• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.288-298
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• 2015

For the commercialization of unmanned aircraft, we must validate the safety of the air/ground collision alert systems (CAS). The validation procedure of CAS requires the flight test which is not only expensive but also dangerous. To alleviate this problem, we need the simulation based validation process for the CAS. We developed an integrated UAV simulation (IUS) environment which interconnect the flight simulator, the Matlab/Simulink, and a target avionics simulation model. We developed the collision warning module of the TCAS and tested using IUS and flight encounter models. Using IUS, we can evaluate the performance and reliability of a target avionic system at the preliminary design stage of a development life cycle.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.179-190
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• 2017

A terrestrial LiDAR survey was conducted and unmanned aerial vehicle(UAV) images were taken for target cultural properties to present the utilization measures of terrestrial LiDAR and UAV in three-dimensional modeling of cultural properties for the identification of the status and restoration of cultural properties. Then the accuracy of the point clouds generated through this process was compared, an overlap analysis of the 3D model was conducted, and a convergence model was created. According to the results, the modeling with terrestrial LiDAR is more appropriate for precise survey because 3D modeling for the detection of displacement and deformation of cultural properties requires an accuracy of mm units. And UAV model has limitation as the impossibility of detailed expression of parts with sharp unevenness such as cracks of bricks. However, it is found that the UAV model has a wide range of modeling and has the advantage of modeling of real cultural properties. Finally, the convergence model created in this study using the advantages of the terrestrial LiDAR model and the UAV model could be efficiently utilized for the basic data development of cultural properties.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1506-1513
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• 2019

Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.25-33
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• 2022

In this study, a monitoring method using high-resolution images acquired by unmanned aerial vehicles and deep learning algorithms was proposed for the management of the Sinduri coastal sand dunes. Class classification was done using U-net, a semantic division method. The classification target classified 3 types of sand dune vegetation into 4 classes, and the model was trained and tested with a total of 320 training images and 48 test images. Ignored label was applied to improve the performance of the model, and then evaluated by applying two loss functions, CE Loss and BCE Loss. As a result of the evaluation, when CE Loss was applied, the value of mIoU for each class was the highest, but it can be judged that the performance of BCE Loss is better considering the time efficiency consumed in learning. It is meaningful as a pilot application of unmanned aerial vehicles and deep learning as a method to monitor and manage sand dune vegetation. The possibility of using the deep learning image analysis technology to monitor sand dune vegetation has been confirmed, and it is expected that the proposed method can be used not only in sand dune vegetation but also in various fields such as forests and grasslands.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.78-83
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• 2018

Rapidity and accuracy are important considerations when a drone is employed in a wide surveillance area to detect a target. They are more important when the scope of application is a search and rescue operation or the monitoring of natural disasters, which may require prompt warnings and response. During the actual operation of a drone, rapidity and accuracy are associated with the change in the altitude of the drone. The aim of this study is to analyze the characteristics of drones at varying altitudes and prove that altitude is a relevant factor in the performance of drones. Herein, the characteristics of the drone at varying altitudes were analyzed through several search simulations. The results suggest that a high-altitude drone is relatively advantageous compared to a low-altitude drone in a probability-based target search, and that the search altitude is also a very important and fundamental factor in target search by drones.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1178-1186
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• 2019

In recent years, interest in drones has grown, and many countries are developing them into a strategic industry of the future. Drones are not only used in industries such as logistics and agriculture but also in various public sectors such as life rescue, disaster investigation, traffic control, and firefighting. One of the most important tasks of a drone is to accurately identify targets in these applications. Target recognition may vary depending on the search environment of the drone. Therefore, this study tests and analyzes the drone's target recognition performance according to changes in the search environment such as the search altitude and the search angle. In addition, we propose a new algorithm that improves upon the disadvantages of the Haar cascade method, which is the existing algorithm that recognizes the target by analyzing a captured image.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.57-65
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• 2015

Global Navigation Satellite System (GNSS) including Global Positioning System (GPS) is an important element for navigation of both the military and civil Unmanned Aerial Vehicle (UAV). Contrary to the military UAVs, the civil UAVs use the civil signals which are unencrypted, unauthenticated and predictable. Therefore if the civil signals are counterfeited, the civil UAV’s position can be manipulated and the appropriate movement of the civil UAV to the target point is not achieved. In this paper, spoofing on the autonomous navigation UAV is implemented through field experiments. Although the demanded conditions for appropriate spoofing attack exists, satisfying the conditions is restricted in real environments. So, the Way-point of the UAV is assumed to be known for experiments and assessments. Under the circumstances, GPS spoofing signal is generated based on the Software-based GNSS signal generator. The signal is emitted to the target UAV using the antenna of the spoofer and the effect of the signal is analyzed and evaluated. In conclusion, taking the UAV to the target point is hardly feasible. To implement the spoofing as expectation, the position and guidance system of the UAV has to be known. Additionally, the GPS receiver on the UAV could be checked whether it appropriately tracks the spoofing signal or not. However, the effect of the spoofing signal on the autonomous UAV has been verified and assessed through the experimental results. Spoofing signal affects the navigation system of the UAV so that the UAV goes off course or shows an abnormal operation.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.63-72
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• 2010

In the future warfare, the importance of the counter-fire operation is increasing. The counter-fire operation is divided into offensive counter-fire operation and defensive counter-fire operation. Reviewing the researches done so far, the detection asset of offensive counter-fire operation called UAV(Unmanned Aerial Vehicle) and its operational effectiveness analysis is continually progressing. However, the analysis of the detection asset of defensive counterfire called Target Acquisition Radar(TAR) and its quantitative operational effectiveness are not studied yet. Therefore, in this paper, we studied operational effectiveness of TAR using C2 Theory & MANA Simulation model, and showed clear quantitative analysis results by comparing both cases of using TAR and not using TAR.

• Aerospace Engineering and Technology
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• v.4 no.2
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• pp.7-14
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• 2005

This paper describes development of automatic flight control system for an unmanned target drone. Current target drone is operated by pilot control of on-board servo motor via remote control system. Automatic flight control system for the target drone greatly reduces work load of ground pilot and can increase application area of the drone. Most UAVs being operated nowdays use high-priced sensors as AHRS and IMU to measure the attitude, but those are costly. This paper introduces the development of low-cost automatic flight control system with low-cost sensors. The integrated automatic flight control system has been developed. The performance of automatic flight control system is verified by flight test.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.101-106
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• 2010

Researches on air-ground robot cooperating system has been made recently. The cooperation among homogeneous robots focused on the architecture of the system, quality and influence of the communication. In contrast, the cooperation among heterogeneous robots such as aerial vehicle and ground vehicle robots has not been much handled. There are a couple of main points for those air-ground cooperating robots. One is using UAV (Unmanned Aerial Vehicle) as an extra sensor of UGV (Unmanned Ground Vehicle). This kind of application is usually used in situations such as guiding UGV to an appropriate path which could be better determined from the eye in the sky as UAV. The other main application of air-ground cooperating robot system is the localization. By combining sensor information from both UAV and UGV, the robot system as a whole can localize a target object or find features in the environment with better performance than UGV or UAV alone. Although these applications are recently studied in many different ways and devices, there are still a lot of possibilities in the field of air-ground cooperating robot systems. We introduce those research fields in this paper.