• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.189-195
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• 2024

K-drone delivery system is a commercial delivery system that ensures compliance with aviation safety. It is equipped with flight routes for automated deliveries and features a drone identification device along with a real-time monitoring system for drone positions. This study proposes the application of an ICAO SMS-based safety management system as an appropriate safety management framework that non-aviation personnel drone operators can adopt. It suggests that applying an ICAO SMS-based safety management system to the K-drone delivery system, similar to Europe and the USA, is valid. To introduce ICAO SMS into the K-drone delivery system, procedures must be established for building a drone safety management system, from business registration to drone operation and training. The main components of the drone safety management system are presented, including 8 items: safety policy and authority and responsibility, risk management, safety assurance, safety promotion, drone control system, drone delivery operation manual, drone maintenance manual, and accident response, incorporating the four fundamental elements of ICAO SMS and European Light UAV operator Certificate standards. Additionally, as a core aspect of the drone safety management system, a seven-step risk assessment procedure is proposed, which includes basic risk identification, hazard identification, risk scenario development, calculation of severity and likelihood, SMS matrix risk assessment, mitigation measures, and implementation determination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1929-1952
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• 2024

The development of drones is transforming society as a whole and playing a game-changing role in warfare. However, numerous problems pose threats to the lives and safety of people, and the counter-drone system lags behind the rapid development of drones. Most countries, including South Korea, have not established a reliable counter-drone system in response to the threat posed by numerous drones. Due to budget constraints in each country, an Analytic Hierarchy Process (AHP) analysis was conducted among a group of experts who have been involved in policymaking and research and development related to counter-drone systems. This analysis aimed to determine the priority of building a counter-drone system. Based on various research data, the counter-drone system was analyzed in three stages: detection/identification, governance, and response. The hierarchical design mapped out the existing researched counter-drone technology into a hierarchical model consisting of 31 evaluation criteria. The conclusion provided a roadmap for establishing a counter-drone system based on the prioritization of each element and considering factors such as technological advancement, outlining directions for development in each field.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.23-36
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• 2021

Recently, the rapid development of drones is increasing as a variety of threats to important facilities of the country. In order to build an anti-drone system that responds to drones with high technical characteristics, standardization is required in terms of operation, system, and technology. By applying the defense architecture framework, it contributes to the establishment of the optimal system by proposing a standardization plan for the operational and system perspectives of the anti-drone system by creating outputs equivalent to the stage of prior research on weapons systems. It is a prerequisite for building a drone system the operational concept of the anti-drone system, the definition of the drone threat, the function of each component, the interface, the definition of data flow, the system performance and effect scale, etc. Management, security officers, and equipment manufacturers of important national and public facilities on site expect that it will be used as an objective standard at the government level for the component technology of the equipment to respond to the drone threat and the performance required in the environment.

• 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.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.35-40
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• 2019

In this research, we suggest a real-time tele-driving system for unmanned drone operations using the LTE communication system. The drone operator is located 180km away and controls the altitude and position of the drone with a 50ms time delay. The motion data and video from the drone is streamed to the operator. The video is played on the operator's head-mounted display (HMD) and the motion data emulates the drone on the simulator for the operator. In general, a drone is operated using RF signal and the maximum distance for direct control is limited to 2km. For long range drone control over 2km, an auto flying mode is enabled using a mission plan along with GPS data. In an emergency situation, the autopilot is stopped and the "return home" function is executed. In this research, the immersion tele-driving system is suggested for drone operation with a 50ms time delay using LTE communication. A successful test run of the suggested tele-driving system has already been performed between an operator in Daejeon and a drone in Inje (Gangwon-Do) which is approximately 180km apart.

• 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.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.124-131
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• 2017

Recently, drone is extending its range of usability. For example, the delivery, agriculture, industry, and entertainment area take advantage of drone mobilities. To control real drones, it needs huge amount of drone control training steps. However, it is risky; falling down, missing, destroying. The virtual drone system can avoid such risks. We reason that what kinds of technologies are required for building the virtual drone system. First, it needs that the virtual drone authoring tool that can assemble drones with the physical restriction in the virtual environment. We suggest that the drone assembly method that can fulfill physical restrictions in the virtual environment. Next, we introduce the virtual drone simulator that can simulate the assembled drone moves physically right in the virtual environment. The simulator produces a high quality rendering results more than 60 frames per second. In addition, we develop the physics engine based on SILS(Software in the loop simulation) framework to perform more realistic drone movement. Last, we suggest the virtual drone controller that can interact with real drone controllers which are commonly used to control real drones. Our virtual drone system earns 7.64/10.0 user satisfaction points on human test: the test is done by one hundred persons.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.31-37
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• 2017

Recently Drone technology is emerging as an alternative new way of distribution systems services. Amazon, Google which are global network chain distribution companies are developing an idea of Drone based delivery service and applied for patent for Drone distribution systems in USA. In this paper, we investigate a way to adopt Drone system to Air Cargo logistics, in particular, drone system based on combination of Light ID and RFID technology in the management procedure in stock warehouse. Also we explain the expected impact of Drone systems to customs declaration process. In this paper, we address the investigated limitations of Drone by the Korean Aviation Act as well as suggest the directions of future research for application of Drone to Air logistics industry with investigated limitations.

• Journal of Aerospace System Engineering
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• v.15 no.6
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• pp.33-41
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• 2021

As interest in drones controlled by remote technology rapidly increases worldwide, drones are used in many fields such as military, rescue, and transportation as future innovations in many countries. In particular, as the drone has been spotlighted as a new future strategic industry, it is also used for public purposes such as disaster relief and crime investigation. However, security concerns such as the penetration of major national facilities, collisions with aircraft, and the possibility of privacy infringement due to aerial photography are also being raised. Unlike the increased interest in drones, there have been few studies on drone use, drone safety, and public attitudes toward drone use. This paper analyzed the perceptions of drone attitudes, drone stability and policy support for drones for Korean and United Kingdom.