• The Journal of Korean Association of Computer Education
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• v.4 no.1
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• pp.65-76
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• 2001

The purposes of this study are to test the learning effects of CAI in relation to the learner's locus of control and to test the interactive effects between learner's locus of control and types of learning control. In order to achieve the above purpose, a test was administered to investigate learner's locus of control to 160 First grades of J middle school located in Yosu. On the basis of the test results, the subjects who belonged to the extreme of both pole were divided into the internal and external control groups. Both groups were randomly assigned to teacher-control and learner-control types in CAI. After class, post-test on learning achievement was administered and ANOVA was employed to analyze the data. The research findings are as follows. First, as a result of learning through CAI, the learning scores of internals were shown higher than that of the external group. Second, learner-control group showed higher learning scores than teacher-control group. Third, there was not found a significant interactive effect between learner's locus of control and types of learning control of CAI.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.2
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• pp.146-165
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• 2012

The purpose of this study is development of learning coaching program that is grafted onto advantage of Self-directed learning and coaching intended for Youth-After-School-Academy children and analysis the effect on self-efficacy and Self-directed learning ability from this program. The program of this study is developed on the base of Seels & Richey's 'ADDIE Model'. In order to verify the effect of this study, two times tests were carried out on 14 persons of the experimental group and the control group respectively, before and after the program was performed. The MANCOVA & ANCOVA was done on the difference between the post-test results of the experimental group and the control group. Findings of this study might be summarized as follows: First, the post-test result in the experimental group on self-efficacy was meaningfully higher than in the control group. Second, on Self-directed learning ability the result in the experimental group was also higher than in the control group. Therefore, learning coaching program impacted on self-efficacy and Self-directed learning ability of Youth-After-School-Academy children. This program that aim to discover the potential on learning, expect to be effective for children education of today when pursue Self-directed learning ability and creativity.

• Journal of the Korean Home Economics Association
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• v.51 no.2
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• pp.253-261
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• 2013

The purpose of this study was to investigate the moderating effects of parental monitoring on the relationship between children's dependency on mobile phones and control of learning behavior. The data came from the 2010 Korean Children and Youth Panel (N = 1,609) conducted by the National Youth Policy Institute. The analysis method used was Structural Equation Modeling by using SPSS 17.0 and AMOS 7.0. To test the significant moderating effects, Ping's two-step technique, which is free from the requirement of nonlinear constraints, was used. Our results demonstrated that children's dependency on mobile phones had negative effects on control of learning behavior, and the interaction effects between such dependency and parental monitoring affected the control of learning behavior. Thus, these results proved the moderating effects of parental monitoring in the control of learning behavior. This study suggests that parental monitoring buffers against having difficulties to control and adjust one's behavior associated with control of learning behavior, which is affected by the dependency on mobile phones among children. We discussed that the risks of children's dependency on mobile phones and parental monitoring should be acknowledge as a significant protective factor.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1573-1576
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• 1997

In this paper we present an associative menory network(AMN) controller for learning of robot trajectories. We use AMN controller in order to improve the performance of conventional learning control, e.g. RCL, which had studied by Sadegh et al. Computer simulations show the feasibility and effectiveness of the proposed AMN controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.760-765
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• 1993

A multivariable learning control is designed in frequency domain. A general to of feedback assisted learning scheme is considered and an inverse model based learning algorithm is derived through convergence analysis in frequency domain. Performance of the proposed control method is evaluated through numerical simulation.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.175-191
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• 2007

This study selected 113 gifted underachievers and 128 gifted achievers from 17 elementary schools to examine gifted children's personal characteristics(self-concept, locus of control, and learning habits) that have an effect on underachievement. Self-concept(general self-concept and academic self-concept), locus of control, and learning habits(endurance, learning strategy, and learning motivation) variables were analyzed to determine gifted underachievers' personal characteristics. (1) Comparison of personal characteristics of gifted achievers with gifted underachievers indicated gifted underachievers had low self-concept, external locus to control, and problems in learning habits. (2) The sub factors of habits of learning motivation and learning strategy had the greatest effect on underachievement of gifted children.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.869-879
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• 1995

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. In the previous paper, I had studied the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short. In this paper, we present two examples. The first illustrates the effect of coupling between subsystems in the system dynamics, and the second studies the application of decentralized learning control to robot problems. The latter example illustrates the application of decentralized learning control to nonlinear systems, and also studies the effect of the coupling between subsystems introduced in the input matrix by the discretization of the system equations. The conclusion is that for sufficiently small learning gain, and sufficiently small sample time, the simple learning control law based on integral control applied to each robot axis will produce zero tracking error in spite o the dynamic coupling in the robot equations. Of course, the results of this paper have much more general application than just to the robotics tracking problem. Convergence in decentralized systems is seen to depend only on the input and output matrices, provided the sample time is suffiently small.