• Industry Promotion Research
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• v.1 no.1
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• pp.51-57
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• 2016

This study verified the effect of the level of learning organization on satisfaction of learning organization support project and the moderating effect of charismatic leadership. The level of learning organization positively affected satisfaction of learning organization support project. Especially, personal mastery affected creating&spreading knowledge and financial performance, system thingking affected building learning infra, shared vision affected learning culture&activity, teamwork and financial performance. Also, charismatic leadership partially moderated the effect of learning organization. This study provided implications to manage successful learning organization by analyzing causal relationship between learning theory and practical corporate performance.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.251-267
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• 2002

Self Organizing Maps(SOM) by Kohonen is very fast algorithm in neural networks. But it doesn't show sure rules of training results. In this paper, we introduce to Bayesian Learning for Self Organizing Maps(BLSOM) which combines self organizing maps with Bayesian learning. So it supports explanatory power of models and improves prediction. BLSOM has global optima anywhere but SOM has not. This is proved by experiment in this paper.

• Industry Promotion Research
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• v.1 no.1
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• pp.65-70
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• 2016

This study verified the effect of charismatic leadership on motivation to learn and the moderating effect of level of building learning organization in learning organization support project. Survey was conducted for manufacture company participating in learning organization support project. Charismatic Leadership positively affected motivation to learn, specifically vision-related behaviors positively affected task value. At the same time, the moderating effect of level of building learning organization was verified and specifically personal mastery and team learning moderated the effect of charismatic leadership on motivation to learn. This study results provided academic and practical implications for operating successful learning organization of company.

• Communications of Mathematical Education
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• v.15
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• pp.161-166
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• 2003

본 연구는 학습 구조차트 구성을 통하여 고등학교 수학의 학습내용을 구조적 ${\cdot}$ 체계적으로 조직화시켜 학생들로 하여금 학습 내용의 효과적인 이해와 상호 관련성을 촉진시키고 학습 내용의 조직화 및 구조화 활동이 고등학생들의 학업에 미치는 영향을 조사하는데 그 목적이 있다. 본 연구에 따르면 수학 학업성취도가 상인 학생은 문제풀이시 머릿속에서 차트를 그리게 되고 여러 가지 개념을 나열하여 조작할 수 있는 능력이 생겼으며 문제 유형에 맞춘 학습 보다는 어떤 개념들이 문제풀이에 사용되었으며 이러한 개념들이 어떻게 나열되는지에 대한 학습으로 관심이 전환되었다. 수학학업 성취도가 하인 학생들은 학습 구조차트의 구성에만 만족하는 편이며 선행지식의 부족으로 복합적인 개념의 문제풀이에 있어서는 여전히 어려움을 경험하고 있었다. 성적이 낮은 학생일수록 개념에 대한 구조화와 조직화에 대한 어려움이 많은 것으로 보여 이들 학생들에 대한 장기적인 연구가 필요하다고 본다.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.416-423
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• 2000

The existing self-organizing feature map of Kohonen has weakpoint that need too much input patterns in order to converse into the learning rate and equilibrium state when it trains. Making up for the current weak point, B.Bavarian suggested the method of that distributed the learning rate such as Gaussian function. However, this method has also a disadvantage which can not achieve the right self-organizing. In this paper, we proposed the method of improving the convergence speed and the convergence rate of self-organizing feature map converting the Gaussian function into dynamic approximate curve used in when trains the self-organizing feature map.

• Journal of The Korean Association For Science Education
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• v.20 no.4
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• pp.519-526
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• 2000

In this study, the effects of visual organization and cooperative learning in problem-solving strategy were investigated. Three classes (N=127) at a high school were assigned to SV (Strategy-Visual organization) group, SVC (Strategy-Visual organization-Cooperative learning) group, and control group. After instructions, students' multiple-choice problem-solving ability, strategy performing ability, anxiety about chemistry learning, perception of involvement, and motivation to learning science were examined. Although multiple-choice problem-solving ability was not different significantly, there was a significant main effect in strategy performing ability. The scores of the SV and SVC groups were significantly higher than those of the control group, especially in the subcategories of problem understanding and recalling related principles. In the tests of perception of involvement and motivation to learning science, the scores of the SV and SVC groups were also significantly higher than those of the control group. However, problem-solving strategy using visual organization could not alleviate anxiety about chemistry learning.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.47-48
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• 2019

본 논문에서는 신경망의 일종인 자기조직화지도(Self Organizing Map)을 이용하여 컨트롤러의 순서를 정하는 모델을 제안하였다. 자기조직화지도는 자율 학습에 의한 클러스터링을 수행하는 알고리즘으로써 컨트롤러에 가중치를 부여하고 컨트롤러 간 거리를 계산하여 효율적인 컨트롤러 선택을 목표로 한다.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.204-210
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• 2002

The self-organizing feature map of Kohonen has disadvantage that needs too much input patterns in order to converge into the equilibrium state when it trains. In this paper we proposed the method of improving the convergence speed and rate of self-organizing feature map converting the interaction set into Dynamic Gaussian function. The proposed method Provides us with dynamic Properties that the deviation and width of Gaussian function used as an interaction function are narrowed in proportion to learning times and learning rates that varies according to topological position from the winner neuron. In this Paper. we proposed the method of improving the convergence rate and the degree of self-organizing feature map.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.73-77
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• 2001

본 논문에서는 부분적으로 관측 가능한 환경에서 사전의 모델 정보 없이 확률적인 행동 정책을 학습하는 상태 조직화 강화 학습 모델을 제안한다. 기존의 강화학습은 환경 모델을 사전에 필요로 하고 상태 전체의 관측이 필요하기 때문에 학습 이전에 문제에 대해 알아야 한다는 제약이 있다. 또한 작은 문제에 대해서는 잘 적용되지만 상태의 수가 매우 많고 부분적으로만 관측한 경우가 많은 실제 문제에는 그대로 적용하기가 불가능하다. 이러한 두 가지 단점을 해결하기 위해 본 논문에서는 사전의 모델 정보 없이 부분적인 관측값으로부터 상태와 행동 정책을 동시에 학습해 나가는 강화 학습 모델을 제안하고, 제안된 방법을 부분적으로만 관측이 가능한 미로 탐색 문제에 적용하였다.