본 연구에서는 무인항공기인 드론을 활용한 VDMS(Vision-based Displacement Measurement System)를 통해 동적변위계측 정 확도와 동특성 추정 신뢰성 검증을 위한 동적실험을 실시하였다. 비행하는 드론의 이동 및 회전진동을 보정하기 위해 영상 내부의 변 위가 발생하지 않는 고정점을 활용한 보정밥법을 사용하였으며, 검증을 위해 설치한 범용 센서인 LVDT와 LDS의 변위계측 결과와 비 교하여 그 오차를 시간영역과 진동수영역에서 분석하였다. 3가지 타입의 장비 모두 최대 변위 도달 및 주기 운동 계측에 있어서 대체 적으로 유사한 결과를 나타내었다. LDS 기준의 오차 분석 결과, 드론과 LVDT는 가진 진동수 변화에 의한 오차 값은 미비하나, 최대 발생 변위가 작을수록 오차 값은 증가하였다.

Airborne surveys are an essential analysis method for rapid response and contamination identification in the early event of a radiation emergency. On the other hand, airborne surveys are far from the ground, so it is necessary to convert the dose rate at a height of 1 m above the ground. In order to improve the accuracy of the analysis value, a lot of analysis of the measurement data is required. In our previous research, we developed MARK-A1, an instrument for rapid radiation aerial survey in high radiation environment, and MARK-M1, a multipurpose instrument for radiation detection. In this study, a large unmanned aerial vehicle (UAV) was used to conduct airborne surveys using three types of detectors in the Jeju Island environment. The NaI(Tl) detector uses one 3-inch scintillator (38 mm φ × 38 mm H), and the LaBr3 detector uses two 2-inch scintillators (25 mm φ × 25 mm H). The CZT detector uses a detector with dimensions of (15 mm × 15 mm × 7.5 mm). The UAV has a payload of 15 kg (J10, JCH systems Inc. Seoul, Korea). Three different detectors were operated at a constant height of 20 m, 30 m, and 50 m. The flight experiments were performed in the west near Jeju City. Dose rate conversion factors were used to convert the dose rate from the measured spectra, and hovering flights were conducted between 1 and 50 meters to derive altitude correction factors. In this paper, the data measured with each detector in the same area were compared and the differences were derived.

UAVs (Unmanned Aerial Vehicle) are a rising threat to national facilities due to their cheap price and accessibility. Incidents such as the terrorism attack in Saudi Arabia’s oil facilities and the paralysis of the airport system in England’s Gatwick airport shows the need for integrating CUAS (Counter- Unmanned Aerial Systems) in important national facilities. Recently efforts have been made to evaluate the technical performance of the CUAS. Especially SNL (Sandia National Laboratory) modified the methodology used for PPS (Physical Protection Systems) to develop a performance metrics for CUAS. The performance metrics can be used to effectively analyze the facilities capability of countering drone attacks in a probabilistic way. In this study, we managed to derive the safety boundary of a reference nuclear power plant model based on its current CUAS and protection capabilities with a simplified methodology. Based on the outermost boundary of the model, the time table of the UAS consist of 4 variables which are the assessment time, transmission time, neutralization time and the maximum vehicle velocity. Dividing the maximum velocity to the net time derived, we estimated the minimum sensing point of the CUAS which is the minimum safety boundary of the facility to safely manage the UAV attack. Two practice cases were evaluated with the methodology which is based on the UAV groups classified by the United States DOD (Department Of Defense) that matches the classification of the UAV in Korea. Each variable was assumed to fit the process of a realistic nuclear power plant. Using the variables, we calculated the minimum safety boundary of the facility. With the methodology introduced in this study, regulators and stakeholders can easily evaluate the capability of the facilities CUAS for a design basis UAV attack. Also it can be used as a simple tool to analyze the facilities vulnerability for specific UAV specifications and a guideline to check the protective procedures of the facility.

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.

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.

Recently, unmanned aerial vehicles (UAV, Drone) are highly regarded for their potential in the agricultural field, and research and development are actively conducted for various purposes. Therefore, in this study, to present a framework for tracking research trends in UAV use in the agricultural field, we secured a keyword search strategy and analyzed social network, a methodology used to analyze recent research trends or technological trends as an analysis model applied. This study consists of three stages. As a first step in data acquisition, search terms and search formulas were developed for experts in accordance with the Keyword Search Strategy. Data collection was conducted based on completed search terms and search expressions. As a second step, frequency analysis was conducted by country, academic field, and journal based on the number of thesis presentations. Finally, social network analysis was performed. The analysis used the open source programming language 'Python'. Thanks to the efficiency and convenience of unmanned aerial vehicles, this field is growing rapidly and China and the United States are leading global research. Korea ranked 18th, and bold investment in this field is needed to advance agriculture. The results of this study's analysis could be used as important information in government policy making.

The purpose of this study is to evaluate the fixed wing type domestic UAV for monitoring of algae bloom in aquatic environment. The UAV used in this study is operated automatically in-flight using an automatic navigation device, and flies along a path targeting preconfigured GPS coordinates of desired measurement sites input by a flight path controller. The sensors used in this study were Sequoia multi-spectral cameras. The photographed images were processed using orthomosaics, georeferenced digital surface models, and 3D mapping software such as Pix4D. In this study, NDVI(Normalized distribution vegetation index) was used for estimating the concentration of chlorophyll-a in river. Based on the NDVI analysis, the distribution areas of chlorophyll-a could be analyzed. The UAV image was compared with a airborne image at a similar time and place. UAV images were found to be effective for monitoring of chlorophyll-a in river.

