• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.497-505
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• 2018

In a situation where the amount of bulky waste needs to be quantified, a three-dimensional model of the wastes can be constructed using drones. This study constructed a drone-based 3D model with a range of flight parameters and a GCPs survey, analyzed the relationship between the accuracy and time required, and derived a suitable drone application technique to estimate the amount of waste in a short time. Images of waste were photographed using the drone and auto-matching was performed to produce a model using 3D coordinates. The accuracy of the 3D model was evaluated by RMSE calculations. An analysis of the time required and the characteristics of the top 15 models with high accuracy showed that the time required for Model 1, which had the highest accuracy with an RMSE of 0.08, was 954.87 min. The RMSE of the 10th 3D model, which required the shortest time (98.27 min), was 0.15, which is not significantly different from that of the model with the highest accuracy. The most efficient flight parameters were a high overlapping ratio at a flight altitude of 150 m (60-70% overlap and 30-40% sidelap) and the minimum number of GCPs required for image matching was 10.

• Journal of Aerospace System Engineering
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• v.15 no.1
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• pp.102-110
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• 2021

Recently, multi-copters equipped with various collision avoidance sensors have been introduced to improve flight stability. LiDAR is used to recognize a three-dimensional position. Multiple cameras and real-time SLAM technology are also used to calculate the relative position to obstacles. A three-dimensional depth sensor with a small process and camera is also used. In this study, a small collision-avoidance multi-copter system capable of in-door flight was developed as a platform for the development of collision avoidance software technology. The multi-copter system was equipped with LiDAR, 3D depth sensor, and small image processing board. Object recognition and collision avoidance functions based on the YOLO algorithm were verified through flight tests. This paper deals with recent trends in drone collision avoidance technology, system design/manufacturing process, and flight test results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1247-1255
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• 2016

The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.854-864
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• 2016

The use of small drone platform has become a popular topic in these days but its application for SAR operation has been little known due to the burden of the payload implementation. Drone platforms are distinguished from the conventional UAV system by the increased vulnerability to the turbulences, control-errors and poor motion stability. Consequently, sophisticated motion compensation may be required to guarantee the successful acquisition of high quality SAR imagery. Extremely limited power and mass budgets may prevent the use of additional hardwares for motion compensation and the difficulty of SAR focusing is further aggravated. In this paper, we have carried out a feasibility study of mico-SAR drone operation. We present the image acquisition results from the preliminary flight tests and a quality assessment is followed on the experimental SAR images. The in-flight motion errors derived from the unique drone movements are investigated and attempts have been made to compensate for the geometrical and phase errors caused by motions against the nominal trajectory. Finally, the successful operation of drone SAR system is validated through the focussed SAR images taken over test sites.

• Ecology and Resilient Infrastructure
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• v.8 no.1
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• pp.22-31
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• 2021

Surface image velocimetry (SIV) is a noncontact velocimetry technique based on images. Recently, studies have been conducted on surface velocity measurements using drones to measure a wide range of velocities and discharges. However, when measuring the surface velocity using a drone, reference points must be included in the image for image correction and the calculation of the ground sample distance, which limits the flight altitude and shooting area of the drone. A technique for calculating the surface velocity that does not require reference points must be developed to maximize spatial freedom, which is the advantage of velocity measurements using drone images. In this study, a technique for calculating the surface velocity that uses only the drone position and the specifications of the drone-mounted camera, without reference points, was developed. To verify the developed surface velocity calculation technique, surface velocities were calculated at the Andong River Experiment Center and then measured with a FlowTracker. The surface velocities measured by conventional SIV using reference points and those calculated by the developed SIV method without reference points were compared. The results confirmed an average difference of approximately 4.70% from the velocity obtained by the conventional SIV and approximately 4.60% from the velocity measured by FlowTracker. The proposed technique can accurately measure the surface velocity using a drone regardless of the flight altitude, shooting area, and analysis area.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.407-408
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• 2016

A drone has been popularized to such an extent as to be seen in the near parks recently. The drone refers to an unmanned aerial vehicle(UVA) which can fly and be steered by a radio wave without a pilot and it has a airplane or helicopter shape. The drone was first started to be used from military purpose, but its usage has been expanded to the private such as broadcast shooting, crop-dusting, field discovery and hobby. However the drone that we can see often in the market is expansive, hard to be repaired when it broken down and has a discomfort of the short flight time. In this paper, to solve an uncomfortable talk on the cheap ATmega128 Using (Quad copter) drone for implementation. Axes gyroscope and accelerometers mcu between posture an attitude control, communications through drone control, pid. Receiver input them into transmitter signals of movements to control drone c the programming was implemented in on the basis of language.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.213-218
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• 2022

In this paper, a technology for a high-efficiency wireless power transmission transmitting and receiving coil that can wirelessly charge a drone is introduced. The drone station implements the ability to charge the battery wirelessly without the need to remove the battery to charge the drone's battery. In order to charge the drone's battery in the shortest time, wireless charging efficiency must be high. In order to increase the wireless charging efficiency of the drone station, a method for manufacturing high-efficiency transmitting and receiving coils and a performance measurement method are presented. Transmitting and receiving coils were manufactured considering the size and weight of the drone so as not to interfere with the flight of the drone. Efficiency of 88% or more was realized at a distance of 40mm or more between the transmitting and receiving coils.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.256-257
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• 2017

Pressure sensors are usually used in the measurement of drone altitude in an indoor environment since GPS (global positioning system) signal is not available. In this paper, we propose a new method which uses the Chebyshev filter to decrease a high frequency error in the measured values of the pressure sensor. Considering performance of a drone flight controller, the filter order is limited to the $3^{rd}$ order. We explain the transfer function of the $3^{rd}$ order Chebyshev filter.