• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2606-2626
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• 2022

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2666-2687
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• 2022

As a new alternative communication scheme in 5G, unmanned aerial vehicle (UAV) is used as a relay in the remote base station (BS) for assistant communication. In order to ameliorate the quality of the backhaul link, a UAV backhaul transmission scheme based on improved Nash equilibrium (NE) strategy is proposed. First, the capacity of air-to-ground (A2G) channel by the link preprocess is maximized. Then, the maximum utility function of each UAV is used as the basis of obtaining NE point according to the backhaul channel and the backhaul congestion. Finally, the improved NE strategy is applied in multiple iterations until maximum utility functions of all the UAVs are reached, and the UAVs which are rejected by air-to-air (A2A) link during the process would participate in the source recovery process to construct a multi-hop backhaul network. Simulation results show that average effective backhaul rate, minimum effective backhaul rate increases by 10%, 28.5% respectively in ideal A2G channel, and 11.8%, 42.3% respectively in fading channel, comparing to pure NE strategy. And the average number of iterations is decreased by 5%.

• 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%.

• ETRI Journal
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• v.45 no.2
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• pp.213-225
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• 2023

Physical layer security (PLS) can improve the security of both terrestrial and nonterrestrial wireless communication networks. This study proposes a simplified framework for nonterrestrial cyclic prefixed orthogonal variable spreading factor (OVSF)-encoded multiple-input and multiple-output nonorthogonal multiple access (NOMA) systems to ensure complete network security. Various useful methods are implemented, where both improved sine map and multiple parameter-weighted-type fractional Fourier transform encryption schemes are combined to investigate the effects of hybrid PLS. In addition, OVSF coding with power domain NOMA for multi-user interference reduction and peak-toaverage power ratio (PAPR) reduction is introduced. The performance of $\frac{1}{2}$-rated convolutional, turbo, and repeat and accumulate channel coding with regularized zero-forcing signal detection for forward error correction and improved bit error rate (BER) are also investigated. Simulation results ratify the pertinence of the proposed system in terms of PLS and BER performance improvement with reasonable PAPR.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.144-146
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• 2022

차량 환경에서 발생하는 계산 집약적인 태스크가 증가하면서 모바일 엣지 컴퓨팅(MEC, Mobile Edge Computing)의 필요성이 높아지고 있다. 하지만 지상에 존재하는 MEC 서버는 출퇴근 시간과 같이 태스크가 일시적으로 급증하는 상황에 유동적으로 대처할 수 없으며, 이러한 상황을 대비하기 위해 지상 MEC 서버를 추가로 설치하는 것은 자원의 낭비를 불러온다. 최근 이 문제를 해결하기 위해 UAV(Unmanned Aerial Vehicle)기반 MEC 서버를 추가로 사용해 엣지 서비스를 제공하는 연구가 진행되고 있다. 그러나 UAV MEC 서버는 지상 MEC 서버와 달리 한정적인 배터리 용량으로 인해 서버 간 로드밸런싱을 통해 에너지 사용량을 최소화 하는 것이 필요하다. 본 논문에서는 UAV MEC 서버의 에너지 사용량을 고려한 마이그레이션 기법을 제안한다. 또한 GRU(Gated Recurrent Unit) 모델을 활용한 트래픽 예측을 바탕으로 한 마이그레이션을 통해 지연시간을 최소화할 수 있도록 한다. 제안 시스템의 성능을 평가하기 위해 MEC의 마이그레이션 시점을 결정하는 기준점와 차량의 밀도에 따라 실험을 진행하고, 서버의 로드 편차, UAV MEC 서버의 에너지 사용량 그리고 평균 지연 시간 측면에서 성능을 분석한다.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.614-630
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• 2023

Dead trees significantly impact forest production and the ecological environment and pose constraints to the sustainable development of forests. A lightweight YOLOv4 dead tree detection algorithm based on unmanned aerial vehicle images is proposed to address current limitations in dead tree detection that rely mainly on inefficient, unsafe and easy-to-miss manual inspections. An improved logarithmic transformation method was developed in data pre-processing to display tree features in the shadows. For the model structure, the original CSPDarkNet-53 backbone feature extraction network was replaced by MobileNetV3. Some of the standard convolutional blocks in the original extraction network were replaced by depthwise separable convolution blocks. The new ReLU6 activation function replaced the original LeakyReLU activation function to make the network more robust for low-precision computations. The K-means++ clustering method was also integrated to generate anchor boxes that are more suitable for the dataset. The experimental results show that the improved algorithm achieved an accuracy of 97.33%, higher than other methods. The detection speed of the proposed approach is higher than that of YOLOv4, improving the efficiency and accuracy of the detection process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.779-800
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• 2024

Unmanned Aerial Vehicle (UAV) networks, also known as drone networks, have gained significant attention for their potential in various applications, including communication. UAV networks for communication involve using a fleet of drones to establish wireless connectivity and provide communication services in areas where traditional infrastructure is lacking or disrupted. UAV communication networks need to be highly secured to ensure the technology's security and the users' safety. The proposed survey provides a comprehensive overview of the current state-of-the-art UAV network security solutions. In this paper, we analyze the existing literature on UAV security and identify the various types of attacks and the underlying vulnerabilities they exploit. Detailed mitigation techniques and countermeasures for the protection of UAVs are described in this paper. The survey focuses on the implementation of novel technologies like Q-learning, blockchain, IRS, and mmWave. This paper discusses network simulation tools that range in complexity, features, and programming capabilities. Finally, future research directions and challenges are highlighted.

• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.213-221
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• 2024

Aircraft's operational effectiveness is reduced due to threats from enemy anti-aircraft weapons, which is a weak point. In particular, guided missiles, which pose a threat to aircraft, are rapidly developing due to technological advancements in seekers, and are classified as one of the important technologies in weapon systems. Missile approach warning equipment installed to ensure aircraft survivability detects guided missiles and provides relevant information to respond. Tests were conducted domestically to verify the detection level of missile approach warning equipment, and test results were presented under various test conditions.

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.3
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• pp.165-172
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• 2024

Italian ryegrass (IRG) has become a vital forage crop due to its increasing cultivation area and its role in enhancing forage self-sufficiency. However, its production is susceptible to environmental factors such as climate change and drought, necessitating precise yield prediction technologies. This study aimed to assess the growth characteristics of IRG and predict dry matter yield (DMY) using vegetation indices derived from unmanned aerial vehicle (UAV)-based remote sensing. The Green Leaf Index (GLI), normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and optimized soil-adjusted vegetation index (OSAVI) were employed to develop DMY estimation models. Among the indices, GLI demonstrated the highest correlation with DMY (R² = 0.971). The results revealed that GLI-based UAV observations can serve as reliable tools for estimating forage yield under varying environmental conditions. Additionally, post-winter vegetation coverage in the study area was assessed using GLI, and 54% coverage was observed in March 2023. This study assesses that UAV-based remote sensing can provide high-precision predictions of crop yield, thus contributing to the stabilization of forage production under climate variability.