• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.871-876
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• 2002

In this paper, we establish some characterizations of tightness for a sequence of random elements taking values in the space of upper-semicontinuous fuzzy sets with compact support in R.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.113-118
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• 1998

In this paper we study central limit theorem(C.L.T.) and law of iterated logarithm (L.I.L.) for fuzzy random variables with respect to Hausdorff distance.

• Nonlinear Functional Analysis and Applications
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• 제27권3호
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• pp.621-640
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• 2022

The aim of this study is to establish some mean convergence theorems for double array of fuzzy random variables in metric space endowed with a convex combination operation under various assumptions.

• 한국안전학회지
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• 제30권4호
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• pp.151-158
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• 2015

Usually risk assessment is performed for the safety of diverse industries though, many kinds of risks cannot be analyzed effectively by using classical probability models due to lack of experience data and impreciseness of human decision making. For these reasons, fuzzy risk assessment utilizing subjective judgment and experience of skillful experts has been considered as a solution. In this study, to comprehend the relationship between conventional fuzzy theory and human conceptual images on risks, linguistic variables were reviewed with reference to fuzzy membership functions, especially in the Korean language. As interviewees, about a hundred people including students as well as safety engineers voluntarily participated. The research results showed that most people were in favor of adjective expressions decorated with adverbs rather than naive expressions such as "high" or "low", and that directly translated linguistic variables were not appropriate for the Korean people in risk assessment as far. Therefore, with consideration of the selection tendency by the Korean people in linguistic variables, it could be concluded that 5 level expressions would be most favorable for linguistic variables in risk assessments in Korea.

• 조명전기설비학회논문지
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• 제15권1호
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• pp.70-77
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• 2001

본 논문에서는 고도정수처리설비에 활용되는 오존접촉조의 반응 특성을 퍼지 모델 형태로 표현하는 방법을 제안한다. 퍼지모델에 사용될 입력 및 출력 변수들은 오존처리의 목적과 정수장의 수질관리항목을 기준으로 선정하였다. 제안된 입력 변수들은 용존유기탄소농도, $UV_{254}$흡광도, 과망간산칼륨소비량, 주입오존농도, 수온 및 접촉시간이며, 출력변수들은 용존유기탄소농도, $UV_{254}$흡광도 및 과망간산칼륨소비량이다. 입력변수들에 대한 소속도 함수들은 삼각형 형태로 설계하였으며, 파이롯플랜트에서 취득한 조업데이터를 참고하여 소속도를 결정했다. 퍼지모델의 결론부는 선형식 형태로 설계하였으며, 선형식에 포함되는 상수들은 조업데이터를 이용하여 최소자승법으로 구했다. 또한 출력 변수들간에 상호 영향이 없다는 특성으로부터 전체 퍼지모델로을 각 변수별로 독립적인 기능을 갖는 3개의 부분 퍼지모델로 분할하여 설계함으로서 계산과 이해의 편리를 도모하였다. 모의실험을 통해 제안된 퍼지모델의 타당성을 확인해 본 결과, 모델의 튜닝시에 사용한 입력 데이터에 대해 퍼지모델의 출력이 조업데이터와 거의 동일함을 알 수 있었다.

• 한국안전학회지
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• 제19권1호
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• pp.49-55
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• 2004

This study presents an analysis of comparison of P-type fire detection system with fuzzy logic-applied fire detection system. The fuzzy logic-applied fire detection system has input variables obtained by fire experiment of small scale with K-type temperature sensor and optical smoke sensor. And the antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire probability. Also triangular fuzzy membership function is used for input variables and fuzzy rules. To calculate the final fire probability a centroid method is introduced. A fire experiment is conducted with controlling wood crib layer, cigarette to simulate actual fire and false alarm situation. The results show that peak fire probability is 25[%] for non-fire and is more than 80[%] for fire situation, respectively. The fuzzy logic-applied fire detection system suggested here is able to distinguish fire situation and non-fire situation very precisely.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1264-1272
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• 1993

The software for unmanned control of three row typed rice combine has been developed using fuzzy logic. Three fuzzy variables were used : operating status of combine, steering, and speed. Eleven fuzzy rules were constructed and the eleven linguistic variables were used for the fuzzy rules. Six sensors were use of to get input values and sensor input values were quantified into 11 levels. The fuzzy output was infered with fuzzy inferrence which uses the correlation product encoding , and it must have been defuzzified by the method of center of gravity to use it for the control. The result of performance test using graphic simulation showed that the intelligently unmanned control of a rice combine was possible using fuzzy logic control.

• Journal of applied mathematics & informatics
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• 제42권2호
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• pp.379-390
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• 2024

This study proposes a fuzzy inventory model for managing large-scale production, incorporating cost considerations. The model accounts for two types of expenditure scenarios-parametric and exponential. Uncertainty surrounds holding costs, setup costs, and demand rates. The approach considers a supply chain system with a complex manufacturing process, factoring in transportation costs based on the quantity of goods and distance between the supplier and retailer. The initial crisp model is then transformed into a fuzzy simulation, incorporating specific fuzzy variables affecting inventory costs. The proposed method significantly reduces overall inventory costs for the entire supply chain. Retailer demand is linked to inventory levels, and vendor/distributor storage deteriorates over time. The fuzzy condition assumes hexagonal variables for all associated factors. The study employs the signed distance method for defuzzification to determine the optimal order quantity with hexagonal fuzzy numbers. Mathematical examples are provided to illustrate the practicality of the proposed approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.