• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.523-536
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• 2013

A recent air defense missile system is required to have a capability to intercept short-range super-high speed targets such as tactical ballistic missile(TBMs) by performing engagement control efficiently. Since flight time and distance of TBM are very short, the missile defense system should be ready to engage a TBM as soon as it takes an indication of the TBM launch. As a result, it has to predict TBM trajectory accurately with cueing information received from an early warning system, and designate search direction and volume for own radar to detect/track TBM as fast as it can, and also generate necessary engagement information. In addition, it is needed to engage TBM accurately via transmitting tracked TBM position and velocity data to the corresponding intercept missiles. In this paper, we proposed a method to estimate TBM trajectory based on the Kepler's law for the missile system to detect and track TBM using the cueing information received before the TBM arrives the apogee of the ballistic trajectory, and analyzed the bias of prediction error in terms of the transmission period of cueing data between the missile system and the early warning system.

• Journal of Advanced Navigation Technology
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• v.19 no.2
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• pp.133-139
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• 2015

This paper surveys vision based autonomous navigation technologies for unmanned systems. Main branches of visual navigation technologies are visual servoing, visual odometry, and visual simultaneous localization and mapping (SLAM). Visual servoing provides velocity input which guides mobile system to desired pose. This input velocity is calculated from feature difference between desired image and acquired image. Visual odometry is the technology that estimates the relative pose between frames of consecutive image. This can improve the accuracy when compared with the exisiting dead-reckoning methods. Visual SLAM aims for constructing map of unknown environment and determining mobile system's location simultaneously, which is essential for operation of unmanned systems in unknown environments. The trend of visual navigation is grasped by examining foreign research cases related to visual navigation technology.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.898-905
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• 2018

In this work, infrared targets for a developed hardware-in-the-loop simulation(HILS) system are proposed for a performance test of a dual-sensor imaging seeker equipped with an infrared and a visible sensor that can lock and track for ground and air targets. This integrated system is composed of 100 modules of heat and light sources to simulate various kinds of target and the trajectory of moving targets based on scenarios. It is possible to simulate not only the position, velocity, and direction for these targets but also background clutter and jamming environments. The design and measurement results of an infrared target, such as the HILS system configuration, developed for testing and evaluation of a dual-sensor imaging seeker are described. In the future, it is planned to test the lock-on and tracking performance of an imaging seeker equipped with single or dual sensors dynamically in real time based on a simulation flight scenario in the developed HILS system.

• Journal of the Korea Computer Graphics Society
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• v.29 no.4
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• pp.7-16
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• 2023

For decades, creating a desired locomotive motion in a goal-oriented manner has been a challenge in character animation. Data-driven methods using generative models have demonstrated efficient ways of predicting long sequences of motions without the need for explicit conditioning. While these methods produce high-quality long-term motions, they can be limited when it comes to synthesizing motion for challenging novel scenarios, such as punching a random target. A state-of-the-art solution to overcome this limitation is by using a GAN Discriminator to imitate motion data clips and incorporating reinforcement learning to compose goal-oriented motions. In this paper, our research aims to create characters performing combat sports such as boxing, using a novel reward design in conjunction with existing GAN-based approaches. We experimentally demonstrate that both the Adversarial Motion Prior [3] and Adversarial Skill Embeddings [4] methods are capable of generating viable motions for a character punching a random target, even in the absence of mocap data that specifically captures the transition between punching and locomotion. Also, with a single learned policy, multiple task controllers can be constructed through the TimeChamber framework.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.76-76
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• 2023

최근 자율주행과 관련한 시장의 관심은 기존 자동차 자율주행에서 선박 자율운항으로 자연스럽게 이동하고 있다. 이에 인공지능 및 빅데이터 등과 같은 최근 기술을 선박 자율주행에 적용하는 자율운항선박(MASS: Maritime Autonomous Surface Ship) 개발이 활발히 진행되고 있으며, 레이더 및 카메라 등과 같은 센서 정보를 선박 자율운항에 적용하여 다양한 선박 운동 및 정보를 획득하는 연구 기술이 집중되고 있다. 이러한 경향에 따라 IMO(International Maritime Organization)과 같은 국제기구에서도 자율운항선박 표준화 본격 논의로 기술표준 선점 경쟁에 참여하고 있다. 이 중 연안 자율운항선박 개발은 IMO에서 주관하는 무인화 핵심기술로 여겨지고 있어, 기존 대양 항해 기술과 함께 연안 항해에 대한 기술 개발의 중요성이 높아지고 있다. 특히 항만 인근 해역에서는 다수의 선박이 입출항함으로 인해 해상에서의 안전과 물류의 효율화가 요구되기 때문에 고도화된 자율운항 기술개발이 필요하다. 하지만 자율운항선박에서의 상황인식 기술은 탑재된 센서의 제한된 시야각 및 기상조건에 따른 인식률이 떨어지는 문제가 생긴다. 이러한 기술적 한계를 극복하기 위해 육상에 설치된 레이더를 활용하여 선박을 탐지할 수 있는 기술이 필요하다. 본 연구에서는 고해상도 육상 레이더를 기반하여 얻어진 레이더 화면상의 물표 정보를 이용해 인공지능 기법에 활용하기 위한 라벨링 자동 생성 방법에 대해 소개한다. 얻어진 물표 정보에 인공지능 기법을 적용하여 선박 길이 정보를 추정하는 기술에 대해 소개한다.

