• 한국군사과학기술학회지
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• 제15권1호
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• pp.1-8
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• 2012

Obstacle detection is much studied by using sensors such as laser, vision, radar and ultrasonic in path planning for UGV(Unmanned Ground Vehicle), but not much reported about its characterization. In this paper not only an obstacle classification method using 2-dimensional LMS(Laser Measurement System) but also a decision making method whether to avoid or traverse the obstacle is proposed. The basic idea of decision making is to classify the characteristics by 2D laser scanned data and intensity data. Roughness features are obtained by range data using a simple linear regression model. The standard deviations of roughness and intensity data are used as measures for decision making by comparing with those of reference data. The obstacle classification and decision making for the UGV can facilitate a short path to the target position and the survivability of the robot.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.147-155
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• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• 산업경영시스템학회지
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• 제36권1호
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• pp.78-85
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• 2013

Since the basic built-in-test, prognostic health management (PHM) has evolved into more sophisticated and complex systems with advanced warning and failure detection devices. Aerospace and military systems, manufacturing equipment, structural monitoring, automotive electronic systems and telecommunication systems are examples of fields in which PHM has been fully utilized. Nowadays, the automotive electronic system has become more sophisticated and increasingly dependent on accurate sensors and reliable microprocessors to perform vehicle control functions which help to detect faults and to predict the remaining useful life of automotive parts. As the complication of automotive system increases, the need for intelligent PHM becomes more significant. Given enormous potential to be developed lays ahead, this paper presents findings and discussions on the trends of automotive PHM research with the expectation to offer opportunity for further improving the current technologies and methods to be applied into more advanced applications.

• 전력전자학회논문지
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• 제13권6호
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• pp.430-438
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• 2008

최근 영구자석 동기전동기는 여러 장점으로 인해 많은 응용 분야에서 적용이 확대되고 있다. 특히 전기 자동차, 항공기 분야, 의료 및 군사 분야에 그 적용이 활발히 진행되고 있어 상대적으로 고 신뢰 운전이 매우 중요한 과제로 대두되고 있으며, 특히 전압 원 인버터의 고장 감지 및 진단 등에 관한 많은 연구가 진행되고 있다. 본 논문에서는 영구자석 동기전동기의 개방 고장을 감지하고 진단하는 방법을 제안하였고, 모의해석 및 실험을 통해 제안된 방법이 실제 적용이 가능성을 확인하였다. 제안된 방법은 기존의 개방 고장 감지 방법과 달리 별도의 하드웨어를 요구하지 않으며, 또한 빠른 개방 고장 감지 특성을 가지고 있다.

• 한국군사과학기술학회지
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• 제18권4호
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• pp.358-368
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• 2015

Recently, unmanned systems are largely utilized in various fields due to the persistency and the least operational risk and an unmanned surface vehicle(USV) is the one of the representative application in the naval field. To assign multiple roles to an USV, we developed a sonar system which consists of a forward detecting sonar for the long-range detection, a downward detecting sonar for the small target scan and identification, and a strut type body for mounting sonar systems. In this paper, we described the developed sonar system for USV and the sea test results for verifying system performance. The test results showed that the developed sonar system was able to detect the underwater target about several kilometers away and could recognize a small object at the downside of the sonar system. We expect that the developed sonar system will be easily applied to other unmanned platforms without serious consideration.

• 한국군사과학기술학회지
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• 제23권5호
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• pp.466-474
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• 2020

Semi-active laser missile systems with high accuracy are necessary to asymmetric threats, such as UAV(Unmanned Aerial Vehicle). They are usually used to attack stationary or slow moving targets, therefore we should study on the laser designator which can detect and track fast moving targets in order to deal with UAV. In this study, design specifications are came up through performance analysis of existing laser designators, and laser designation method for fast moving target is developed. The detection and tracking performance of developed laser designator are verified through inside/outside tests on ground/aerial stationary/moving targets. Through this study, we obtain laser designator techniques that could be applied to actual semi-active laser missile systems.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.344-347
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• 2006

Noise reduction and control is an important problem in the performance of underwater acoustic system and on the habitability of the passenger ship for crew and passenger. Furthermore, sound generated by a propeller is critical in underwater detection and is often related to the survivability of the vessel especially for military purpose. Generally propeller noise is often the dominant noise source of marine vehicle. The flow field is analyzed with potential-based panel method, and then the time dependent pressure and sheet cavity volume data are used as the input for Ffowcs Williams-Hawkings formulation to predict the far-field acoustics. Through this study, the dominant noise source of underwater propeller is analyzed, which will provide a basis for proper noise control strategies.

• Journal of Mechanical Science and Technology
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• 제19권8호
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• pp.1529-1543
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• 2005

Recently an unmanned aerial vehicle (UAV) has been widely used for military and civil applications. The role of a navigation system in the UAV is to provide navigation data to the flight control computer (FCC) for guidance and control. Since performance of the FCC is highly reliant on the navigation data, a fault in the navigation system may lead to a disastrous failure of the whole UAV. Therefore, the navigation system should possess a fault detection and isolation (FDI) algorithm. This paper proposes an attitude determination GPS/INS integrated navigation system with an FDI algorithm for a UAV. Hardware for the proposed navigation system has been developed. The developed hardware comprises a commercial inertial measurement unit (IMU) and the integrated navigation package (INP) which includes an attitude determination GPS (ADGPS) receiver and a navigation computer unit (NCU). The navigation algorithm was implemented in a real-time operating system with a multi-tasking structure. To evaluate performance of the proposed navigation system, a flight test has been performed using a small aircraft. The test results show that the proposed navigation system can give accurate navigation results even in a high dynamic environment.

• 로봇학회논문지
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• 제19권1호
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• pp.79-91
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• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• 한국ITS학회 논문지
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• 제23권1호
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• pp.112-122
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• 2024

최근 드론과 같은 무인비행장치 기술이 발전함에 따라 환경적, 사회적 및 경제적으로 많은 이점이 있지만, 공항, 공공기관, 발전소, 군 등 국가중요시설에 악의적인 의도를 가질 경우 국가 안전과 국민 생활에 심각한 피해를 줄 수 있다. 이러한 드론의 위협에 대응하기 위해 RF스캐너와 같은 탐지 장비 도입을 시도하고 있다. 특히 변전소, 발전소, 우리나라 전력 계통에 의해 설치된 전력 전송용 송전탑은 RF스캐너 탐지 경로에 송전탑이 위치하면 탐지 성능에 영향을 줄 수 있다. 실험은 상용 드론을 이용하여 드론에서 방사되는 신호 세기 측정하여 감쇠율을 확인하였다. 평균 감쇠율과 최대 감쇠율은 2.4 GHz와 5.8 GHz 대역에서 유사한 경향을 보였고, 구조물의 밀도에도 영향을 받는 것을 알 수 있다.