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The dynamic performance design of catenary-pantograph system which collects current for the next generation Korean high speed train(KHST) was considered. Used was the same dynamic model of the catenary-pantograph system as that of TGV-K which will be introduced for Kyung-bu corridor. Using the model, sensitivity analysis for design variables were made to improve dynamic performance of KHST system. The results of sensitivity analysis and performance improvement are as follows: (1) It was found that aerodynamic force, tension of contact wire, mass of contact strip, mass of supporting contact strip, mass of clamp, mass of steady arm, and stiffness of plunger were the design variables most influencing the dynamic performance of the system. (2) Pantograph with reductions of 20% aerodynamic force, 34% weight of supporting contact strip, 20% spring constant of plunger, and 34% equivalent mass of steady arm was very possible system for the KHST which will be running at maximum operating speed 350 ㎞/h.
본 논문에서는 고진공용 터보분자펌프의 비접촉 고속회전을 위한 자기베어링 시스템의 디지털 제어시스템의 설계에 대하여 소개하였으며, 실례로 800 l/s급의 고진공 펌프에 대하여 축 유연모드의 후방향 위험속도를 넘는 최대 40,000 rpm까지의 회전실험 결과를 나타내었다. 제안된 제어시스템은 기본적으로 PID 기반의 직접궤환 제어기와 자이로스코픽 모멘트 효과를 제어하기 위한 교차궤환기, 유연모우드 감쇄를 위한 리드필터와 동기진동 저감을 위한 노치필터 등으로 구성되어 있으며, 이러한 제어기는 자기부상형 터보분자펌프 외에 고속 플라이휠과 같은 자기베어링에 적용될 수 있다. In this paper, a digital controller of magnetic bearing system for a high vacuum turbomolecular pump (TMP) is designed and examined. For stabilizing and providing damping in magnetic bearing, the digital PID controller is applied for each 5 control axes, and the inter-axis cross feedback controller is also applied to suppress low frequency vibration caused by gyroscopic moment of the rotor at high speed of rotation. The fabricated rotor-shaft has its first flexible natural frequency lower than maximum speed, about 614Hz, so the two lead filters are applied to increase damping of flexible mode. Notch filters with rotating frequency were selected to reduce vibration of the pump housing caused by unbalance load. The implemented controllers are verified by examination of frequency response and rotating test up to 40,000 rpm, which is higher than critical speed of backward flexible mode.
The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.