• ETRI Journal
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• v.44 no.6
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• pp.945-954
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• 2022

The 5G mobile network is promising to handle the dynamic traffic demands of user equipment (UE). Unmanned aerial vehicles (UAVs) equipped with wireless transceivers can act as flying base stations in heterogeneous networks to ensure the quality of service of UE. However, it is challenging to efficiently allocate limited bandwidth to UE due to dynamic traffic demands and low network coverage. In this study, a blockchain-enabled bandwidth allocation framework is proposed for secure bandwidth trading. Furthermore, the proposed framework is based on the Cournot oligopoly game theoretical model to provide the optimal solution; that is, bandwidth is allocated to different UE based on the available bandwidth at UAV-assisted-based stations (UBSs) with optimal profit. The Cournot oligopoly game is performed between UBSs and cellular base stations (CBSs). Utility functions for both UBSs and CBSs are introduced on the basis of the available bandwidth, total demand of CSBs, and cost of providing cellular services. The proposed framework prevents security attacks and maximizes the utility functions of UBSs and CBSs.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.2
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• pp.37-47
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• 2024

This paper examines the unique transportation challenges posed by Nepal's diverse and rugged terrain, which significantly hampers socio-economic development due to its negative impact on infrastructure, trade, and accessibility. Despite ongoing efforts to enhance road and traditional air transport systems, Nepal's geographic and environmental conditions continue to obstruct efficient connectivity, particularly in rural and remote areas. This study proposes Advanced Air Mobility (AAM) as a transformative solution, leveraging recent technological advancements in unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft. By conducting a comprehensive analysis of Nepal's current transportation infrastructure and the feasibility of AAM implementation, the paper highlights the potential benefits of AAM, including improved accessibility, economic growth, and environmental sustainability. Furthermore, it addresses the anticipated challenges and regulatory considerations necessary for integrating AAM into Nepal's transportation network. Through a multidisciplinary approach, this research aims to contribute to the discourse on overcoming transportation barriers in mountainous regions, offering policy recommendations and identifying areas for future study to facilitate the adoption of AAM in Nepal and similar contexts worldwide.

• Journal of Electrochemical Science and Technology
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• v.7 no.1
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• pp.41-51
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• 2016

In the stratosphere, the air is stable and a photovoltaic (PV) system can produce more solar energy compared to in the atmosphere. If unmanned aerial vehicles (UAVs) fly in the stratosphere, the flight stability and efficiency of the mission are improved. On the other hand, the weakened lift force of the UAV due to the rarefied atmosphere can require more power for lift according to the weight and/or wing area of the UAV. To solve this problem, it is necessary to minimize the weight of the aircraft and improve the performance of the power system. A regenerative fuel cell (RFC) consisting of a fuel cell (FC) and water electrolysis (WE) combined PV power system has been investigated as a good alterative because of its higher specific energy. The WE system produces hydrogen and oxygen, providing extra energy beyond the energy generated by the PV system in the daytime, and then saves the gases in tanks. The FC system supplies the required power to the UAV at night, so the additional fuel supply to the UAV is not needed anymore. The specific energy of RFC systems is higher than that of Li-ion battery systems, so they have less weight than batteries that supply the same energy to the UAV. In this paper, for a stratospheric long-endurance hybrid UAV based on an RFC system, three major design factors (UAV weight, wing area and performance of WE) affecting the ability of long-term flight were determined and a simulation-based feasibility study was performed. The effects of the three design factors were analyzed as the flight time increased, and acceptable values of the factors for long endurance were found. As a result, the long-endurance of the target UAV was possible when the values were under 350 kg, above 150 m² and under 80 kWh/kg H₂.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.408-414
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• 2019

