• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.241-245
• /
• 2001

The existing fuzzy traffic controllers construct the rule-base based on the intuitive knowledge and experience or the standard rule-base, but the rule-base constructed by the above methods has difficulty in representing exactly and detailedly the control knowledge of the export and the operator. Therefore, in this paper, we propose a method that can improve the performance of the fuzzy traffic control by designing the fuzzy traffic controller which represents the control knowledge more exactly. The proposed method so modifies the position and shape of the fuzzy membership function based on the input-output data clustering that the fuzzy traffic controller can represent the control knowledge more exactly. Our method use the rough control knowledge based on intuitive knowledge and experience as the evaluation function for clustering the input-output data. The fuzzy traffic controller designed by the our method could represent the control knowledge of the expert and the operator more exactly, and it outperformed the existing controller in terms of the number of passed vehicles and the wasted green-time.

• Journal of Bio-Environment Control
• /
• v.12 no.1
• /
• pp.30-37
• /
• 2003

An expert system was developed for the stress diagnosis of cucumber and tomato using FoxPro. The principle points in building the system were integration with Korean, effective processing of mass information, and easy access for non-experts such as farmers. The method of inferencing was forward chaining based on pattern matching. Knowledge base was expressed with IF∼THEN rules and was expressed in the form of tree. Also, the expert system was designed so that additions and modifications of all information could easily be performed on windows. The results tested by farmers with the developed system showed that the expert system was reliable for the practical use. It was expected the expert system could be directly applied to the stress diagnosis of other vegetable plants by modifying only data bases.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.91-97
• /
• 2011

In this paper we present an approach to the structure identification based on genetic algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy-genetic hybrid system in order to predicate the Mackey-Glass Chaotic time series. In this scheme the basic idea consists of two steps. One is the construction of a fuzzy rule base for the partitioned input space via genetic algorithm, the other is the corresponding parameters of the fuzzy control rules adapted by the backpropagation algorithm. In an attempt to test the performance the proposed system, three patterns, x(t-3), x(t-6) and x(t-9), was prepared according to time interval. It was through lots of simulation proved that the initial small error of learning owed to the good structural identification via genetic algorithm. The performance was showed in Table 2.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.555-565
• /
• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of Intelligence and Information Systems
• /
• v.1 no.1
• /
• pp.1-22
• /
• 1995

본 연구에서 퍼지인식도(Fuzzy Cognitive Map) 개념을 기초로 하여 (1) 특정 문제영역에 대한 전문가의 인과관계 지식(causal knowledge)을 추출하는 알고리즘을 제시하고, (2) 이 알고리즘에 기초하여 작성된 해당 문제영역에 대한 여러 전문가들의 인과관계 지식을 계층별로 분해하여, (3) 해당 계층간의 양방향 추론이 가능한 추론메카니즘을 제시하고자 한다. 특정 문제영역에 있어서의 인과관계 지식이란 해당 문제를 구성하는 여러 개념간에 존재하는 인과관계를 표현한 지식을 의미한다. 이러한 인과관계 지식은 기존의 IF-THEN 형태의 규칙과는 달리 행렬형태로 표현되기 때문에 수학적인 연산이 가능하다. 특정 문제영역에 대한 전문가의 인과관계 지식을 추출하는 알고리즘은 집합연산에 의거하여 개발되었으며, 특히 상반된 의견을 보이는 전문가들의 의견을 통합하여 하나의 통합된 인과관계 지식베이스를 구축하는데 유용하다. 그러나, 주어진 문제가 복잡하여 다양한 개념들이 수반되면, 자연히 인과관계 지식베이스의 규모도 커지게 되므로 이를 다루는데 비효율성이 개재되기 마련이다. 따라서 이러한 비효율성을 해소하기 위하여 주어진 문제를 여러계측(Hierarchy)으로 분해하여, 해당 계층별로 인과관계 지식베이스를 구축하고 각 계층별 인과관계 지식베이스를 연결하여 추론하는 메카니즘을 개발하면 효과적인 추론이 가능하다. 이러한 계층별 분해는 행렬의 분해와 같은 개념으로도 이해될 수 있다는 특징이 있어 그 연산이 간단명료하다는 장점이 있다. 이와같이 분해된 인과관계 지식베이스는 계층간의 추론메카니즘을 통하여 서로 연결된다. 이를 위하여 본 연구에서는 상향 또는 하향방식이 추론이 가능한 양방향 추론방식을 제시하여 주식시장에서의 투자분석 문제에 적용하여 그 효율성을 검증하였다.

• Journal of KIISE:Databases
• /
• v.35 no.2
• /
• pp.97-111
• /
• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Journal of KIISE:Software and Applications
• /
• v.31 no.2
• /
• pp.137-152
• /
• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Proceedings of the Korea Database Society Conference
• /
• 1995.12a
• /
• pp.187-190
• /
• 1995

자연스러운 음성 합성을 위해서는 운율 및 장단음 처리가 선행되어야 한다. 본 논문에서는 음성학적 실험과 억양 규칙을 이용하여 한국어 문장으로부터 운율 제어 정보와 모음 길이 정보를 추출한 뒤 음성 합성에 적용함으로써 합성음의 자연성을 향상시키는 방법을 제안한다. 이러한 정보는 문장 분석 후 일련의 운율 규칙을 적용하여 반복된 실험을 통해 수치화함으로써 얻을 수 있었다. 실험결과, 운율 및 장단처리를 적용한 본 시스템에서는 자연성이 향상된 결과를 얻을 수 있었다.

• Proceedings of the Korea Database Society Conference
• /
• 1995.12a
• /
• pp.197-202
• /
• 1995

본 논문에서는 조명, 얼굴 개수 및 얼굴의 크기에 제한 받지 않고 복잡한 배경에서 얼굴 영역을 추출하는 알고리즘을 제안한다. 이 알고리즘은 3단계로 구성된다. 첫번째 단계는 입력 영상의 평균 그레이값을 계산하고 그 값이 임계치보다 작다면 히스토그램 균일화 작업을 수행한다. 두번째 단계에서는 입력 영상의 모자이크 이미지를 만들고 이 이미지에 대해 확장된 quartet을 만들고 실험을 통해 얻어진 규칙을 적용하여 대략적으로 얼굴의 후보 영역들을 추출한다. 이 작업은 모자이크 이미지를 구성하는 셀의 크기를 변화시킬 때마다 적용한다. 세번째 단계에서는 추출된 얼굴 후보 영역에 대해 Octet을 만들고 이 octet에 규칙을 적용하여 후보 영역에 대한 검증 작업을 수행한다. 세번째 과정에서 만들어진 모자이크 이미지는 두 번째 과정에서 얻어진 이미지보다 더 세밀하게 얼굴의 특징들을 표현하고 검증한다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 1992.11a
• /
• pp.247-256
• /
• 1992

개념적 데이터 모델의 스키마를 논리적 스키마의 하나인 관계 스키마로의 변환 과정은 개체 무결성과 참조 무결성을 보장하여야 한다. 또한 안전한 데이터 베이스를 설계하고 구현하기 위해서는 보안성을 기존의 데이터 모델에 첨가시켜서 변환 과정에 보안성을 보장하기 위한 변환 규칙을 정의하여야 한다. 이 과정을 일관성 있고 효율적으로 하기 위해 변환 과정을 자동적으로 수행하는 도구가 필요하다. 본 논문에서는 보안 객체-관련성 모델을 이용하여 보안 관계 스키마로 변환하기 위한 변환 규칙을 정의하고, 변환 처리 과정을 자동적으로 수행하는 자동화 도구를 설계 및 구현하였다.