본 연구는 수수를 대상으로 수수 주산지에서 취득한 무인기 기반 RGB영상에 Seg-Net과 U-Net모델을 작성 후, 일반화 가능성을 검토하여 실시간 재배지 파악에 더 효율적인 기법을 제안하기 위해 수행되었다. 경상북도 안동시의 수수 재배지 영상 264장을 이용해 모델학습을 진행하고, 충청북도 제천시 수수 재배지 영상 14장을 이용하여 테스트를 진행하였다. U-Net의 학습은 14epoch, AccuracyC = 0.9426, LossC = 0.1593, Dice_coefC = 0.9223, AccuracyV = 0.6403, LossV = 1.9624, Dice_coefV = 0.6402에 4시간 37분이 소요되었으며, Seg-Net의 학습은 101epoch, AccuracyC = 0.6363, LossC = 0.6573, Dice_coefC = 0.5586, AccuracyV = 0.5711, LossV = 0.6785, Dice_coefV = 0.5586이며 1시간 55분이 소요되었다. Test결과 U-Net은 AccuracyT = 0.6806, LossT = 0.7180, Dice_coefT = 0.5558, Seg-Net은 AccuracyT = 0.7472, LossT = 0.5225, Dice_coefC = 0.6159로 나타났다. Seg-Net의 Calibration성능은 낮지만 일반화 성능이 뛰어나며, 모델의 빠른 학습 시간, 더 낮은 메모리 요구량으로 수수 재배지 분할에서 U-Net보다 효율적인 모델이라고 사료된다.

North Korea has repeatedly provoked using unmanned aerial vehicles (UAVs), and the threat posed by UAVs continues to escalate, as evidenced by recent directives involving the use of waste-laden balloons and the development of suicide drones. North Korea’s small UAVs are difficult to detect due to their low radar cross-section (RCS) values, necessitating the efficient deployment and operation of assets for effective response. Against this backdrop, this study aims to predict the infiltration routes of enemy UAVs by considering their perspective, avoiding key facilities and obstacles, and propose deployment strategies to enable rapid detection and response during provocations. Utilizing the Markov Decision Process (MDP) based on previous studies, this research presents a model that reflects both UAV flight characteristics and regional environments. Unlike previous models that designate a single starting point, this study addresses the practical challenge of uncertainty in initial infiltration points by incorporating multiple starting points into the scenarios. By aggregating and integrating the probability maps derived from these variations into a unified map, the model predicts areas with a high likelihood of UAV infiltration over time. Furthermore, based on case studies in the capital region, this research proposes deployment strategies tailored to the specifications of currently known anti-drone integrated systems. These strategies are expected to support military decision-making by enabling the efficient operation of assets in areas with a high probability of UAV infiltration.

Due to the recent impact of global warming, heavy rainfall and droughts have been occurring regardless of the season, affecting the growth of Italian ryegrass (IRG), a winter forage crop. Particularly, delayed sowing due to frequent heavy rainfall or autumn droughts leads to poor growth and reduced winter survival rates. Therefore, techniques to improve yield through additional sowing in spring have been implemented. In this study, the growth of IRG sown in Spring and Autumn was compared and analyzed using vegetation indices during the months of April and May. Spectral data was collected using an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral sensor, and the following vegetation indices were utilized: Normalized Difference Vegetation Index; NDVI, Normalized Difference Red Edge Index; NDRE (I), Chlorophyll Index, Red Green Ratio Index; RGRI, Enhanced Vegetation Index; EVI and Carotenoid Reflectance Index 1; CRI1. Indices related to chlorophyll concentration exhibited similar trends. RGRI of IRG sown in autumn increased during the experimental period, while IRG sown in spring showed a decreasing trend. The results of RGRI in IRG indicated differences in optical characteristics by sowing seasons compared to the other vegetation indices. Our findings showed that the timing of sowing influences the optical growth characteristics of crops by the results of various vegetation indices presented in this study. Further research, including the development of optimal vegetation indices related to IRG growth, is necessary in the future.

This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.

본 논문은 북한의 무인기 위협과 한국의 대응을 분석하기 위한 것이 다. 이를 위해 무인기와 비대칭위협, 북한의 무인기 위협 현황 및 평가, 한국의 북한 무인기 위협 대응과제를 살펴본 후 결론을 도출하였다. 최 근 북한은 비행금지구역을 침범하는 등 무인기에 의한 새로운 형태의 대 남도발을 시도하고 있다. 북한의 무인기 침투의 경우 운용상의 비대칭성 과 대남 테러 가능성으로 인해 비대칭성이 존재한다. 북한의 무인기 개 발은 기존의 정보·감시·정찰의 목적에서 공격용으로 전환되는 추세이다. 북한의 무인기 개발배경은 북한의 안보적·상징적·대남위협적 동기 및 효 과에서 나온 것이다. 향후 북한의 무인기 대응을 위해 ①민·관·군 협력 제고, ②합동 드론사령부 창설, ③한미동맹 차원의 협력 제고 등의 과 제를 적극 추진해 나가야 할 것이다.

