• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.147-153
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• 2017

The main subject of year 2016 Davos forum was "The 4th Industrial Revolution." Recently, interests and investment in drone market, so called industrial revolution in the sky is growing in many countries around the world. Before, drone was used for military purpose such as reconnaissance or attacking but today, it is used in various private sectors such as unmanned delivery service, agriculture, leisure activities, etc. Presently, many major countries in the world are already involved in the 'war without gunfire' to be dominant in this drone industry. Korean government also has announced an extreme relaxation of regulations for growing drone industry by opening a conference with Ministers related to economics. During the conference, business scope of drone which was limited to agriculture, photographing, and observation was expanded to all the fields except for cases hindering national safety and security. In terms of shooting purpose drone its process of receiving approval for flight and shooting is simplified to online registration. What is more, drone delivery service will be allowed in island areas such as Goheung, Yeongwol, etc from first term of year 2017. Finding the way to apply drone in criminal investigation is also speeding up. Recently, Public Safety Policy Research Center in Korean National Police University has inquired for research service and its result will be out around November. Likewise, although more and stronger foundation for supporting drone industry is made but there are still, some opinions saying that we should take a careful approach in consideration to the side effect such as abuse in crime. One may also try terror by placing a dangerous substance. If drone falls, it may hurt any civilians. Moreover, if shopping purpose drone is hacked, it may result in violation of privacy. Compared to America, Europe, and China, we are at the very beginning stage of drone industry and it is necessary to reorganize legal issues to grow this industry. This can be thought from two perspectives; first, the growth of drone industry is blocked by difficult regulations on Aviation Law and Radio Regulation Law. The second issue is the safety and privacy that are required for operating drone. For the advanced technologies to make human life more profitable, more active and proactive actions are required by criminal law side. In preparation to the second mechanical era where man and machines should go together, I hope that responsible preparation is required in all fields including the criminal law.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.141-146
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• 2019

Economic analysis predicts that the drone market will grow, and the growth of the toy and hobby drone market is expected to gradually expand. Drone expectations are rising due to the net economic function of drone market growth, but accidents due to improper management and operations are also increasing. The difference in toy drone performance is incomparably small compared to industrial drone performance, but the ordinary buyer can not know whether the difference can cause an accident during use. The toy drones used in this study were obtained from KC and CE certification, and 20 kinds of drones were used. The flight time ranged from a minimum of 3 minutes to a maximum of 12 minutes, and the control distance ranged from a minimum of 20m to a maximum of 380m. Therefore, it is necessary to secure product safety through sampling inspection of the radio wave output of toy drones, and it is also necessary to mount an algorithm that automatically lowers the altitude or hover when exceeding the limit flight distance. For future research, we will build data to establish toy drone safety standards through a altitude testing and impact testing of toy drone.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.69-75
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• 2019

Remotely Piloted Aircraft (RPA) controls as a type of unmanned aerial vehicle (drone) is growing rapidly and its flight controller stick disposition is required standardization. We should standardize RPA drone flight control disposition because the flight pilot of RPA is hard to be trained so the flight controller stick differences impairs safety and wastes time and effort of flight controller industry. So this study researches the on-going standardization of RPA drone flight control disposition in Korea and foreign countries. Also this paper analyzes and researches of expert about RPA drone flight controller function and application of flight control mode. I accomplished expert research about standardization plan of unmanned flight control mode and confirm the necessity. Nowadays mode1 and 2 are mostly used in Korea so I carried out preference investigation for two modes. There were 4 preferences choices of RPA drone control mode necessity (importance) and recommendation of standardization modes. They answered that necessity of standardization is important considering pilot training, flight safety and positive development of drone industry. The result of standardization mode preference is that they prefer mode 2 (drone maker 86%, training facilities and research facilities 58%, government bureau 60%). Overall preference result shows that mode 1 24%, mode 1&2 16%, mode 2 60%. So they preferred mode 2 by 60%. The differences between two modes are the direction of throttle and pitch. Direction of throttle and pitch operate opposite way. They prefer mode 2 because mode 2 has similarities of manned flight control mode. Significance of this study is that it showed the necessity of standardization and flight control preference in a quantitative way. It will help drone standardization in related industries and development direction near future.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.39-48
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• 2018