Modern technique development provides a new opportunity to expand entomological researches. Aerial insect sampling has been conducted with fixed-wing unmanned aerial vehicles (UAVs). With improved maneuverability, rotary-wing UAVs can serve as more convenient and feasible tools with lower cost. A rotary-wing UAV with remotely controlled insect nets was developed to collect insects at designated altitudes above the rice field (ca. 80 × 240 meter (width × length)) in Boryeong, South Korea. From 21 flights in June, July, and August 2017, 235, 7, 6, and 3 insects were caught at 5, 10, 50, and 100 meters above the rice field, respectively. The collected insects were identified to family level. Diptera (Phoridae and Chironomidae), Hemiptera (Aphididae and Delphacidae), and Thysanoptera were found from the sample, some of which may contain possible insect pests on rice. Therefore, UAVs have potential as an alternative aerial insect sampling method.

This study was carried out to apply the UAV(Unmanned Aerial Vehicle) coupled with Multispectral sensor for the algae bloom monitoring in river. The study acquired remote sensing data using UAV on the midstream area of Gum River, one of four major rivers in South Korea. Normalized difference vegetation index (NDVI) is used for monitoring algae change. This study conducted water sampling and analysis in the field for correlating with NDVI values. Among the samples analyzed, the chlorophyll concentration exhibited strong and significant linear relationships with NDVI, and thus NDVI was chosen for algae bloom index to identify emergence aspect of phytoplankton in river. Aerial remote sensing technology can provide more accurate, flexible, cheaper, and faster monitoring methods of detecting and predicting eutrophication and therefore cyanobacteria bloom in water reservoirs compared to currently used technology. As a result, there was high level of correlation in chlorophyll-a and NDVI. It is expected that when this remote water quality and pollution monitoring technology is applied in the field, it would be able to improve capabilities to deal with the river water quality and pollution at the early stage.

Recently, remote sensing technology as a nondestructive method has been utilized to detectthe quantity and quality of crops using unmanned aerial system. To predict vegetation growth(leaf dry mass and nitrogen content) of soybean, two vegetation index(NDVI and Green NDVI)were calculated from images acquired by multi-spectral camera mounted on a UAV and eachprediction models between vegetation growth and index were evaluated. As a result, there wasno significant difference between vegetation growth and index when each vegetation stage foreach yellow and black bean were compared to each other. However, there was significantdifference between vegetation growth and index when all vegetation stage for each yellow andblack bean were compared to each other. Moreover, there was significant difference betweenvegetation growth and NDVI(r= 0.799 for leaf dry mass, r= 0.796 for nitrogen content), andGreen NDVI(r= 0.860 for leaf dry mass, r= 0.845 for nitrogen content) for all vegetation stageswith all soybeans. The accuracy and precision of Green NDVI model(R2= 0.740 for leaf drymass, R2= 0.714 for nitrogen content) were better than those of NDVI model regardless ofvarieties and vegetation growth. Therefore, Green NDVI has considerable potential to detect thequantity and quality of soybeans.

UAV (Unmanned Aerial Vehicle), the pilotless plane or drone, draws researchers’ attention at these days for its extended use to various area. The research was initiated for military use of the UAV, but the area of applicable field is extended to surveillance, communication, and even delivery for commercial use. As increasing the interest in UAV, the needs of research for operating the flying object which is not directly visible when it conducts a certain mission to remote place is obviously grown as much as developing high performance pilotless plane is required. One of the project supported by government is related to the use of UAV for logistics fields and controlling UAV to deliver the certain items to isolated or not-easy-to-access place is one of the important issues. At the initial stage of the project, the previous researches for controlling UAV need to be organized to understand current state of art in local researches. Thus, this study is one of the steps to develop the unmanned system for using in military or commercial. Specifically, we focused on reviewing the approaches of controlling UAV from origination to destination in previous in-country researches because the delivery involves the routing planning and the efficient and effective routing plan is critical to success to delivery mission using UAV. This routing plan includes the method to avoid the obstacles and reach the final destination without a crash. This research also present the classification and categorization of the papers and it could guide the researchers, who conduct researches and explore in comparable fields, to catch the current address of the research.

data. Recent developments in unmanned aerial vehicle (UAV) technology provide cost effective and real time applications for site specific data collection. For the mapping of herbage biomass (BM) on a hill pasture, we tested a UAV system with digital cameras (visible and near-infrared (NIR) camera). The field measurements were conducted on the grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17 and June 27, 2014. Plant samples were obtained from 28 sites. A UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number (DN) values of Red and NIR channels were extracted from the aerial photos and a normalized differential vegetation index using DN (NDVIdn) was calculated. The results show that the correlation coefficient between BM and NDVIdn was 0.88. For the precision management of hilly grazing pastures, UAV monitoring systems can be a quick and cost effective tool to obtain site-specific herbage BM data.

An unmanned aerial vehicle (UAV) is a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or no

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky