• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.83-88
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• 2006

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped robot. The learning control scheme consists of a feedforward learning nile and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robotic motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.2
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• pp.197-205
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• 2012

Purpose: This study aimed to demonstrate correlations among self-regulated learning, attention control and Yangseng, to clarify any differences depending on general characteristics and ultimately to understand factors affecting self-regulated learning of undergraduates. Methods: Data were collected for a month from April 1st, 2011. A total of 438 undergraduate nursing students of two universities in Chungbuk and Chungnam were surveyed by using a questionnaire about self-regulated learning, attention control and Yangseng. Results: Self-regulated learning of the subjects showed statistically significant correlations with their attention control (r=.302, p=.001) and Yangseng (r=.292, p=.001). In addition, self-regulated learning could be explained by attention control (${\beta}$=3.648, p=.001), Yangseng (${\beta}$=3.645, p=.001), perceived academic achievement levels (${\beta}$=.124, p=.018), or eating breakfast (${\beta}$=.102, p=.027). In the model, the variables explained self-regulated learning by 19.0%. Conclusion: Nursing instructors should encourage undergraduate nursing students to enhance their attention control so that they can improve their self-regulated learning abilities, which will eventually develop their problem solving skills. In addition, it was shown that self-regulated learning correlates with yangseng including eating a regular breakfast. Maintaining a desirable lifestyle is also essential for students to succeed in self-regulated learning.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.648-651
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• 1997

This paper proposes methods for the cooperative control of multiple mobile robots and constructs a robotic soccer system in which the cooperation will be implemented as a pass play of two robots. To play a soccer game, elementary actions such as shooting and moving have been designed, and Q-learning, which is one of the popular methods for reinforcement learning, is used to determine what actions to take. Through simulation, learning is successful in case of deliberate initial arrangements of ball and robots, thereby cooperative work can be accomplished.

• Journal of Korean Public Health Nursing
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• v.26 no.3
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• pp.465-477
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• 2012

Purpose: The purpose of this study was to investigate the influence of breakfast characteristics of university students on their self-regulated learning, attentional control, and fatigue in order to provide-basic data for establishing desirable eating habits, self-regulated learning skills using attentional control, and advisable learning habits of university students. Method: The level of fatigue was estimated using the Visual analogue scale (VAS) and Critical flicker frequency (CFF). Attentional control was measured using the Attentional Control Questionnaire (ACQ) adapted by Yoon. Self-regulated learning was surveyed by the Self-Regulated Learning Test developed by Chung. Data from atotal of 142 university students were collected from November 30 to December 9, 2011. Result: 69% of the subjects skipped their breakfast. Attentional control has a negative correlation with fatigue (r=-.179, p=.033) and a positive correlation with self-regulated learning (r=.352, p<.001). The multiple regression model of self-regulated learning consists of attentional control (t=3.218, p=.002), commuting time (t=-3.076, p=.003), understanding the importance of breakfast (t=-2.413, p=.008), and skipping breakfast(t=-2.195, p=.030) and its R-square was 21.8%. Conclusion: Learning efficiency of university students should be improved by means of attentional control, which is related to self-regulated learning. Also, it is essential for university students to establish healthy lifestyles including regular eating habits and attentional control, in order to improve their self-regulated learning.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.450-453
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• 1997

An 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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• 1995.10a
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• pp.518-521
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• 1995

In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.

• Journal of the Korean Society of School Health
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• v.35 no.3
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• pp.113-122
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• 2022

Purpose: The purpose of this study was to identify the effects of applying jigsaw cooperative learning to basic nursing practicums on self-directed learning ability, self-efficacy, and learning outcomes. Methods: This study was based on a non-equivalent control group design, and the subjects were nursing students. The study allocated 30 people in the experimental group and 30 people in the control group, and jigsaw cooperative learning was applied to the experimental group for 2 hours every week for a total of 8 weeks. The traditional educational method was applied to the control group. The collected data were analyzed using SPSS v26.0. Results: The experimental group to which jigsaw cooperative learning was applied showed statistically significant differences in self-directed learning ability (F=4.49, p=.038), self-efficacy (F=6.15, p=.016), and learning outcomes (F=19.48, p<.001) compared to the control group. Conclusion: By applying jigsaw cooperative learning to basic nursing practicums, this study confirmed its effect not only on the effective domain such as self-directed learning ability and self-efficacy, but also on learning outcomes in the practical domain. We propose future studies apply jigsaw cooperative learning to various practice classes to achieve learning outcomes that focus on cultivating students' practical capabilities.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.185-190
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• 2009

A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control expert system based on flexible iterative learning control (F-ILC). Finally, a feedback compensation unit is incorporated against irregular heavy disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1069-1074
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• 1989

A discrete-time learning control for robotic manipulators is studied using its pulse transfer function. Firstly, discrete-time learning stability condition which is applicable to single-input two-outputs systems is derived. Secondly, stability of learning algorithm with position signal is studied. In this case, when sampling period is small, the algorithm is not stable because of an unstable zero of the system. Thirdly, stability of algorithm with position and velocity signals is studied. In this case, we can stabilize the learning control system which is unstable in learning with only position signal. Finally, simulation results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. This simulation results agree well with the analytical ones.