• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.321-324
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• 2009

비교적 적은 크기이면서 예측력에 있어 만족할 만한 의사결정목을 생성하는 방법으로서 적절한 크기의 샘플링을 제안하였다. 일반적으로 샘플의 크기가 작을수록 작은 의사결정목이 생성되므로 적절한 예측 정확도를 갖는 작은 트리를 생성하기를 원할 경우 적당한 크기의 샘플링을 하는 것이 트리의 최적화를 위한 계산을 더 시행하는 것보다 바람직하다고 할 수 있으며, 이와 같은 사실은 현재 알려진 가장 대표적 의사결정목 생성 알고리즘인 C4.5 및 CART를 사용하여 실험으로서 보여주었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.203-214
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• 1999

본 연구는 A카드 회사에서 현재 실시하고 텔레마케팅 시스템에 데이터마이닝 기법 가운데 하나인 CHAID, CART 알고리즘 및 신경망 기법을 적용하여 모델을 개발하고 개발괸 모델들의 성과를 분석한다. 이를 통하여 어떻게 기업이 데이터베이스와 데이터마이닝 기법을 마케팅에 효과적으로 사용할 수 있는가에 대한 방안을 제시하고 여러 모델들의 성과를 비교 분석하는 방안을 함께 제시한다.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.283-288
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• 2004

지금까지 의사결정나무에서 분리 변수의 선택에 관한 연구는 많았으나, 대부분 연속형 변수와 명목형 변수에 국한되어 왔다. 본 연구에서는 순서형 변수에 주목하여 CART, QUEST, CRUISE 등 기존 알고리즘과 본 연구에서 제안하는 비모수적 접근 방법인 K-S test, framer-von Misos test 방법의 변수 선택력을 비교하였다. 그 결과 본 연구에서 제안하는 framer-von Mises test 방법이 다른 알고리즘에 비하여, 변수 선택력과 안정성에 있어서 좋은 성과를 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1710-1715
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• 2005

Two fuzzy controllers are coordinated to control a cartpole such that the pole is balanced as well as the cart is brought back to the track origin. The coordination is due to the BOXES scheme that is established through the evaluation of the outcomes of the control action by one of the fuzzy controllers. It is found that the control scheme is good at selecting proper fuzzy controller so that the pole is balanced fast while the cart moves back to the track origin steadily.

• 한국IT서비스학회:학술대회논문집
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• 2002.11a
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• pp.225-231
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• 2002

B2B EC는 그 시장규모와 기업, 사회전반에 미치는 영향이 커다란 주요한 연구 분야이다. 그런데, B2C EC와 달리 B2B EC에서는, 구매자의 전자시장에서의 구매기록은 ERP로 대표되는 기업내의 전자구매시스템과 통합되어야 한다. 이를 위한 구조로서 기존의 Inside-Out 접근방식과 Outside-In 접근방식, 그리고, 최근의 b-Cart 접근방식이 있다. 본 논문에서는 이들 3가지 방식에 대한 고찰과 비교 분석을 시도한다. 이를 통해서 기업간 전자상거래에 있어서 ERP와 전자시장과의 통합구조로 b-Cart 접근방식이 효율적인 프레임웍임을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.124-124
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• 2000

In this paper, The PID controller for stabilization of an inverted pendulum system is proposed. The PR control rule is very common in control systems. It is the basic tool for solving most process control problem. We consider the inverted pendulum system containing two PID controllers. The first controls the angle of the pendulum. The second is used to control the position of the cart. We can show stabilization of the PID controller through simulation of the inverted pendulum system.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.555-563
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• 2004

In classification and discrimination, we often face with incomplete group variable arising typically from many missing values and/or incredible cases. This paper suggests the use of K-means clustering for pre-adjusting incompleteness and in turn classification based on generalized statistical distance is performed. For illustrating the proposed procedure, simulation study is conducted comparatively with CART in data mining and traditional techniques which are ignoring incompleteness of group variable. Simulation study manifests that our methodology out-performs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.154-160
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• 2009

A control system composed of two fuzzy controllers is proposed to balance the pole as well as to move the cart to the center of the track of the cartpole system. The two fuzzy controllers are designed with 2 input variables respectively and their control characters are studied in order to devise a control scheme that alternates the two fuzzy controllers. It is found that the control system using the scheme works well even though there is some residual oscillations of the pole and the cart.

• 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.