• 음성과학
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• 제10권4호
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• pp.23-40
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• 2003

This paper examines transcription of sounds from a phonological perspective. It has found that most of transcriptions have been done on a segmental basis alone, without consideration of the whole phonological systems and levels, and without a full understanding of the nature of the linguistic and phonetic alphabets. In a word, sound transcriptions have not been done on the basis of the phonology of the language and the alphabet. This study shows a phonological model for transcribing foreign and native sounds, suggesting ways of improving some of the current transcription systems such as the Hangeul transcription of loan words and the romanization of Hangeul, as well as the phonetic transcription of English and other foreign languages.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1181-1186
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• 1993

In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1082-1087
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• 1994

A set of text database is indispensable to the probabilistic models for speech recognition, linguistic model, and machine translation. We introduce an environment to canstruct text databases : an automatic tagging system and a set of tools for lexical knowledge acquisition, which provides the facilities of automatic part of speech recognition and guessing.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.517-521
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• 1998

DEA(data envelopment analysis) is a non-parametric technique for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs. In fact, in a real evaluation problem input and output data of entities often flucturate. These fluctuating data can be represented as linguistic variables characterized by fuzzy numbers. Based on a fundamental CCR model, a fuzzy DEA model is proposed to deal with fuzzy input and output data, Furthermore, a model that extends a fuzzy DEA to a more general case is also proposed with considering the relation between DEA and RA (regression analysis) . the crisp efficiency in CCR modelis extended to an L-R fuzzy number in fuzzy DEA problems to reflect some uncertainty in real evaluation problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.353-353
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• 2000

To control a light weight flexible manipulator, a composite fuzzy controller is proposed. The controller is designed based on two time scaled models. A singular perturbation technique is applied for deriving the models. The proposed controller, however, does not use the complex equilibrium manifold equations, which are usually needed in the controller based on the two time scaled models. The controller for a slow sub-model and a fast sub-model are T-S type fuzzy controllers, which use 3 linguistic variables for each sub-model. A step trajectory is used in simulations as a reference trajectory of joint motions. The results of simulations with the proposed controller show excellent damping of flexible motions compared to a controller with derivative control of flexible motions.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 춘계학술대회
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• pp.155-159
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• 1995

A design method that can easily construct intelligent patient monitoring systems is proposed. To achieve the design method, the SES/MB concept and a discrete event-based logic control formalism based on a set theory is introduced. In this control paradigm the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by DEVS model of the system under control. Because data to be used for rule-based symbolic reasoning are to be abstracted, several AI methods are applied the processes. These methods are applied to intelligent patient monitoring systems so that they facilitate transformation from low level raw data to high level linguistic data. Model-based system representations have advantages of reusability, extensibility, flexsibility, independent testability and encapsulation.

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.720-725
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• 2001

비선형 미분방정식으로부터 TSK(Takagi-Sugeno-Kang) 퍼지모델을 유도한느 것은 퍼지 제어의 이론분야에서는 매우 중요한 문제이다. 본 논문에서는 off-equilibrium에서 상수항을 가지는 부분 미분 방정식을 배제시키는 방법을 제안한다. 이는 전건부의 언어적 표현이 삼각형 소속함수들을 가지는 기본적인 TSK 퍼지모델에서 체계적으로 유도되어진다. 그리고, 유도된 TSK 퍼지모델의 전건부 소속함수들은 GA(Genetic Algorithm)를 이용하여 최적화함으로써 실제 미분방적식에 근사화한다. 아울러 이상의 제안된 방법의 우수성을 모의실험을 통하여 검증한다.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.832-836
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• 2008

퍼지회귀분석은 독립변수들과 종속변수간의 관계를 평가하는데 사용된다. 만약 언어적 표현으로 된 자료를 처리할 때 일반적인 회귀분석을 사용한다면 과도한 단순화 때문에 어느 정도 한계를 가진다. 본 논문에서는 프로젝트의 성과를 예측하기 위해 퍼지 입출력을 갖는 퍼지회귀분석을 사용한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.