• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.81-85
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• 2022

In this study, we conducted a structural design and analysis of air intake of aircraft engine using composite materials. First, an investigation on structural design requirement of target structure was carried out. The distributed pressure load and acceleration condition was applied to structural design. To evaluate the structural design result, finite element analysis was carried out. The stress, deflection and buckling analysis for structural safety evaluation was performed. Finally, it was confirmed that the air intake through structural analysis is safety.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.198-201
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• 2021

무인 항공기(UAV, Unmanned Aerial Vehicles)는 높은 기동성을 가지며 설치 비용이 저렴하다는 이점이 있어 홍수, 지진 등의 재난 재해 감시 시스템에 이용되고 있다. 재난 재해 감시 시스템에서 UAV는 지상에 위치한 사물인터넷(IoT, Internet of Things) 기기로부터 데이터를 수집하는 임무를 수행하기 위해 계획된 항로를 따라 비행한다. 이때 UAV가 정상 경로로 비행하기 위해서는 실시간으로 GPS 위치 확인이 가능해야 한다. 만일 UAV가 계산한 현재 위치의 GPS 정보가 잘못될 경우 비행경로에 대한 통제권을 상실하여 임무 수행을 완료하지 못하는 결과가 초래될 수 있다는 취약점이 존재한다. 이러한 취약점으로 인해 UAV는 공격자가 악의적으로 거짓 GPS 위치 신호를 전송하는GPS 스푸핑(Spoofing) 공격에 쉽게 노출된다. 본 논문에서는 신뢰할 수 있는 시스템을 구축하기 위해 지상에 위치한 기기가 송신하는 신호의 세기와 GPS 정보를 이용하여 UAV에 GPS 스푸핑 공격 여부를 탐지하고 공격당한 UAV가 경로를 이탈하지 않도록 대응하기 위해 연합학습(Federated Learning)을 이용하는 방안을 제안한다.

• Smart Structures and Systems
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• v.31 no.6
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• pp.573-583
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• 2023

Although unmanned aerial vehicles have been used to overcome the limited accessibility of human-based visual inspection, unresolved issues still remain. Onsite inspectors face difficulty finding previously detected damage locations and tracking their status onsite. For example, an inspector still marks the damage location on a target structure with chalk or drawings while comparing the current status of existing damages to their previous status, as documented onsite. In this study, an augmented-reality-based structural inspection system with onsite damage information marking was developed to enhance the convenience of inspectors. The developed system detects structural damage, creates a holographic marker with damage information on the actual physical damage, and displays the marker onsite via an augmented reality headset. Because inspectors can view a marker with damage information in real time on the display, they can easily identify where the previous damage has occurred and whether the size of the damage is increasing. The performance of the developed system was validated through a field test, demonstrating that the system can enhance convenience by accelerating the inspector's essential tasks such as detecting damages, measuring their size, manually recording their information, and locating previous damages.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.300-302
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• 2022

In this paper, We propose a mobility model-based resource collision avoidance technique that can share radio resources without resource collision between UAV in a FANET environment. The proposed technique tries to reduce the occurrence of resource collisions by estimating UAV mobility based on information obtained from GPS devices installed in UAV. Through simulations, The performance was compared with the contention-based protocol, and it was confirmed that the proposed algorithm can reduce resource collisions.

• 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.

• 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 the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.169-185
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• 2019

While images and aerial photographs using conventional satellites have the advantage of providing data in a vast area, there is a difficult aspect: the limitations of filming and processing data in a particular region at a desired time and the repetitive filming of a short cycle. With the development of many new technologies to overcome these shortcomings, methods of building cadastral information are changing rapidly. In particular, unmanned aerial vehicles that deploy cadastral information quickly and accurately using UAV have increased interest in technology that obtains cadastral information. Therefore, the purpose of this study was to suggest the application of cadastral measurement tasks in areas subject to cadastral measurement using UAV. To this end, the Commission decided to compare and analyze the accuracy of high-resolution images produced by observation area and apply them to existing cadastral work using verified images and cadastral data. In this study, we will analyze the applicability of UAVs to their cadastral survey by analyzing the current status of legislation related to cadastral survey and the technical characteristics of UAVs and propose technological, legal and institutional improvement measures for introduction based on them.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.476-483
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• 2021

In order to maintain the safety of the airspace with ever increasing traffic volume, it is necessary to thoroughly analyze the collision risk with the current data. In this study, collision risk analysis was conducted using Detect and Avoid (DAA) Well-Clear (DWC) metrics, risk induces developed for the DAA systems of unmanned aerial vehicles. All flights in year 2019 that flew within the Incheon Flight Information Region (FIR) boundary were analyzed using the recorded Automatic Dependent Surveillance-Broadcast(ADS-B) data. High risk regions as well as trends by airports and seasons were identified. The results indicate that the risk is higher around the congested area near Incheon International Airport and Gimpo International Airport. Seasonally, the risk was highest in August that coincides with the Summer vacation period. The result will be useful for the baseline data for various aviation safety enhancement activities such as revision of routes and procedures and training of the field specialists.