• 한국환경복원기술학회지
• /
• 제24권6호
• /
• pp.89-96
• /
• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• 한국환경복원기술학회지
• /
• 제26권6호
• /
• pp.85-96
• /
• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• 제어로봇시스템학회논문지
• /
• 제14권12호
• /
• pp.1205-1211
• /
• 2008

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target's 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

• 한국항공운항학회지
• /
• 제26권2호
• /
• pp.47-53
• /
• 2018

In this paper, we investigate a collision avoidance algorithm for unmanned aerial vehicles using potential field based on the relative velocity of obstacles. The potential field consists of the attraction force and the repulsive force that are generated for the target and the obstacles. And the field can be classified into the attractive potential field generated by the target and the repulsive potential field generated by the obstacle, respectively. In this study, we construct an attractive potential field as a function of the distance between the UAV and the target position. On the other hand, a repulsive potential field is created by a function of distance and the relative velocity of the obstacle with respect to the UAV. The proposed potential field based collision avoidance algorithm is evaluate through simulations.

• 한국항행학회논문지
• /
• 제23권2호
• /
• pp.134-141
• /
• 2019

레이다 시스템은 송신 파형에 따라 크게 PD (pulse Doppler) 레이다와 FMCW (frequency modulated continuous wave) 레이다로 구분되며, 송수신 특성에 따라 PD 레이다는 장거리 표적 검출에 유리한 반면, FMCW 레이다는 단거리 표적 검출에 적합한 특성을 갖는다. 이에 본 논문에서는 중/장거리 뿐 아니라 단거리 무인기 탐지를 위해 PD 레이다 시스템과 FMCW 레이다 시스템을 모두 지원 가능한 멀티모드 레이다 신호처리 프로세서 (RSP; radar signal processor)를 제안한다. 제안된 레이다 신호처리 프로세서는 Verilog-HDL을 이용하여 RTL 설계 후, Altera Cyclone-IV FPGA를 이용하여 구현 및 검증 되었다. 구현 결과, 총 19,623개의 logic elements, 9,759개의register, 그리고 25,190,400의 memory bit로 구현 가능함을 확인하였으며, 기존의 PD 레이다와 FMCW 레이다 신호처리 프로세서를 개별 구현한 경우에 비해 logic elements와 register 요구량이 약 43%와 39% 감소됨을 확인하였다.

• 한국통신학회논문지
• /
• 제41권12호
• /
• pp.1868-1875
• /
• 2016

본 논문에서는 항공 ICT분야에서 최근 이슈가 되고 있는 UAV(Unmanned Aerial Vehicle)에 대한 효율적인 연구 개발 전략 수립을 위하여 특허분석을 기반으로 기술의 메가트렌드 및 경쟁력 분석 결과를 제시한다. UAV 핵심 기술의 메가트렌드 분석을 위해 한국, 미국, 일본 및 유럽의 공개/등록 특허를 중심으로 특허 동향을 분석하였다. 분석 대상 기술은 UAV와 ICT기술이 융합이 되는 기술을 중심으로 3개의 소분류로 구분하였으며, 특허검색 결과 총 3,433건이 검색되었다. UAV 핵심 기술별 경쟁력 및 한국의 경쟁력을 분석하기 위하여 특허 활동력, 특허 피인용률 및 주요 시장 확보율을 분석 지표로 사용하였다. 각 핵심 기술에 대한 R&D 전략 영역을 제시하고 이를 기반으로 각 영역별 연구개발 전략을 제시하였다. 본 논문을 통해 우리나라의 기술 수준, 선진 기업의 연구 개발동향 및 핵심특허 현황 등을 객관적인 특허정보를 기반으로 분석하였으며, 향후 UAV 분야의 연구개발 및 특허 확보 전략 수립에 활용이 될 것으로 기대 한다.

• 한국수소및신에너지학회논문집
• /
• 제25권4호
• /
• pp.393-404
• /
• 2014

Unmanned aerial vehicles (UAVs) have been applied to not only military missions like surveillance and reconnaissance but also commercial missions like meteorological observation, aerial photograph, communication relay, internet network build and disaster observation. Fuel cells make UAVs eco-friendly by using hydrogen. Proton exchange membrane fuel cells (PEMFCs) show low operation temperature, high efficiency, low noise and high energy density and those characterisitcs are well fitted with UAVs. Thus Fuel cell based UAVs have been actively developed in the world. Recently, fuel cell UAVs have started to develope for high altitude UAVs because target altitude of UAVs is expanded upto stratosphere altitude. Long endurance of UAVs is essential to improve effects of the missions. Improvement of UAV endurance time could be fulfilled by developing a hydrogen fuel storage system with high energy density and reducing the weight of UAVs. In this paper, research trend and analysis of fuel cell UAVs are introduced in terms of their altitude and endurance time and then the prospect of fuel cell UAVs are shown.

• 전기학회논문지
• /
• 제67권2호
• /
• pp.314-324
• /
• 2018

This paper proposes a linear distributed target tracking filter for multiple unmanned aerial vehicles(UAVs) sharing their passive sensor measurements through communication channels. Different from the conventional nonlinear filtering schemes, the distributed passive target tracking problem is newly formulated within the framework of a linear robust state estimation theory incorporated with a linear uncertain measurement equation including the coordinate transform uncertainty. To effectively cope with the performance degradation due to the coordinate transform uncertainty, a linear consistent robust Kalman filter(CRKF) theory is devised and applied for designing a distributed passive target tracking filter. Through the simulations for typical UAV surveillance mission, the superior performance of the proposed method over the existing schemes of distributed passive target tracking are demonstrated.

• 대한원격탐사학회지
• /
• 제38권2호
• /
• pp.199-213
• /
• 2022

비지도 도메인 적응은 연단위 작물 분류를 위해 매년 반복적으로 양질의 훈련자료를 수집해야 하는 비실용적인 문제를 해결할 수 있다. 이 연구에서는 작물 분류를 위한 딥러닝 기반 비지도 도메인 적응 모델의 적용성을 평가하였다. 우리나라 마늘, 양파 주산지인 합천군과 창녕군을 대상으로 무인기 영상을 이용한 작물 분류 실험을 통해 deep adaptation network (DAN), deep reconstruction-classification network, domain adversarial neural network (DANN)의 3개의 비지도 도메인 적응 모델을 정량적으로 비교하였다. 비지도 도메인 적응 모델의 분류 성능을 평가하기 위해 소스 베이스라인 및 대상 베이스라인 모델로 convolutional neural networks (CNNs)을 추가로 적용하였다. 3개의 비지도 도메인 적응 모델은 소스 베이스라인 CNN보다 우수한 성능을 보였으나, 소스 도메인 영상과 대상 도메인 영상의 자료 분포 간 불일치 정도에 따라 서로 다른 분류 성능을 보였다. DAN의 분류 성능은 두 도메인 영상 간 불일치가 작을 때 다른 두 모델에 비해 분류 성능이 높은 반면에 DANN은 두 도메인 영상 간 불일치가 클 때 가장 우수한 분류 성능을 보였다. 따라서 신뢰할 수 있는 분류 결과를 생성하기 위해 두 도메인 영상의 분포가 일치하는 정도를 고려해서 최상의 비지도 도메인 적응 모델을 선택해야 한다.

• Journal of Electrochemical Science and Technology
• /
• 제7권1호
• /
• pp.41-51
• /
• 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.