• IE interfaces
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• v.5 no.2
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• pp.53-74
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• 1992

In order to design an FMS(Flexible Manufacturing System), an effective design tool is necessary for selecting and anlayzing the complex interacting factors of a manufacturing system. Considering most of the previous researches have been focused on evaluating the designed FMS, development of an integrated FMS designing system is needed for performing the design task such as machine selection and layout determination as well as the evaluation task of the designed FMS. In this paper, we developed a prototype of such a system called the intelligent FMS designer based on the techniques of Expert System and Simulation. By using this designing system, possible design alternatives can be analyzed and appropriate manufacturing capacity can be estimated at the designing stage. Various operating characteristics of the designed FMS can be also predicted before it is implemented. Eventually a suitable design alternative can be provided with cost details within relatively short time period. To demonstrate the performance and effectiveness of the designer, a simple example is tested. The results show a great potential for automating design processes of an FMS.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1966-1971
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• 2005

Though several previous anti-windup techniques have been proposed, they are limited to linear systems or friction is not considered. Thus this paper proposes a compensation scheme for input-constrained robot systems with friction to cope with the windup phenomenon and shows its effectiveness by simulations. Given a feedback linearizing controller for a robot system designed without considering its input constraint, an additional dynamic compensator is proposed to account for the constraint. The dynamic anti-windup is based on the minimization of a reasonable performance index, and properties of the resulting closed-loop are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.3-6
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• 2006

Methods for risk engineering is a bundle of engineering tools including fundamental concepts and approaches of soft computing with application to real issues of risk management. In this talk fundamental concepts and soft computing approaches of risk engineering will be introduced. As the term of risk implies both advantageous and hazardous uncertainty in its origins, a fundamental theory to describe uncertainties is introduced that includes traditional probability and statistical models, fuzzy systems, as well as less popular modal logic. In particular, modal logic capabilities to express various kinds of uncertainties are emphasized and relations with rough sets and evidence theory are described. Another topic is data mining related to problems in risk management. Some risk mining techniques including fuzzy clustering are introduced and a recently developed algorithm is overviewed. A numerical example is shown.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.115-118
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• 2004

In this paper, a systematic design approach based on parallel distributed compensation techniques is proposed for anticontrol of chaos in a general continuous-time Takagi-Sugeno (TS) fuzzy system. The verification of chaos in the controlled continuous-time TS fuzzy system is done by the following procedure. First, we establish an asymptotically approximate relationship between a continuous-time TS fuzzy system with time-delay and a discrete-time TS fuzzy system. Then Marotto theorem is applied. The boundedness in the controlled continuous-time TS fuzzy system is also proven via its associated discrete-time TS fuzzy system.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.423-431
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• 2000

In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.269-272
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• 2006

병원의 각종 측정 장비에서 출력되는 결과지나 의사들이 작성한 기록지를 스캔하여 이미지형태로 저장하는 이미징 시스템 개발이 크게 요구되고 있다. 본 논문에서는 신경망과 그래픽 기법을 사용하여 대학병원 심전도실에서 사용되는 여섯 종류의 심전도 출력지를 이미지 형태로 저장하고 검색하는 이미징 시스템의 설계와 구현에 대해 논하였다. 구현된 시스템은 여섯 종류의 심전도 출력지를 분류하고, 분류된 각 출력지에 인쇄된 중요한 측정 데이터를 인식하여 데이터베이스에 저장한다. 심전도 출력지의 분류는 각 샘플 서식들의 평균 히스토그램을 구한 다음 새로운 출력지가 들어올 때 평균 히스토그램과의 거리가 가장 가까운 출력지로 분류하는 nearest-neighbor 방법을 사용하였다. 출력지에 인쇄된 데이터의 인식을 위해 먼저 XML로 작성한 출력지별 추출 정보를 기반으로 스캔한 이미지의 영역 분할 작업을 수행한다. 분할된 영역들은 신경망을 이용해 문자 인식을 하고, 인식된 문자들이 데이터베이스의 해당 속성값으로 저장된다. 스캔한 출력지는 의사들이 주석을 붙이거나 조건 검색을 위해 이미지 형태로 저장된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.5-13
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• 1998

In this paper, a fuzzy controller for DC-DC converters is proposed in order to obtain good performances that can not be achieved by liner control techniques in the presence of wide parameter variz. tions. The presented approach is general and can he applied to any DC-DC converter topologies. While the conventional method uses error and derivative of error, the proposed controller method uses state variables. Such method is very efficient in case of DC-DC converters and can guaranlee both stable small-signal responses and improved large-signal responses. Application results of boost converter(booster) show control potentialities.