• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.354-359
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• 2003

In this paper, we consider the problem of designing a simple learning variable structure system for repeatable tracking control of robot manipulators. We combine a variable structure control law as the robust part for stabilization and a feedforward learning law as the intelligent part for nonlinearity compensation. We show that the tracking error asymptotically converges to zero. Finally, we give computer simulation results in order to show the effectiveness of our method.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.127-143
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• 2006

While e-learners' satisfaction would be determined by qualify of e-learning environment including learning management systems, learning contents and interactions, the influence of quality on satisfaction can be changed by e-learners' self-regulated efficacy The objective of this study is to show difference of the relationship between qualify and satisfaction In e-learning by self-regulated efficacy. For this purpose, we propose a research model which consists of five quality factors in e-learning as explaining variables, satisfaction as a result variable and self-regulated efficacy as a control variable. For empirical test of this model, the sample is collected from e-learning classes in a college and divided into two groups by self-regulated efficacy in order to analyze the effects of control variable. By multi-group analysis, we show two groups are different from each other in the relationship between quality and satisfaction of e-learning environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• Journal of Gifted/Talented Education
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• v.6 no.1
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• pp.93-109
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• 1996

The purpose of this study was to investigate the relationship between learning strategies and cognitive learning abilities with achievement scores of elementary school children. To achieve this purpose, 109 sixth grade children were sampled in Seoul-City, and the 'Questionnaire on the Learning Strategies and Learning Abilities Test' were administered to them. The collected data were analyzed by Pearson's Product Moment Correlation and Multiple Regression Analysis. The major findings of this study were as follows: Firstly, there appeared to be statistically significant correlations between learning strategies and achievement scores. The process of thinking variable of learning strategies were most significantly correlated with achievement scores(r=.251- .458, p<.01). The calculated R2 indicated that the combined effects of process of thinhng and affective domain on the achievement scores were about 21.5%. Secondly, there appeared to be statistically significant correlations between cognitive learning abilities and achievement scores. The verbal reasoning and verbal comprehension variable of cognitive learning abilities were most significantly correlated with achievement scores(r=.215-,493, p<.01). The calculated R2 indicated that the verbal reasoning and verbal comprehension variable of cognitive learning abilities explained about 27.6% of the variance of achievement scores. Thirdly, there appeared to be no statistically significant correlations between learning strategies and cognitive learning abilities. The results of this study shows that the development of learning strategies and cognitive learning abilities could improve the achievement scores in school learning.

• School Mathematics
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• v.1 no.1
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• pp.19-37
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• 1999

In this study, we examined some matters related to the learning of variable concept in school mathematics on the basis of the theoretical foundation from the previous studies(e.g. Davis, 1975; Rosnick, 1981; IK chemann, 1981; Wagner, 1983.) and practices on variable concept teaching by evaluating the current state of that. Matters be discussed are as follows; the use of symbol for place holders in elementary mathematics, the dealing with sets those elements are literals and operations of such sets, the teaching of dummy variable, the construction of literal expressions that contains variables, the labeling indeterminates as a constant, the change in the exact meaning of variable according to the function concept, the teaching of a generalization by means of variables. After considering on these matters that are connected with the teaching-learning of variable concept, we suggested the alternative proposal to the current state of variable concept teaching.

• Journal of the Korean School Mathematics Society
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• v.14 no.2
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• pp.199-213
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• 2011

The factors contributing to learning can be broadly classified into four different groups; Learner's characteristic variable, Instructor's characteristic variable, Learning task characteristic variable and environmental characteristic variable. And the first thing we need to do here is understanding of learner's characteristics among those factors in order to devise a plan for education. Accordingly, the purpose of this study is to find out what impact the affective traits (self-concept learning habits learning attitude), one of the learner's features, have on the mathematics-learning achievement and to seek for a good teaching method with reference to elementary school students' learning accomplishments and attitudes. For this, a questionnaire survey was conducted of 78 students of two fifth-grade classes in an elementary school located in South Chungcheong Province in this study. In consequence, it has been shown that the mathematics-learning achievement has the greatest relevance to the self-concept in connection with mathematics followed by the self-concept in connection with learning, the learning habits relating to mathematics, the attitude towards mathematics, the learning habits concerning studies and the attitude towards learning.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.161-169
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• 1997

A variety of learning methods are used for neural networks. Among them, the backpropagation algorithm is most widely used in such image processing, speech recognition, and pattern recognition. Despite its popularity for these application, its main problem is associated with the running time, namely, too much time is spent for the learning. This paper suggests a method which maximize the convergence speed of the learning. Such reduction in e learning time of the backpropagation algorithm is possible through an adaptive adjusting of the slope of the activation function depending on total errors, which is named as the variable slope algorithm. Moreover experimental results using this variable slope algorithm is compared against conventional backpropagation algorithm and other variations; which shows an improvement in the performance over pervious algorithms.

• Land and Housing Review
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• v.8 no.3
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• pp.123-130
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• 2017

This study aims to empirically verify the impacts of career learning on employability and the mediating effect of job expertise in a public corporation. For the empirical analysis, I surveyed 958 employees(valid respondents) working in a public corporation. And the structural equation modeling(SEM) was used to statistically analize and test the research hypotheses. The independent variable is 'career learning', the dependent variable is 'employability' and the mediating variable is 'job expertise'. The results are as follows. The empirical analysis shows that the positive effects of 'career learning ${\rightarrow}$ job expertise', 'job expertise ${\rightarrow}$ employability' and 'career learning ${\rightarrow}$ employability' are all verified. And the mediating effect of job expertise between career learning and employability is also partially verified. So, all the proposed hypotheses are accepted. From this result, I can clearly suggest that the employees can be growing to professionals with high employability when they retire if they are voluntarily and self-motivated to set up their career plan and to enhance their job expertise. In this context, it is expected that the company should support the employees to continue to strengthen their own expertise in their job place through their mid-long term career learning plan.

• 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.