• IEIE Transactions on Smart Processing and Computing
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• 제6권2호
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.37-40
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• 1997

This paper presents a performance improvement of distributed fuzzy control system by changing the triangular membership function widths according to the input variables. The control region consists of 4 parts according to the sign of error and change of error terms. Each control part is operated by the suitable nonuniform triangular membership function. Through the simulation for the boiler-turbin model of a fossil power plant using decentralized contol, it is verified this proposal

• 한국안전학회지
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• 제8권4호
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• pp.82-90
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• 1993

Manual lifting, as a part of Manual Materials Handling Activities, is recognized by authorities in the field of occupational health and safety as a major hazard to industrial workers. The most important problem in applying fuzzy model of manual materials lifting is the decision of membership functions on each approaches. : Biomechanical, Physiological, Psychophysical. The primary objectives of this paper suggests to process deciding the most acceptable membership functions for establishing permissible weights on manual lifting activities using fuzzy sets.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.93.1-93
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• 2001

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.7-10
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• 1998

The conventional relational databases have difficulties to efficiently represent various of data because an attribute of a tuple should have only one elementary value. In order to represent ambiguous and imprecious information, fuzzy set and rough set have been gaining acceptance, especially as a tool for knowledge discovery in databases. One of former researches applies only one fuzzy membership value to a tuple. We suggest a more advanced model for data representation by way of applying many membership values to a tuple, i.e. one membership value to each attribute of a tuple.

• 디지털콘텐츠학회 논문지
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• 제18권1호
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• pp.217-224
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• 2017

최근에 카오스 이론을 사회과학의 한 분야의 사랑 모델에 적용하고자 하는 노력을 지속하고 있다. 로미오와 줄리엣으로 표현하는 미분 방정식에서 카오스 거동을 만들기 위해서 외력을 인가한다. 그러나 이 외력은 사람의 감정을 정확하게 표현하지 못하는 단점을 가진다. 본 논문에서는 이러한 단점을 해결하기 위하여, 로미오와 줄리엣의 사랑모델에서 외력을 사람의 말이나 행동에 가장 유사한 형태로 제공하기 위해 퍼지 소속 함수를 도입하고 이를 삼각 퍼지 소속 함수를 제시하였다. 또한 제시된 퍼지 소속 함수를 가진 로미오와 줄리엣의 사랑모델에서 카오스 거동을 확인하기 위하여, 시계열과 위상공간을 이용하였으며 이를 통하여 카오스 거동의 존재를 확인한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권3호
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• pp.182-187
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• 2007

A fuzzy modeling method is proposed to build the dynamic model of a walking-beam reheating furnace from the recorded data. In the proposed method, the number of membership function on each variable is increased individually and the modeling accuracy is evaluated iteratively. When the modeling accuracy is satisfied, the membership functions on each variable are fixed and the structure of fuzzy model is determined. Because the training data is limited, in this process, as the number of membership function increase, it is highly possible that some rules are missing, i.e., no data in the training set corresponds to the consequent part of a missing rule. To complete the rulebase, the output of the model constructed at the previous step is used to generate the consequent part of the missing rules. Finally, in the real time application, a rolling update scheme to rulebase is introduced to compensate the change of system dynamics and fine tune the rulebase. The proposed method is verified by the application to the modeling of a reheating furnace.