• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.1
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• pp.81-88
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• 2020

Flight data recorder is a equipment that records data required for investigation of aircraft accidents and should be developed in compliance with the ED-112A standard. Unlike general data storage device, flight data recorder must be able to recover data after an aircraft accident, requiring a housing and a memory board to protect data in extreme environments. To attain this performance, we designed a housing that can withstand the test by analyzing the physical environment of the impact, shear/tensile, penetration resistance and static crush test of the crash survival test and minimized the size and weight compared to the existing one in consideration of the installation of the aircraft in this paper. Insulation material and thermal block material were applied to endure high and low temperature fire so that the internal temperature does not rise above 150℃ even in 260℃, 10 hour environment. In addition, the memory board is designed to minimize the size and we devise a hoping programming method to prevent continuous data loss of more than 16 seconds. Through this, Crash protected memory module that satisfies ED-112A was completed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.74-81
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• 2004

In this study preliminary structural design has been performed to develop an all composite wing box for experimental aircraft(classified in FAR Part 21). Considerations on composite materials and their manufacturing process were taken into account throughout the design phase. Aerodynamic loads were estimated by using Shrenk method(NACA TM No 948) and FAR Part 23 Appendix A. The structural layout has been determined to carry effectively the critical loads and to maximize the benefit of composite structure. Maximum strain failure allowable and first ply failure criteria were applied for the sizing of major structural members. Finally, the designed composite wing box structure is presented in the form of drawings, which include material specifications, stacking sequences and joint design.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.6
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• pp.96-103
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• 2004

Sought a set of component performance lines from experiment data or some data supplied in the engine manufacturer to improve the traditional scaling method and suggested a map scaling method that construct component performance lines newly using polynomial equations of MATLAB program. In this study, applied technique that is proposed newly to PT6A-62 that verified technique that is proposed newly using experiment data of small. size turboshaft engine, and is actuality aircraft engine. In identification of the component maps of the turboprop engine, the simulated performance using the proposed scaling method was compared with the real engine performance data and the performance using the traditional scaling method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.1
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• pp.99-104
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• 2009

In this paper, the drop characteristic of the small aircraft landing gear of SUAV has been analyzed and performed on orifice optimal design for shock absorption efficiency. The SUAV landing gear was simple oleo pneumatic type without metering pin. The landing gear was modelled by MSC ADAMS software. Drop test evaluation was conducted to confirm the analysis model. As a result of correlation between analysis and test results, it was verified that these results were coincided with very well. After confidence review of analysis model through the correlation between test and analysis results, design parametric study was performed by using confirmed analysis model. Optimal orifice size with best efficiency have been decided in this study.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• Aerospace Engineering and Technology
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• v.2 no.1
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• pp.22-28
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• 2003

This paper presents a method for estimating static aerodynamic derivatives by analyzing data obtained from the flying quality evaluation test of a small canard aircraft. The aerodynamic derivatives extracted from maximum likelihood estimation method and from the proposed method in this paper are compared in the same polt. Reliable static aerodynamic derivatives were extracted from a limited number of the flight tests by the proposed method. The parameter data obtained from this method can be used as reference for the conventional parameter identification methods such as maximum likelihood estimation method.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.313-318
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• 2012

In the present study, the preliminary study on a small solar-powered RC airplane are performed for the development of a long-endurance solar-powered UAV. Solar energy enables the solar-powered UAV to fly longer or eternally. The solar-powered UAV transfers the solar energy to electric energy and this energy is used for the flight and the battery charge. To increase the flying time, the efficiency of the solar-cell power system must be increased and the required power for flight must be minimized. Hence, the system integration including solar cell and controller, the power system design, and the aerodynamic and structural designs of the UAV is very important. The present study have performed the design, manufacture, and flight test of the small solar-powered UAV for the preliminary study of the long-endurance solar-powered UAV. From this study, the system integration technology of the solar-powered UAV design is established, and the possibility and the issue points for the development of the long-endurance solar-powered UAV are discussed.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.10
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• pp.1061-1068
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• 2013

In this research, the design optimization of a composite sandwich has been performed for using as an airplane wing skin. Automated analysis framework for aero-structure interaction is used for calculating load data on the wing. For automated analysis framework, FLUENT is used for computational fluid dynamics (CFD) analysis. CFD mesh is generated automatically by using parametric modeling of CATIA and GAMBIT. A computational structure mechanics (CSM) mesh is generated automatically by the parametric method of the CATIA and visual basic script of NASTRAN-FX. The structure is analyzed by ABAQUS. Composite sandwich optimization is performed by NASTRAN SOL200. Design variables are thicknesses of the sandwich core and composite skin panel plies. The objective is to minimize the weight of the wing and constraints are applied for wing tip displacement, global failure index and local failure indexes.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.