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스펙트럼 밀도 함수를 이용한 풍동실험 강제진동 데이터 분석
양훈민,오세윤,이종건 한국항공우주학회 2014 한국항공우주학회 학술발표회 논문집 Vol.2014 No.11
본 논문은 풍동에서 수행되는 강제진동 실험 데이터를 스펙트럼 밀도 함수를 이용하여 분석하는 방법을 제안한다. 국방과학연구소 저속풍동에 비행체 모형과 실험 장치를 설치하고, 롤 방향으로 강제진동기법을 적용하여 동적 데이터를 얻었다. 롤 축에 대한 각 변위의 진동을 강제적으로 가해주는 동시에 엔코더로 각도를 측정하고, 공력에 의한 모형의 롤링모멘트 반응을 6성분 밸런스로 측정하였다. 측정된 데이터를 스펙트럼 밀도 함수를 이용하여 분석하고, 동안정미계수 결정에 필수적인 롤 가진 각도 신호와 측정된 롤링모멘트 신호 사이의 위상 이동 값을 계산하였다. 본 분석 방법을 통해 동안정미계수를 계산한 결과, 기존 풍동에서 계산된 값들과 같은 경향성을 보임을 확인하였다. In this paper, the method of analyzing the forced oscillatory data using spectral density function is presented. Model and experimental apparatus were installed in the ADD low-speed wind tunnel and dynamic data was obtained by applying the forced oscillation technique to the roll direction. Forcing the roll displacement, the roll angle was measured by encoder and the rolling moment due to aerodynamics was measured by 6-component internal balance. Measured data were analyzed by using spectral density function. Phase shift was calculated between the roll excitation angle signal and the measured rolling moment signal, which is necessary for determining the dynamic stability derivatives. The dynamic stability derivatives calculated by this method has a similar tendency with the previously determined values of other wind tunnel.
오세윤,양훈민,김성철 한국항공우주학회 2015 한국항공우주학회 학술발표회 논문집 Vol.2015 No.4
표준 동역학 모형의 동적 롤댐핑 풍동실험에 적용한 실험설계법 접근법에 관한 연구를 수행하였다. 풍동실험은 실험설계법 기반 풍동실험연구의 일환으로 국방과학연구소 저속 풍동실험실에서 수행되었으며, 이러한 풍동실험연구에 적용한 실험설계법 기반 접근법은 롤 방향 강제진동 동적 실험의 실험효율성을 크게 증가 킬 수 있는 것으로 확인되었다. This paper investigates the use of design of experiments (DOE) approach to the roll-damping dynamic wind tunnel testing of the standard dynamic model (SDM). The wind tunnel testing was conducted at the Agency for Defense Development’s Low Speed Wind Tunnel as part of series of tests using the DOE. The application of the DOE to the roll-oscillation dynamic tests can result in a significant increase in test efficiency.
오세윤,양훈민 한국군사과학기술학회 2023 한국군사과학기술학회지 Vol.26 No.6
. This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection
김정훈,양훈민,오세윤 한국군사과학기술학회 2023 한국군사과학기술학회지 Vol.26 No.1
Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.
오세윤,양훈민 한국군사과학기술학회 2023 한국군사과학기술학회지 Vol.26 No.5
This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection
이종건,오세윤,양훈민,이도관 한국항공우주학회 2014 한국항공우주학회 학술발표회 논문집 Vol.2014 No.11
비행체의 공기역학적 동안정 미계수 측정을 위한 강제진동 제어시스템이 개발되었다. 강제진동 제어시스템은 유압작동기, 서보밸브, 그리고 위치제어시스템을 사용하여 구성하였다. 또한 개발된 강제진동 제어시스템을 사용하여 유로내부에 추가 구조물 설치 없이 강제 진동 풍동 실험을 수행하여 제어시스템의 성능을 확인하였다. An forced oscillation control system for dynamic wind tunnel testing was developed. The forced oscillation control system consists of hydraulic actuator, servo valve, and position control system. Also dynamic wind tunnel test was carried out using the developed forced oscillation control system without additional structure installation in the wind tunnel and the performance of the control system was confirmed.