• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.5
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• pp.99-105
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• 2005

Development of an engine with good fuel economy is very important for successful implementation of long endurance miniature UAVs (unmanned aerial vehicles). In the study, a 4-stroke glow-plug engine was modified to a gasoline-fueled spark-ignition engine. Engine tests measuring performance and friction losses were conducted to tune a simulation program for performance prediction. It has been found that excessive friction losses are caused by insufficient lubrication at high speeds. The simulation program predicts that engine power and fuel economy get worse with high altitude due to increasing portion of friction losses. The simulation results suggest quantitative guidelines for further development of a practical engine.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.101-114
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• 2018

Several different approaches and mechanisms are introduced to solve the UAV path planning problem. In this paper, we designed and implemented an agent-based simulation software using the Repast platform and Java Genetic Algorithm Package to examine an evolutionary path planning method by implementing and testing within the Repast environment. The paper demonstrates the life-cycle of an agent-based simulation software engineering project while providing a documentation strategy that allows specifying autonomous, adaptive, and interactive software entities in a Multi-Agent System. The study demonstrates how evolutionary path planning can be introduced to improve cognitive agent capabilities within an agent-based simulation environment.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.395-398
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• 2009

Currently, a new generation of ducted fan UAVs (Unmanned Aerial Vehicles) is under development for a wide range of inspection, investigation and combat missions as well as for a variety of civil roles like traffic monitoring, meteorological studies, hazard mitigation etc. The current study presents extensive results obtained experimentally in order to investigate the tip clearance effects on performance characteristics of a ducted fan for small UAV systems. Three ducted fans having different tip clearance gap and with same rotor size were examined under three different yawed conditions of calibrated slanted hot-wire probe. Three dimensional velocity flow fields were measured from hub to tip at outlet of the ducted fan. The analysis of data were done by PLEAT (Phase locked Ensemble Averaging Technique) and three non-linear differential equations were solved simultaneously by using Newton -Rhapson numerical method. Flow field characteristics such as tip vortex and secondary flow were confirmed through axial, radial and tangential velocity contour plots. At the same time, the effects of tip clearance on axial thrust and input power were also investigated by using wind tunnel measurement system. For enhancing the performance of ducted fan, tip clearance level should be as small as possible.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1170-1176
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• 2010

This study addresses adaptive control of UAVs(Unmanned Aerial Vehicles) pitch-axis maneuver. The MRAC(Model Referenced Adaptive Control) approach is employed to accommodate uncertainties which are introduced by feedback linearization of pitch attitude control by elevator input. The model uncertainty is handled by adaptation laws which update model parameters while the UAV is under control by the feedback control law. Steady-state pitch attitude achieved by the stabilizing control law is derived to provide insight on the closed-loop behavior of the controlled system. The proposed idea is free of linearization, gain-scheduling procedures, so that one can design high maneuverability of UAVs for pitching motion in the presence of significant model uncertainty.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.55.2-55.2
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• 2011

This research aims to study the effects of the incidence angle and surface temperature on the power generation performance of a thin-film CIGS solar cell for solar powered unmanned aerial vehicles (UAVs). The test rig consists of a unit CIGS solar cell is installed on a table whose angle is controlled manually. A K-type thermocouple is attached to the solar cell surface for temperature measurements. A solar module analyzer measures the voltage and current generated from the test solar cell. The solar module analyzer also calculates the maximum solar power and efficiency of the solar cell. All test data are acquired in a PC. Test results show that the solar cell efficiency decreases significantly with increasing incidence angle and increasing surface temperature in general. As the incidence angle increases from 0 degree to 90 degree, the solar cell efficiency decreases by 60%. The solar cell efficiency decreases by 10% with increasing solar cell surface temperature from $20^{\circ}C$ to $30^{\circ}C$, for exmaple. The direct cooling method of the solar cell using dry ice decreases dramatically the solar cell surface temperature, thus increasing the solar cell efficiency by 15%.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1255-1255
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• 2022

Recently, research on digital twins to generate digital information and manage construction in real-time using advanced technology is being conducted actively. However, in the construction industry, it is difficult to optimize and apply digital technology in real-time due to the nature of the construction industry in which information is constantly fluctuating. In addition, inaccurate information on the topography of construction projects is a major challenge for earthmoving processes. In order to ultimately improve the cost-effectiveness of construction projects, both construction quality and productivity should be addressed through efficient construction information management in large-scale earthworks projects. Therefore, in this study, a 3D digital map-based AR site management work support system for higher efficiency and accuracy of site management was proposed by using unmanned aerial vehicles (UAV) in wide earthworks construction sites to generate point cloud data, building a 3D digital map through acquisition and analysis of on-site sensor-based information, and performing the visualization with AR at the site By utilizing the 3D digital map-based AR site management work support system proposed in this study, information is able to be provided quickly to field managers to enable an intuitive understanding of field conditions and immediate work processing, thereby reducing field management sluggishness and limitations of traditional information exchange systems. It is expected to contribute to the improvement of productivity by overcoming factors that decrease productivity in the construction industry and the improvement of work efficiency at construction sites.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.280-294
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• 2023

Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean-squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple-input multiple-output orthogonal frequency-division multiplexing systems.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.537-549
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• 2023

The distribution characteristics of tidal flat sediment components are used as an essential data for coastal environment analysis and environmental impact assessment. Therefore, a reliable classification map of surface sedimentary facies is essential. This study evaluated the applicability of regression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim, various factors such as the number of field survey data and remote sensing-based auxiliary data, the effect of regression models on regression kriging, and the comparison with other prediction methods (univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV) data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a result of the case study, it was most important to secure an appropriate amount of field survey data and to use topographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sediment facies classification map. In addition, regression kriging, which can consider detailed characteristics of the sediment distributions using ultra-high resolution UAV data, had the best prediction performance compared to other prediction methods. It is expected that this result can be used as a guideline to produce the tidal flat surface sedimentary facies classification map.