• Journal of the Korean earth science society
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• v.32 no.4
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• pp.388-401
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• 2011

Asian dust was observed a total of 66 times in the springtime during the period from 2002 to 2010, with 26 cases in March, 23 cases in April and 17 cases in May. This study investigates a Asian dust episode that occurred during the period from 22 to 25 May 2010, based on synoptic weather patterns, wind vector at 850 hPa, relative humidity at 1000 hPa, Jet streams and wind vector at 300 hPa, PM10 concentration in Korea and satellite imagery. In this case, Asian dust originated on 22 May along the rear of a developing low pressure system in Mongolia. The Asian dust was then transported southeastward and bypassed the Korea peninsula from 23 to 24 May, before reaching Japan on 25 May. Jet streams on 24 May bypassed the Korean peninsula and induced the development of a surface low pressure centered over the peninsula. The resulting air flow was critical to the trajectory of the Asian dust, which likewise bypassed the Korean peninsula. 72-hour backward trajectory data reveal that the Shandong Peninsula and the East China Sea were the points of origin for the air flows that swept through the Japanese sites where Asian dust was observable to the naked eay. The Asian dust pathway is ascertained by horizontal distribution of the Asian dust of RGB imagery from MODIS satellites which captured the Asian dust moving over the Shandong Peninsula, the East China Sea, and northwest of the Kyushu region in Japan. Since the synoptic pattern and the transport way of the Asian dust case are far from typical ones, which Asian dust forecasting technique has long been based on, this study can be good example of exceptional Asian dust pattern and it will be used for more accurate Asian dust forecasting.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.4
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• pp.247-253
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• 2005

This study investigated the effects on foot and ankle roll-over characteristics according to different heel heights during walking. Fifteen female volunteers who have neither musculoskeletal nor foot problems were participated in gait analyses, wearing four different pairs of shoes in different heel heights. To obtain roll-over shape of foot/ankle complex, we used trajectories of knee and ankle joints as well as the renter of pressure between initial contact and opposite initial contact. Results revealed that the entire roll-over shape moved distally as the heel height increased but roll-over characteristics showed uniformly with different heel heights. In addition, we found that nondisablea persons automatically adapted to their foot/ankle complex to varying heel heights within 6cm, by moving the origin of roll-over shape distally to maintain roll-over characteristics. However, since the balance of the gait only by the ankle joint could not be achieved beyond the heel height of 6cm, compensations at the knee and the hip joints occurred simultaneously. Roll-over characteristics in human walking would provide simpler and wider understanding of human walking, and furthermore could be applied to the wide understanding of prosthetics and orthotics of the lower extremity as well as orthopaedic shoes.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.177-186
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• 2012

In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.81-90
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• 2016

As an autonomous driving vehicle (AV) need to cope with external road conditions by itself, its perception performance for road environment should be better than that of a human driver. A vision sensor, one of AV sensors, performs lane detection function to percept road environment for performing safe vehicle steering, which relates to define vehicle heading and lane departure prevention. Performance standards for a vision sensor in an ADAS(Advanced Driver Assistance System) focus on the function of 'driver assistance', not on the perception of 'independent situation'. So the performance requirements for a vision sensor in AV may different from those in an ADAS. In assuming that an AV keep previous steering due to lane detection failure, this study calculated lane departure distances between the AV location following curved road alignment and the other one driving to the straight in a curved section. We analysed lane departure distance and time with respect to the allowance of lane detection malfunction of an AV vision sensor. With the results, we found that an AV would encounter a critical lane departure situation if a vision sensor loses lane detection over 1 second. Therefore, it is concluded that the performance standards for an AV should contain more severe lane departure situations than those of an ADAS.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.111-121
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• 2011

Intelligent video analysis systems require techniques which can predict accidents and provide alarms to the monitoring personnel. In this paper, we present an abnormal behavior analysis technique based on adaptive background generation. More specifically, abnormal behaviors include fence climbing, abandoned objects, fainting persons, and loitering persons. The proposed video analysis system consists of (i) background generation and (ii) abnormal behavior analysis modules. For robust background generation, the proposed system updates static regions by detecting motion changes at each frame. In addition, noise and shadow removal steps are also were added to improve the accuracy of the object detection. The abnormal behavior analysis module extracts object information, such as centroid, silhouette, size, and trajectory. As the result of the behavior analysis function objects' behavior is configured and analyzed based on the a priori specified scenarios, such as fence climbing, abandoning objects, fainting, and loitering. In the experimental results, the proposed system was able to detect the moving object and analyze the abnormal behavior in complex environments.