The purpose of this study is to verify the stable flight conditions of drones within a limited urban area by using the ICAO(International Civil Aviation Organization) reich model which is using to evaluate civil aircraft stability. The results of the study are summarized as follows. First, in order for the drones flying stably, the horizontal safety separation distance between a drone and another should be at least 1,852M. Second, assuming that no obstacles within 1,852M of horizontal space, two drones can be fly into upper and lower spaces. However there are obstacles such as buildings, it is impossible to secure a 1,852M distance between drones. Third, sensitivity analysis point out that the separation interval($s_x$) of drone aviation has the greatest influence on the TLS(Target Level of Safety). If future research is conducted to lower the numerical values, the safety distance between a drone and another drone will be drastically reduced, allowing more detailed urban space division, and will be presented as a scientific numerical value for establishing a dedicated path for the drones.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.491-499
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• 2020

There are policies that foster the drone industry, which either put a legal precedent on drones through the "Drone Act" or grant a delay or exemption in applying the safety measures of "the Aviation Safety Act". Yet, the definition of a drone is unclear, requiring further discussion on commercial usage. Therefore, we have studied cases domestically and abroad, and also analyzed issues with the current aviation legislation. It was found that a drone is defined as "an unmanned aircraft where a pilot is not on board, and its net weight is 150 kg or less". However, there are several issues, such as that a drone taxi requires a pilot on board, and its weight is 150 kg or more. Thus, we propose to define a drone as "an unmanned aerial vehicle (provided, that its own net weight should be 300 kg or under, or not be limited to weight) under Article 2 (3) of the "Aviation Security Act" as prescribed by Ordinance of the Ministry of Land, Infrastructure, and Transport, which operates either by remote, automatically, or autonomously; or an unmanned aircraft under Article 2 (6) of the "Aviation Security Act".

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.633-646
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• 2023

The increases in UAVs(Unman Aerial Vehicle) such as drone result in safety issues and the threat of illegal drone as well. Recognizing the need for Drone forensics, domestic and foreign organizations and agencies are trying to establish drone forensic guidelines. The definition of Drone forensic artifacts and examination of forensic tools must be provided, in order to establish a practical drone forensic framework on security sites and also the concept of drone live forensic which provides meaningful data that can be extracted in a live state. In this study, the drone forensic methodology covering various types of drones is explained, and the practical forensic methodology with live forensic PoC(Proof Of Concept) tools; LiPFo(Live-PX4-Forenensic) is proposed.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.99-103
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• 2017

Recently, acccording to price decline and miniaturization of drone, it is increased dramatically that drone usage in various category including military and private sectors. In accordance with popular usage, There is a increasing risk of safety accident, national security and public privacy problem. Hence there is a high demand for study and analysis applicable to the related technology and anti-drone method including drone detection and jamming. In general, it is extremely difficult to detect and recognize drones using conventional sensors. In this paper, we classify drone detection technology and Drone detection experiments are performed using CW RADAR to obtain and analyze micro-doppler pattern. This preliminary study aims to provide fundamental theory on radar drone detection and experimental test results such that in-depth anti-drone technology can be established in future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1080-1088
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• 2018

General methods of controlling a drone are divided into manual control and automatic control, which means a drone moves along the route. In case of manual control, a man should be able to figure out the location and status of a drone and have a controller to control it remotely. When people control a drone, they collect information about the location and position of a drone with the eyes and have its internal information such as the battery voltage and atmospheric pressure delivered through telemetry. They make a decision about the movement of a drone based on the gathered information and control it with a radio device. The automatic control method of a drone finding its route itself is not much different from manual control by man. The information about the position of a drone is collected with the gyro and accelerator sensor, and the internal information is delivered to the CPU digitally. The location information of a drone is collected with GPS, atmospheric pressure sensors, camera sensors, and ultrasound sensors. This paper presents an investigation into drone control by a remote computer. Instead of using the automatic control function of a drone, this approach involves a computer observing a drone, determining its movement based on the observation results, and controlling it with a radio device. The computer with a Depth camera collects information, makes a decision, and controls a drone in a similar way to human beings, which makes it applicable to various fields. Its usability is enhanced further since it can control common commercial drones instead of specially manufactured drones for swarm flight. It can also be used to prevent drones clashing each other, control access to a drone, and control drones with no permit.

• The Journal of Korea Robotics Society
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• v.12 no.4
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• pp.425-431
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• 2017

As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.

• 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.