Recently, the Defense Advanced Research Projects Agency(DARPA) in the United States is studying a new concept of war called Mosaic Warfare, and MUM-T(Manned-Unmanned Teaming) through the division of missions between expensive manned and inexpensive unmanned aircraft is at the center. This study began with the aim of deriving the priority of autonomous functions according to the role of unmanned aerial vehicles in the present and present collaboration that is emerging along with the concept of mosaic warfare. The autonomous function of unmanned aerial vehicles between the presence and absence collaboration may vary in priority depending on the tactical operation of unmanned aerial vehicles, such as air-to-air, air-to-ground, and surveillance and reconnaissance. In this paper, ACE (Air Combat Evaluation), Skyborg, and Longshot, which are recently studied by DARPA, derive the priority of autonomous functions according to air-to-air collaboration, and use AHP analysis. The results of this study are meaningful in that it is possible to recognize the priorities of autonomous functions necessary for unmanned aircraft in order to develop unmanned aerial vehicles according to the priority of autonomous functions and to construct a roadmap for technology implementation. Furthermore, it is believed that the mass production and utilization of unmanned air vehicles will increase if one unmanned air vehicle platform with only essential functions necessary for air-to-air, air-to-air, and surveillance is developed and autonomous functions are expanded in the form of modules according to the tactical operation concept.

Manned-unmanned teaming can be a very promising air-to-air combat tactic since it can maximize the advantage of combining human insight with the robustness of the machine. The rapid advances in artificial intelligence and autonomous control technology will speed up the development of manned-unmanned teaming air-to-air combat system. In this paper, we introduce a manned-unmanned teaming air-to-air combat tactic which is composed of a manned aircraft and an UAV. In this tactic, a manned aircraft equipped with radar is functioning both as a sensor to detect the hostile aircraft and as a controller to direct the UAV to engage the hostile aircraft. The UAV equipped with missiles is functioning as an actor to engage the hostile aircraft. We also developed a combat scenario of executing this tactic where the manned-unmanned teaming is engaging a hostile aircraft. The hostile aircraft is equipped with both missiles and radar. To demonstrate the efficiency of the tactic, we run the simulation of the scenario of the tactic. Using the simulation, we found the optimal formation and maneuver for the manned-unmanned teaming where the manned-unmanned teaming can survive while the hostile aircraft is shot-downed. The result of this study can provide an insight to how manned aircraft can collaborate with UAV to carry out air-to-air combat missions.

본 연구의 목적은 무인기 정사영상 정보를 활용하여 초미세 고해상도 3차원 공간 모델을 구축하고, 구축된 모델을 이용하여 개체목의 수고와 흉고직경을 추정하는 기술을 분석하고자 하였다. 이를 위해 경상북도 봉화군 일대 잣나무 조림지를 대상으로 무인기 정사영상을 촬영하였으며, SfM 기술을 이용해 촬영영상에 대한 3차원 수고 모델을 추출하였다. 유역분류 알고리즘을 이용해 개체목을 선별·추출하였고, 개체목별 수관면적에 따른 흉고직경을 추정하였다. 본 연구 결과에 의하면, 추출된 수고모델과 현장에서 측정한 수고는 평균 제곱근 편차에서 1.492m (R2 = 0.3401) 차이를 보였으며, 오차율이 가장 적은 수고모델 추출 방법은 지형분석지점 사양이 각도 20°, 이격거리 1m, 격자크기 60m 이었다. 개체목 추출율은 75.4% 이었으며, 수고가 높은 우세목의 추출율은 85.2% 이상이었다. 추출된 개체목의 수관면적과 흉고직경의 상관성은 두 변수 사이에 유의 수준(P<0.01)에서 상관관계가 있었으며, 적합도 77.05% 수준에서 수관면적이 커질수록 흉고직경도 증가하는 추세를 확인할 수 있었다.

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

본 연구는 소방무인기 운용지침(안)을 제안하고자 비행환경 계측과 영상분석을 실시하였다. 데이터 수집을 위 해 계측용 소방무인기를 이용한 재난 및 사고현장의 고도별 온도, 고도별 풍속 측정 및 데이터 분석을 통한 비행영향 요소, 비행임무조건, 비행한계 등을 도출하였다. 특히 무인항공기의 운용에 있어 장애요인을 분석하고 실질적인 운용 계획을 수립하고자 산악실험, 해안실험, 고도별 가시도 실험 등 다양한 실험을 실시하였으며, 사고현장 비행계획을 위한 재난 현장 비행사례 영상을 분석하였다.