• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.449-453
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• 2014

Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1017-1021
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• 2004

In this paper, a cost-effective integrated 3D system for measuring and sizing foot is proposed. The proposed system employs two CCDs and a laser line projector which are capable of accurately measuring foot. The measurement is based upon the biologically motivated stereo vision principle providing ruggedness against minor system distortions. According to the tolerance, calibration between two different views are implicitly applied. Furthermore, the measurement system employs a measurement base, a frame grabber, a CCD moving cart, a stepping motor and computer. Analysis and design procedure is presented for the calculation of the 3D foot data and the proposed system. Experimental results on the proposed system would verify the concept and system operation.

• Asia pacific journal of information systems
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• 제21권2호
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• 한국산업융합학회 논문집
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• 제23권1호
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• pp.49-55
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• 2020

This paper presents the system modeling, analysis, and controller design and implementation with a inverted pendulum system in order to test Linear Quadratic control based robust algorithm for inverted pendulum robot. The balancing of an inverted pendulum robot by moving pendulum robot like as 'segway' along a horizontal track is a classic problem in the area of control. This paper will describe two methods to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The results of real experiment show that the proposed control system has superior performance for following a reference command at certain initial conditions.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.195-205
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• 2005

In decision tree analysis, C4.5 and CART algorithm have some problems of computational complexity and bias on variable selection. But QUEST algorithm solves these problems by dividing the step of variable selection and split point selection. When input variables are continuous, QUEST algorithm uses ANOVA F-test under the assumption of normality and homogeneity of variances. In this paper, we investigate the influence of violation of normality assumption and effect of the transformation of variables in the QUEST algorithm. In the simulation study, we obtained the empirical powers of variable selection and the empirical bias of variable selection after transformation of variables having various type of underlying distributions.

• 대한안전경영과학회지
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• 제5권3호
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• pp.107-119
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• 2003

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

• 한국재난정보학회 논문집
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• 제9권1호
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• pp.30-49
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• 2013

본 연구에서는 토지이용에 기반을 두는 새로운 교통사고 예측모형을 개발하였다. 다양한 지역의 특성을 반영할 수 있는 변수에 대한 시장분할 및 추가변수 도입을 토대로 Data Mining 기법의 하나인 의사나무결정법(Classification and Regression Tree)을 활용하여 새로운 유형별 교통사고 예측모형을 개발하였다. 분석결과를 살펴보면 주민등록인구수, 통근 등 활동변수와 활동의 대상이 되는 도로규모, 유발시설 등이 교통사고를 설명하는 변수로 도출되었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1996년도 추계학술대회논문집; 한국과학기술회관, 8 Nov. 1996
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• pp.197-202
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• 1996

The dynamic interaction between a translating flexible arm and a trapezoidal velocity profile of a cart to which the flexible arm is attached is presented. Vibration of the flexible arm due to translation is analytically solved, and the conditions for suppressing vibration is derived in terms of velocity profiles. To prove the validity of the solution and the conditions, numerical computation and experiments are camed out. Only a natural frequency of vibrating plant is needed to obtain the conditions for vibration reduction. With this results, a passive vibration regulator as an open loop control scheme can be designed and direct application to industrial plants such as overhead crane can be made.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.545-547
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• 2019

Policy Gradient 방식의 학습은 최근 강화학습 분야에서 많이 연구되고 있는 주제로, 본 논문에서는 강화학습을 적용시킬 수 있는 OpenAi Gym 의 'CartPole-v0' 와 'Pendulum-v0' 환경에서 Policy Gradient 방식의 Asynchronous Advantage Actor-Critic (A3C) 알고리즘과 Proximal Policy Optimization (PPO) 알고리즘의 학습 성능을 비교 분석한 결과를 제시한다. 딥러닝 모델 등 두 알고리즘이 동일하게 지닐 수 있는 조건들은 가능한 동일하게 맞추면서 Episode 진행에 따른 Score 변화 과정을 실험하였다. 본 실험을 통해서 두 가지 서로 다른 환경에서 PPO 가 A3C 보다 더 나은 성능을 보임을 확인하였다.