• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.149-157
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• 2020

Regarding previously-developed drone simulators, it was easy to check their flight stability or controlling functions based on the condition that their weight was fixed from the design. However, the drone is largely classified into two types that is the one with the fixed weight whose purpose is recording video with camera and racing and another is whole weight-variable during flight with loading the articles for delivery and spraying pesticide though the weight of airframe is fixed. The purpose of this thesis is to analyze the structure of drone and its flight principle, suggest dynamics-model-based simulator that is capable of simulating weight-variable drone and develop the simulator that can be used for designing main control board, motor and transmission along the application of weight-variable drone. Weight-variable simulator was developed by using various calculation to apply flying method of drone to the simulator. First, ground coordinate system and airframe-fixing coordinate system were established and switching matrix of those two coordinates were made. Then, dynamics model of drone was established using the law of Newton and moment balance principle. Dynamics model was established in Simulink platform and simulation experiment was carried out by changing the weight of drone. In order to evaluate the validity of developed weight-variable simulator, it was compared to the results of clean flight public simulator against existing weight-fixed drone. Lastly, simulation test was performed with the developed weight-variable simulation by changing the weight of drone. It was found out that dynamics model controlled various flying positions of drone well from simulation and the possibility of securing the optimum condition of weight-variable drone that has flying stability and easiness of controlling.

• 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.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.245-249
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• 2021

In this paper, we present novel simulation contents for drone racing and autonomous flight of drone. With Depth camera and SLAM, we conducted mapping 3 dimensional environment through RTAB-map. The 3 dimensional map is represented by point cloud data. After that we recovered this data in Unreal Engine. This recovered raw data reflects real data that includes noise and outlier. Also we built drone racing contents like gate and obstacles for evaluating drone flight in Unreal Engine. Then we implemented both HITL and SITL by using AirSim which offers flight controller and ROS api. Finally we show autonomous flight of drone with ROS and AirSim. Drone can fly in real place and sensor property so drone experiences real flight even in the simulation world. Our simulation framework increases practicality than other common simulation that ignore real environment and sensor.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.663-671
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• 2022

Practice through virtual reality can increase the educational effect regardless of time and place, and it is an educational method that is being pursued even in the situation of COVID-19. On the other hand, for VR-based education, related technology development and content development must be made, and experiential methods (flipped learning, blended learning, hybrid learning) must be provided in the educational process. The development scenario was developed with the contents of drone qualification test (ultra-light unmanned multicopter) and drone practice and the possibility of non-face-to-face self-directed learning (flipped learning, blended learning, hybrid learning). It is expected that the quality of vocational education related to drones and the effect of high education will be improved through the contents, and it is thought that it will be possible to suggest a direction for the development of various vocational education contents in non-face-to-face education.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.

• 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.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.107-112
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• 2018

Recently, there are many real life applications that uses drones. There are various applications such as the aerial shot with the drones for the broadcasting service or drone racing competition and so on. Specifically, they patrol for the traffic enforcement on a highway. The police department use the 'Spot Mobility' method which float the drones for 30 minute period. However, this method is inefficient for the wide area with small numbers of enforcement. Therefore, a wireless network system consists of drones to patrol on the highway systematically is required. In this paper, the most efficient routing protocol will be selected for the MANET drone network by applying various routing protocols. To accomplish this, the drone patrol network system with routing protocols are designed and simulated in OPNET simulator.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.9
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• pp.973-978
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• 2015

The drone delivery service as a new important business is emerging worldwide. American DHL parcel service is already running the delivery business using drones, and the Google, Amazon, and china's Alibaba, Baidu are also preparing for the same business. However, there are some problems in security and safety since the heavy parcels flies over the people walking down streets, so many things must be considered. The problems can be solved by communicating among drones to avoid collision. Therefore, the efficient network performance is essential, and to resolve the problem, in this paper, an efficient routing protocol is suggested by the Opnet simulator. The drone intercommunication network of MANET is designed and simulated for the efficient routing protocol.