We developed a surveillance tool for collision avoidance of unmanned aerial vehicles (UAVs) in urban areas. In our tool, users can visualize the safety distance on the actual 3D map of urban area. The estimated positions of UAVs are assumed to be obtained based on the long-term evolution (LTE) signals. The safety distance is defined to include two or more signals with bias. The safety distance calculation method used in this paper enables simulation similar to the actual urban areas where signals are frequently biased due to multipath. In the simulation, the parameters were set based on the measured values, and the change of the safety distance according to the number of faulty signals was simulated. As a result, increasing the number of faulty signals led to a longer safety distance as expected.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.48-55
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• 2024

In recent years, as the commercialization of unmanned aerial vehicles (UAVs) has been actively promoted, much attention has been focused on developing a technology to ensure the safety of UAVs. In general, the UAV has the potential to enter an uncontrollable state caused by sudden maneuvers, disturbances, and pilot error. To prevent entering an uncontrolled situation, it is essential to predict the flight state of the UAV. In this paper, we propose a flight state prediction technique based on an improved CNN-LSTM hybrid mode to enhance the flight state prediction performance. Simulation results show that the proposed prediction technique offers better state prediction performance than the existing prediction technique, and can be operated in real-time in an on-board environment.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.41-47
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• 2024

Recently, as the practical application and commercialization of unmanned aerial vehicles (UAVs) is pursued, interest in ensuring the safety of the UAV is increasing. Because UAV accidents can result in property damage and loss of life, it is important to develop technology to prevent accidents. For this reason, a technique to detect the abnormal flight state of UAVs has been developed based on the AutoEncoder model. However, the existing detection technique is limited in terms of performance and real-time processing. In this paper, we propose a U-Net based abnormal flight detection technique. In the proposed technique, abnormal flight is detected based on the increasing rate of Mahalanobis distance for the reconstruction error obtained from the U-Net model. Through simulation experiments, it can be shown that the proposed detection technique has superior detection performance compared to the existing detection technique, and can operate in real-time in an on-board environment.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.4
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• pp.35-44
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• 2016

Two primary tools to study park use are surveys and direct observation. Existing methods, however, are limited in terms of considerable costs in both time and money and the personal bias of respondents or observers. These limitations result in a lack of efficient, reliable, and affordable tools to investigate park use. This study explores the reliability and usability of unmanned aerial vehicles(UAVs) to explore park use in urban parks in Busan metropolitan city. By comparing with a systematic observation tool, SOPARC(System for Observing Play and Recreation in Communities), this study finds that the number of park users observed by two different techniques are highly correlated and not significantly different with each other. In addition, both methods show a high level of test-retest reliability. Comparing to existing methods, the UAV-using observation tool could cover larger target areas and is suitable to count park users in a more reliable and efficient way and map their use patterns, although it is weak in collecting detailed user information and surveying under poor conditions such as rain. Thus, the UAV method could complement direct observation. This study suggests practical implications of a UAV method to study park use.

• Atmosphere
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• v.34 no.1
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• pp.35-53
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• 2024

Under the leadership of the National Institute of Meteorological Sciences (NIMS), the first domestic autonomous flight-type weather modification experimental drone for fog and lower-level cloud seeding was developed in 2021. This drone is designed based on a multi-copter configuration with a maximum takeoff weight of approximately 25 kg, enabling the installation of up to four burning flares for seeding materials and facilitating weather observations (temperature, pressure, humidity, and wind) as well as aerosol (PM₁₀, PM_2.5, and PM_1.0) particle measurements. This research aims to introduce the construction of the drone and its recent applications over the past two years, providing insights into the experimental procedures, effectiveness verification, and improvement directions of the weather modification drone-based rain enhancement. In particular, partial confirmation of the experimental effects was obtained through the fog dissipation experiment on December 10, 2021, and the lower-level cloud seeding case study on October 5, 2022. To enhance the scope and rainfall amount of weather modification experiments using drones, various technological approaches, including adjustments to experimental altitude, seeding lines, seeding amount, and verification methods are necessary. Through this research, we aim to propose the development direction for weather modification drone technology, which will serve as foundational technology for practical application of domestic rain enhancement technology.