• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.205-209
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• 2004

The K-means algorithm is the most widely used method among the codebook generation algorithms in vector quantization. In this paper, we propose a codebook generation algorithm using a new updating condition to enhance the codebook performance. The conventional K-means algorithm uses a fixed weight of the distance for all training iterations, but the proposed method uses different weights according to the updating condition from the new codevectors for training iterations. Then, different weights can be applied to generate codevectors at each iteration according to this condition, and it can have a similar effect to variable weights. Experimental results show that the proposed algorithm has the better codebook performance than that of K-means algorithm.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.901-904
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• 2003

최근에 웹을 이용한 원격교육에 대한 않은 학습 방법들이 제시되고 있으며, 단순히 학습만을 고려한 방법부터 실시간으로 원격시험을 치르는 방법까지 다양하다 웹 기반의 학습에서 많이 적용되는 방법이 문제를 통한 학습이며, 기존의 학습들은 학습자가 개인 수준에 안는 문제 난이도를 직접 선택하거나, 문제를 동적으로 추출하여 학습하는 방식이다. 그러나 이 방법들은 단순히 출제되는 문제 자체의 차별성을 기반으로 하므로 학습자의 능력별 학습이 이루어지기 어려운 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여, 개인의 문제유형별 약점정보를 기반으로 유형별 추출되는 문제에 난이도를 두어 개인별 학습 능력을 정확히 평가한다. 그리고 다음 단계의 학습에서 개인별 학습정보를 충분히 반영하여 문제들을 출제하므로 학습자의 학습효과를 높일 수 있다. 따라서 이 시스템은 문제유형과 문제난이도에 대한 개인의 정보를 반복적으로 적용하여 학습할 수 있으므로, 더욱 효과적인 원격학습을 제공할 수 있다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.66-74
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• 2014

Recently a variety of study of Korean parsing system is carried out by many software engineers and linguists. The parsing system mainly uses the method of machine learning or symbol processing paradigm. But the parsing system using machine learning has long training time because the data of Korean sentence is very big. And the system shows the limited recognition rate because the data has self error. In this thesis we design system using feature module which can reduce training time and analyze the recognized rate each the number of training sentences and repetition times. The designed system uses the separated modules and sorted table for binary search. We use the refined 36,090 sentences which is extracted by Sejong Corpus. The training time is decreased about three hours and the comparison of recognized rate is the highest as 84.54% when 10,000 sentences is trained 50 times. When all training sentence(32,481) is trained 10 times, the recognition rate is 82.99%. As a result it is more efficient that the system is used the refined data and is repeated the training until it became the steady state.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.689-696
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• 2015

In this study, a learning system based on smart learning is proposed so that students with learning disabilities can learn the effective use of meta-cognitive to solve problems arising during the learning process. The features of the proposed system are as follow. First, it is possible to achieve students' individualized learning by use of smart devices and smart education system. Second, it is possible to provide the constant repetition learning for students. Third, students can improve their achievement using the proposed app. The proposed smart education system using meta-cognition was applied to some learning disabilities students. The following results were obtained. First, the disabled students could have an interest in learning math and improve confidence. Second, the student's mathematical problem-solving skills have improved. Third, students' individualized and self-directed learning was achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.25-32
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• 2012

In this paper, we proposed a development of M-learning smart-trainer content for improving the learning ability of military education. Learners of time and space constraints beyond quickly and accurately can learn with the goal, each subject by partial learning, and repetition, the whole learning quickly and easily by selecting efficiently to help you learn a m-Bizmaker with applications was designed. Experiment targets the military company of two, first aid courses were conducted for the evaluation. Traditional collective comparison group teaching methods, the proposed content, teaching methods applied in the experimental group were selected. The proposed learning applications using smart instructor for verification of learning, with which to compare, test subjects were compared with each of 49 subjects, the results p<.005 level, there was difference among the two groups. Therefore, the proposed application using a smart trainer after class proved that contribute to improving achievement.

• Annual Conference of KIPS
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• 2003.05a
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• pp.239-242
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• 2003

인터넷 원격교육 시스템에서의 많은 개인 학습자의 다양한 학습 요구에 의해 기본 학습자료에 제공에 대한 많은 모형이 대두되고 있으나 기본 학습을 뒷받침해 줄 수 있는 보충학습 자료의 모형은 제시되지 않고 있다. 따라서 본 논문에서는 학습자의 학습 휴리스틱에 의해 기계학습된 보충학습 내용과 위치를 웹과 이메일로 자동 푸쉬해 줄 수 있는 시스템을 제안한다. 휴리스틱에 의해 보충학습 데이터의 트리를 구성한 후 시멘틱 네트를 이용한 속성을 정의하고 기계학습된 학습자의 반복 학습 경로를 분석하여 보충학습을 원활히 진행할 수 있도록 시스템을 설계하는 것이 본 논문의 목적이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.175-179
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• 2003

Convergence condition determines performance of iterative learning control (ILC), for example, convergence speed, remaining error, etc. Hence, the performance can be elevated and a feasible set of learning controllers grows if a less conservative condition is obtained. In the frequency domain, the $H_{\infty}$ norm of the transfer function between consecutive errors has been currently used to test convergence of a learning system. However, even if the convergence condition based on the $H_{\infty}$ norm has a clear property about monotonic convergence, it has a few drawbacks, especially in MIMO plants. In this paper, the relation between the condition and the monotonicity of convergence is clarified and a modified convergence condition is found out using a frequency domain Lyapunov equation, which supersedes the conventional one in the frequency domain.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.127-135
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• 2010

In motion adaptive temporal noise reduction filtering used for reducing video noises, the strength of motion adaptive temporal filtering should be carefully controlled according to temporal movement. This paper presents a motion adaptive temporal filtering scheme based on least-square training. Each pixel is classified to a specific class code according to temporal movement, and then, an iterative least-square training method is applied for each class code to find optimal filtering coefficients. The iterative least-square training is an off-line procedure, and the trained filter coefficients are stored in a lookup table (LUT). In actual noise reduction filtering operation, after each pixel is classified by temporal movement, simple filtering operation is applied with the filter coefficients stored in the LUT according to the class code. Experiment results show that the proposed method efficiently reduces video noises without introducing blurring.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• 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.