• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.854-864
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• 2016

The use of small drone platform has become a popular topic in these days but its application for SAR operation has been little known due to the burden of the payload implementation. Drone platforms are distinguished from the conventional UAV system by the increased vulnerability to the turbulences, control-errors and poor motion stability. Consequently, sophisticated motion compensation may be required to guarantee the successful acquisition of high quality SAR imagery. Extremely limited power and mass budgets may prevent the use of additional hardwares for motion compensation and the difficulty of SAR focusing is further aggravated. In this paper, we have carried out a feasibility study of mico-SAR drone operation. We present the image acquisition results from the preliminary flight tests and a quality assessment is followed on the experimental SAR images. The in-flight motion errors derived from the unique drone movements are investigated and attempts have been made to compensate for the geometrical and phase errors caused by motions against the nominal trajectory. Finally, the successful operation of drone SAR system is validated through the focussed SAR images taken over test sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.79-89
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• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1155-1167
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• 2018

We developed a small sensor observation system (SSOS) at a relatively low cost to observe the atmospheric boundary layer. The accuracy of the SSOS sensor was compared with that of the automatic weather system (AWS) and meteorological tower at the Korea Meteorological Administration (KMA). Comparisons between SSOS sensors and KMA sensors were carried out by dividing into ground and lower atmosphere. As a result of comparing the raw data of the SSOS sensor with the raw data of AWS and the observation tower by applying the root-mean-square-error to the error, the corresponding values were within the error tolerance range (KMA meteorological reference point: humidity ${\pm}5%$, atmospheric pressure ${\pm}0.5hPa$, temperature ${\pm}0.5^{\circ}C$. In the case of humidity, even if the altitude changed, it tends to be underestimated. In the case of temperature, when the altitude rose to 40 m above the ground, the value changed from underestimation to overestimation. However, it can be confirmed that the errors are within the KMA's permissible range after correction.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.649-656
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• 2023

Recently, global warming has become severe, and regulation is established for carbon savings each field. its regulation is applied to various fields using IC engine such as automobile, ship, agricultural machine. Therefore engine block applied Common Rail Direct Injection(CRDI) technology, that carry out thermal-structure analysis to examine design. The thermal load about 900℃ by explosion was applied in cylinder. And pressure about 9 MPa(90 Bar) was applied to structure analysis. As a result, it was the highest at 185.99℃ at the top of cylinder. Static-structure analysis applied thermal load, that was shown maximum equivalent stress at 142.59 Mpa and Maximum principal stress 145.03 MPa, Minimum principal stress -149 MPa. When compare analysis results to material property, it design is safety structurally.

• ETRI Journal
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• v.40 no.2
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.27-37
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• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.9
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• pp.774-781
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• 2018

In this investigation, a gas gun is proposed driven by carbon dioxide gas and installed on a quadcopter-type small unmanned drone for the purpose of cattle vaccination, and we developed a launcher and its connection unit. The system consists of a commercial drone, a gas gun, a solenoid valve, and the remote communication controller, etc. The velocity of launched projectile is measured, and the full system is finally validated through ground test and flight examination loaded for the real aircraft. The feasibility is checked if this technology is applicable to various disease abatement and hazard mitigation in the fields of agriculture and fire-fighting with the present research and development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.827-834
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• 2022

Recently, marine surveys using unmanned ships are attracting attention, and research on small unmanned ships using sails is on the rise. Sail drones can be used for marine surveys, monitoring, and pollution management. Therefore, in this study, using the method of estimating the ship speed for twin sail drones, the optimal conditions for sailing are checked, and the performance to be considered in the initial design stage, such as the motion performance and resistance of the sail drone. Consequently, the twin sail drone had a speed lower than 2.0 m/s, and the stability satisfied the rule by DNV. In addition, the maximum speed at an angle of attack of 20° at TWA 100° was 1.69 m/s and that at an angle of attack of 25° at TWA 100° was 1.74 m/s.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.1-11
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• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.