245 ㎫, and the design change and structural analysis were performed so that the Von Mises stress could be less than 122.5 ㎫ by applying the factor of safety of 2.0 or more. As a result of the structural analysis of the frist modeling, the Von Mises stress was 220.3 ㎫, the factor of safety was 1.12, and the stress was concentrated in the screw fastening holes. As a result of the design change of the screw fastening holes, the Von Mises stress was 169.4 ㎫, the factor of safety was 1.45, and the stress was concentrated on the side part. As a result of the design change by changing screw fastening holes and adding ribs, the Von Mises stress was 121.6 ㎫, and the factor of safety was 2.02. The safety of the hammering system was secured with an optimal design with little change in mass. There was no deformation or damage as a result of experimenting on pine cone harvest by manufacturing the hammering system with an optimal design.