• 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).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.825-835
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• 2017

This paper aims to build a drone platform based on an optimum design of its single arm. Its single arm is assumed as a cantilevered beam with a tip mass. Based on the numerical optimization theory, validation and optimization of a new design is conducted by comparing the results with the similar ones obtained by ANSYS. Finally, this design is reflected in the control simulation, and the requirement of an optimum structural design considering the resonance situation is satisfied.

• Convergence Security Journal
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• v.16 no.3_2
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• pp.25-32
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• 2016

Drone refers to an unmanned flying system according to the remote control. That is a remote control systems on the ground or a system that automatically or semi auto-piloted system without pilot on board. Drones have been used and developed before for military purposes. However there are currently utilized in a variety of areas such as logistics and distribution of relief supplies disaster areas, wireless Internet connection, TV, video shooting and disaster observation, tracking criminals etc. Especially it can be actively used in activities such as search or the structure of the disaster site, and may be able to detect the movement of people and an attacker using an infrared camera at night. Drones are very effective for private security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.149-156
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• 2019

In the field of maintenance of power transmission lines, drones have been used for their patrol and inspection by KEPCO since 2017. This drone technology was originally developed by KEPCO Research Institute, and now workers from four regional offices of KEPCO have directly applied this technology to the drone patrol and inspection tasks. In the drone inspection system, a drone with an optical zooming camera and a thermal camera can fly automatically along the transmission lines by the ground control system developed by KEPCO Research Institute, but its camera gimbal has been remotely controlled by a field worker. Especially the drone patrol and inspection has been mainly applied for the transmission lines in the inaccessible areas such as regions with river-crossings, sea-crossings and mountains. There are often communication disruptions between the drone and its remote controller in such extreme fields of mountain areas with many barriers. This problem may cause the camera gimbal be out of control, even though the inspection drone flies along the flight path well. In addition, interference with the reception of real-time transmitted videos makes the field worker unable to operate it. To solve these problems, we have developed the auto-tracking camera gimbal system with deep learning method. The camera gimbal can track the transmission line automatically, even when the transmitted video on a remote controller is intermittently unavailable. To show the effectiveness of our camera gimbal system, its field test results will be presented in this paper.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.74-80
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• 2022

The utilisation of small drones is becoming increasingly widespread particularly in the military sector. In this study, STANAG 4586, a standard interface for military unmanned aerial vehicles, was applied to a multicopter-type small drone to examine the suitability of the military system. To accomplish this, a small multi-copter vehicle was designed and manufactured, integrating a flight control computer, ground control system, and data link. Furthermore, flight control and ground control equipment software were developed by applying the STANAG 4586 interface, followed by HILS and flight tests.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.27-32
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• 2024

Recently, in the era of the 4th industry, the era of smart agriculture is progressing with the use of related core technologies in the agricultural sector. As a representative example, the use of drones for pest control is increasing, and the use in the agricultural sector is increasing, and the existing control method is being changed by replacing the aging population. However, the importance of control management is increasing due to the increase in agricultural control drones. In this study, various civil complaints are occurring due to the non-standardization of the control operator's work instructions, control area allocation, and control settlement. In this study, we try to resolve civil complaints by computerizing various tasks that occur from the drone control manager's point of view and computerizing them so that they can be managed. Through this, it is intended to manage the control area for large areas and use it as basic data for the development of control management system.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.175-182
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• 2021

The purpose of this paper is to develop a machine learning-based marine drone to prevent the farming from harmful birds such as ducks. Existing drones have been developed as marine drones to solve the problem of being lost if they collide with birds in the air or are in the sea. We designed a CNN-based learning algorithm to judge harmful birds that appear on the sea by maritime drones operating by autonomous driving. It is designed to transmit video to the control PC by connecting the Raspberry Pi to the camera for location recognition and tracking of harmful birds. After creating a map linked with the location GPS coordinates in advance at the mobile-based control center, the GPS location value for the location of the harmful bird is received and provided, so that a marine drone is dispatched to combat the harmful bird. A bird fighting drone system was designed and implemented.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.273-279
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• 2021

A flight control computer(FLCC) applied to an unmanned aerial vehicle(UAV) is a safety-critical item, and which is designed in a multiple structure to increase the reliability of operation by securing fault tolerance. These FLCC of multiple structure should be designed so that each independent processing/control components can perform the same operation at the same time. And for this reason, a synchronization algorithm for synchronizing the operation between FLCCs should be included in an operational flight program. In this paper, we propose a software design method for synchronization between dual FLCCs applied to UAVs. The proposed synchronization method is designed to synchronize using only the minimum hardware resources to reduce a failure rate. In addition, the proposed synchronization method is designed to minimized synchronization errors due to a timer operation by designing in consideration of operation characteristics of the hardware timer used for the synchronization.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.9-15
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• 2023

This study investigated the effect of self-control on life satisfaction of youth participating in drone programs. Youth participating in drone program activities in the metropolitan area (Seoul, Gyeonggi) were conducted as questionnaires. The survey data were coded and statistically analyzed. The statistical significance level was ⍺= .05. The results of the study are as follows. First, simple task preferences and physical activity preferences affect mental health. Second, the pursuit of adventure affects happiness. Third, pursuit of adventure and self-centered factors affect autonomy and self-esteem. In conclusion, it can be judged that adolescents are partially satisfied with their lives through self-control.