• Smart Media Journal
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• v.7 no.3
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• pp.64-71
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• 2018

Utilization of unmanned aerial vehicles (UAVs) are rapidly expanding to various fields ranging from defense, industry, entertainment and personal hobbies. Due to the increased activities of unmanned airplanes, many security problems have emerged, including flight path errors to undesired destinations, secondary threats due to exposed securities caused by the capture of unmanned airplanes in hostile countries. In this paper, we find security vulnerabilities in UAVs such as GPS spoofing, hacking captured video information, malfunction due to signal attenuation through jamming, and exposure of personal information due to image shooting. In order to solve this problem, the stability of the unstructured data is secured by setting the encryption of the video shooting information section using the virtual private network (VPN) to prevent the GPS spoofing attack. In addition, data integrity was ensured by applying personal information encryption and masking techniques to minimize the secondary damage caused by exposure of the UAV and to secure safety. It is expected that it will contribute to the safe use and stimulation of industry in the application field of UAV currently growing.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.568-573
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• 2018

UAV(Unmanned Aerial Vehicle) is widely used in space information construction, agriculture, fisheries, weather observation, communication, and entertainment fields because they are cheaper and easier to operate than manned aircraft. In particular, UAV have attracted much attention due to the speed and cost of data acquisition in the field of spatial information construction. However, ortho image images produced using UAVs are distorted in buildings and forests. It is necessary to solve these problems in order to utilize the geospatial information field. In this study, fixed wing, rotary wing, vertical take off and landing type UAV were used to detect distortions of ortho image of UAV under various conditions, and various object areas such as construction site, urban area, and forest area were captured and analysed. Through the research, it was found that the redundancy of the unmanned aerial vehicle image is the biggest factor of the distortion phenomenon, and the higher the flight altitude, the less the distortion phenomenon. We also proposed a method to reduce distortion of orthoimage by lowering the resolution of original image using DTM (Digital Terrain Model) to improve distortion. Future high-quality unmanned aerial vehicles without distortions will contribute greatly to the application of UAV in the field of precision surveying.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.197-206
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• 2009

To improve the aerodynamic efficiency of Smart Unmanned Aerial Vehicle (SUAV), vortex generators and flow fence are applied on the surface and the tip of wing. The initially applied vortex generator increased maximum lift coefficient and delayed the stall angle while it produced excessive increase in drag coefficient. It turns out reduction of the airplane's the lift/drag ratio. The new vortex generators with L-shape and two different height, 3mm and 5mm, were used to TR-S4 configuration to maintain the desired level of maximum lift coefficient and drag coefficient. Flow fence was also applied at the end of both wing tip to reduce the interaction between nacelle and wing when nacelle tilting angles are large enough and produce flow separation. To examine the effect of flow fence, flow visualization and force and moment measurements were done. The variation of the aerodynamic characteristics of SUAV after applying flow control devices are summarized.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.21-28
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• 2017

The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1457-1463
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• 2018

In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the generated path taking the locations of the obstacles into consideration. This paper proposes a natural path generation method between waypoints based on flight records collected through UAVs flown by users. Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints. These paths are generated by selection of the center path corresponding to the highest Bayesian probability. While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the proposed method generates paths that allow UAVs to avoid obstacles.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.167-174
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• 2023

This study evaluated slope stability through a case study to determine the disaster risks associated with increased deforestation in structures, including schools and apartments, located in urban areas adjacent to slopes. The slope behind the ○○ High School in Gwangju, Korea, collapsed owing to heavy rain in August 2018. Historically, rainwater drained well around the slope during the rainy season. However, during the collapse, a large amount of seepage water flowed out of the slope surface and a shallow failure occurred along the saturated soil layer. To analyze the cause of the collapse, the images of the upper area of the slope, which could not be directly identified, were captured using unmanned aerial vehicles (UAVs). A digital elevation model of the slope was constructed through image analysis, making it possible to calculate the rainfall flow direction and the area, width, and length of logging areas. The change in the instability of the slope over time owing to rainfall lasting ten days before the collapse was analyzed through numerical analysis. Imaging techniques based on the UAV images were found to be effective in analyzing ground disaster risk maps in urban areas. Furthermore, the analysis was found to predict the failure before its actual occurrence.

• ETRI Journal
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• v.45 no.5
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.828-834
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• 2023

In this paper, we studied an antenna that forms a wide beam width by applying a horn-shaped reflector to a patch array antenna. To implement a wide beam width, three patches were arranged in each of the four directions on a square microstrip substrate, and a horn-shaped reflector was applied to the rear of the array antenna. Through this structure, the vertical beam pattern formed from the patch was converted to a diagonal direction, and as a result, the beam widths formed in each of the four sectors were added to create a wide beam width close to a hemisphere. The proposed antenna was studied for application to UAV(unmanned aerial vehicle), and the simulation test results confirmed that the 4.5 dBi beam width was 146.8°.