• 한국정밀공학회지
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• 제31권5호
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• pp.423-430
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• 2014

In this study, the five-DOF motion at ultra-precision linear stage under static and dynamic conditions are evaluated through the extending application of ISO 230-2. As the performance factors, the bi-directional accuracy and repeatability of the five-DOF motion are quantitatively evaluated with the measurement uncertainties which are determined using the standard uncertainty of equipment used in experiment. The motion under static condition are analyzed using geometric errors. The five geometric errors except the linear displacement error are measured using optimal measurement system which is designed to enhance the standard uncertainty of geometric errors. In addition, the motion under dynamic conditions are analyzed with respect to the conditions with different feed rate of the stage. The experimental results shows that the feed rate of stage has a significant effect on straightness motions.

• 한국수학교육학회지시리즈A:수학교육
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• 제48권3호
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• pp.287-302
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• 2009

This study investigated how 5th grade students found and understood triangle congruence conditions (SSS, SAS, ASA). In particular, this study focused on children's processes of discovering triangle congruence conditions and the obstacles which they encountered in the process of making sense of these conditions. Our data indicates that inquiring the cases in which less than three factors of triangle are given is helpful for children to guess triangle congruence conditions and understand the minimal characteristic of these conditions. And the degree of difficulty of discovering each congruence condition is different. Children discovered SAS condition and ASA condition easily, but it was hard for them to discover and understand that SSS was also a triangle congruence condition because they connected the length of a given side with the use of a scaled ruler not a compass.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.565-572
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• 2001

This article explores the problem of ergodicity for the nonlinear autoregressive processes with ARCH structure in a very general setting. A sufficient condition for the geometric ergodicity of the model is developed along the lines of Feigin and Tweedie(1985), thereby extending classical results for specific nonlinear time series. The condition suggested is in turn applied to some specific nonlinear time series illustrating that our results extend those in the literature.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.317.2-317
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• 2002

Transmission loss of KHST passenger vehicle was calculated using measured acoustic data: In order to verify the transmission loss results for KHST case, similar experiment was carried out in laboratory condition, which result was compared those by geometric acoustic method. The computational results shows good agreement with the transmission loss magnitude from experiments. This paper also mentions items to obtain more accurate transmission loss values, i. e. how to assure reverberant field condition, the selection of source speaker' location.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.888-891
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• 2006

SAR image is not dependent on the weather condition and Sun's electromagnetic energy. But geometric distortions exist in almost all radar image, it need to be correction. The Radarsat-1 SAR images are used to monitoring of moisture acquired in May 1/1998 and May 25/1998. Radarsat-1 C band data is sensitive on moisture condition. Study area is located in Non-san site. It is made up almost agricultural area and a little of forest area. In May, Rice-planting is started in the midland of Korea. So moisture condition is undergoing many changes. Forest area need to be terrain effect removal for accurately results because it is included in layover, shadow, and so on. Results of land-cover moisture condition map are useful tool for fields of agriculture, forestry industry, and disaster.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.346-352
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• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 춘계 학술발표회논문집
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• pp.85-92
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• 2003

Hysteretic behaviors of a seismic isolator are identified by using the regularized output error estimator (OEE) based on the secant stiffness model. A proper regularity condition of tangent stiffness for the current OEE is proposed considering the regularity condition of Duhem hysteretic operator. The proposed regularity condition is defined by 12-norm of the tangent stiffness with respect to time. The secant stiffness model for the OEE is obtained by approximating the tangent stiffness under the proposed regularity condition by the secant stiffness at each time step. A least square method is employed to minimize the difference between the calculated response and measured response for the OEE. The regularity condition of the secant stiffness is utilized to alleviate ill-posedness of the OEE and to yield numerically stable solutions through the regularization technique. An optimal regularization factor determined by geometric mean scheme (GMS) is used to yield appropriate regularization effects on the OEE.