• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1991.04a
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• pp.75-79
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• 1991

고도로 산업화가 진행됨에 따라 회전기계는 더욱 중요시되고 있으며 이의 성능 향상에 부단한 노력이 경주되고 있다. 특히 우주 시대의 개막과 더불어 우주선 및 인공위성에 사용하기 위해 초소형이며 초고속의 고성능회전모타 를 개발하기에 이르렀다. 한 예로서 미국립항공우주국(NASA)의 스페이스셔 틀에 사용되는 주엔진 터보펌프를 들 수 있는데 이 터보펌프는 접시만한 크 기로써 71000마력을 생성해 낸다. 이러한 가공할 만한 에너지 밀도와 유량을 감당해 내려면 종래의 회전기계보다는 훨씬 더 높은 회전속도를 가져야 한 다. 이러한 회전체는 큰 관성부하와 진 동 및 동안정성의 문제등을 내포하고 있다. 고성능 회전기계의 또다른 예로서 초정밀 가공용 공작기계를 들 수 있 다. 선반 혹은 밀링머신으로 초정밀가공을 행하기 위해서는 회전축의 진동이 극히 작아야 한다. 이와 같이 오늘날 갈수록 초고성능 초정밀도를 추구함에 있어서 회전축의 진동을 현장에서 모니터링하고 이 진동데이터를 분석하여 회전축을 제어하는 것이 강력히 요구되어진다. 따라서 in-situ 측정이 중요성 을 띠게 되었는데 이는 제어기술의 바탕이 되는 자료를 현장에서 제공할 수 있기 때문이다. 회전축 진동측정의 대상이 되는 것들은 모타, 발전기, 엔진 및 터빈등을 대표적으로 들 수가 있다. 여기서 소형회전기계의 축표면과 같 이 비교적 곡면을 이루고 있는 부분의 진동변위 측정에 신중한 고려가 요구 되어 진다. 이는 축의 곡면도에 따라 감도가 변화하기 때문이다. 따라서 평 판에 대한 calibration 챠트를 회전기계축진동 변위환상에 이용하면 곡률에 따라서 오차가 생기게 된다. 본 연구에서는 비접촉 축진동측정시 발생되는 오차에 대하여 검토하고자 한다. from the studies, the origin of ${\alpha}$$_1$peak was attributed to the detrapping process form trap with 2.88[eV] deep of injected space charge from the chathode in the crystaline regions. The origin of ${\alpha}$$_2$ peak was regarded as the detrapping process of ions trapped with 0.9[eV] deep originated from impurity-ion remained in the specimen during production process of the material, in the crystalline regions. The origin of ${\beta}$ peak was concluded to be due to the depolarization process of "C=0"dipole with the activation energy of 0.75[eV] in the amorphous regions. The origin of ${\gamma}$ peak was responsible to the process combined with the depolarization of "CH$_3$", chain segment, with the activation energy of carriers from the shallow trap with 0.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.13-21
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• 2018

AGVS (Automated Guided Vehicle System) is a core technology of logistics automation which automatically moves specific objects or goods within a certain work space. Conventional AGVS generally requires the in-door localization system and each AGV equips expensive sensors such as laser, magnetic, inertial sensors for the route recognition and automatic navigation. thus the high installation cost is inevitable and there are many restrictions on route(path) modification or expansion. To address this issue, in this paper, we propose a cost-effective and scalable AGV based on a light-weight pattern recognition technique. The proposed pattern recognition technology not only enables autonomous driving by recognizing the route(path), but also provides a technique for figuring out the loc ation of AGV itself by recognizing the simple patterns(bar-code like) installed on the route. This significantly reduces the cost of implementing AGVS as well as benefiting from route modification and expansion. In order to verify the effectiveness of the proposed technique, we first implement a pattern recognition algorithm on a light-weight MCU(Micro Control Unit), and then verify the results by implementing an MCU_controlled AGV prototype.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.84-91
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• 2009

This paper presents an online partial 3D modeling methodology that uses a mobile augmented reality system and aerial photographs, and a tracking methodology that compares the 3D model with a video image. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views. A user's initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user's GPS coordinates, and an inertial sensor which measures pitch. We detect edges of the rooftop based on Graph cut, and find edges and a corner of the bottom by minimizing the proposed cost function. To track the user's position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares estimator and an unscented Kalman filter (UKF). We evaluated the speed and accuracy of both approaches, and we demonstrated the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.