• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.253-261
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• 2022

본 연구에서는 컴퓨터 비전공자 대상의 인공지능 교양 교육을 위한 프로젝트 학습법을 적용한 후 교육적 효과를 파악하고자 한다. 실험집단과 통제집단 각각의 학습몰입, 인공지능 효능감의 향상 정도를 파악하기 위하여 각 집단 내에서 대응표본 t-검정을 실시하였고, 수업 후 실험집단과 통제집단의 학습몰입과 인공지능 효능감에 대한 사전검사와 사후검사의 통계적 효과를 알아보기 위해 독립표본 t-검정을 실시하였다. 그 결과 실험집단과 통제집단은 각각 수업 전과 후 학습몰입과 인공지능 효능감에서 유의미한 향상을 보였다. 인공지능 수업에서 프로젝트 학습방법을 적용한 실험군과 이론과 실습만 진행한 통제집단 간의 학습몰입은 통계적으로 유의한 차이가 없었지만, 프로젝트 학습방법을 적용한 실험집단은 이론과 실습만 진행한 통제집단에 비해 인공지능의 효능감이 유의미한 수준으로 향상되었음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.697-702
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• 1994

This paper illustrates a new learning control for robot manipulators using Lyapunov direct method. It has been shown that under the proposed learning control robot manipulators are always guaranteed to be asymptotically stable with respect to the number of trials. The proposed control is also robust in the sense that the exact knowledge of the nonlinear dynamics is not required except for bounding functions on the magnitude.

• 동의신경정신과학회지
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• 제10권1호
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• pp.1-15
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• 1999

The effects of Sahyangsohapwon on the enhancement of learning and memory of AD model rats were studied with Morris water maze and radial arm maze. Sample group was electrolytically lesioned on nbM, and then daily treated with the medicine for two months. Control group with nbM lesion, and sham group with the sham operation were treated the vehicle for same duration. The following results were observed. In the learning trials of Morris water maze, all three groups were improved in learning capacity as trials were repeated, but the sham group showed more prominent improvement in learning compared with the control group(p<0.01). 2. In memory retention test of Morris water maze, the sham group marked more significant improvement statistically in memory retention compared with the control group(p<0.05). 3. In the learning of radial arm maze, the sham group shows better learning capacity significantly compared with the control group(p<0.05). With the experimental results above, Sahyangsohapwon can be supposed to have the improving effects on the learning and memory of AD rats induced by electronical injury of nbM.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.178-186
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• 1998

In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.734-739
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• 1988

For the trajectory control of dynamic systems with unidentified parameters a second-order iterative learning control method is presented. In contrast to other known methods, the proposed learning control scheme can utilize more than one error history contained in the trajectories generated at prior iterations. A convergency proof is given and it is also shown that the convergence speed can be improved in compared to conventional methods. Examples are provided to show effectiveness of the algorithm, and, via simulation, it is demonstrated that the method yields a good performance even in the presence of distubances.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 춘계전력전자학술대회 논문집(2)
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• pp.584-587
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• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.339-344
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• 1993

A study for a lathe to machine workpieces with noncircular corss-sections is presented. The noncircular cutting is accomplished by controlling the radial tool position synchronized with the revolution angle of spindle. A learning control algorithm is suggested for the toll positioning, of which the control performances are analyzed and simulated on a numerical computer that the effectiveness of the control is convinced. The learning control is tested on a NC-lathe which shows successful results.

• 한국정밀공학회지
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• 제16권7호
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• pp.144-150
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• 1999

n active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.