• 한국지능시스템학회논문지
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• 제14권1호
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• pp.88-92
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• 2004

In this paper, we define and study the semiprimary k-fuzzy ideals in a commutative k-semiring and characterize the quotient semiring R/A of a k-semiring R by a semiprimary k-fuzzy ideal A. In particular, we show that every zero divisor of R/A is nilpotent.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권4호
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• pp.359-371
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• 2001

자원제약 하에서의 프로젝트 스케줄링 문제는 많은 불확실성의 요소들을 내포하고 있으며 스케줄의 구성은 전문가에 의한 자의적 판단에 따르는 것이 일반적 현상이다. 전문가는 스케줄을 구성하기 위해 자신의 경험을 토대로 프로젝트 진행을 위한 작업(activity)들을 식별하고 이 작업들 사이의 선행관계를 규정하여 각 작업들에 대한 예상 시간을 이용하여 스케줄을 작성한다. 이 때 대부분의 스케줄링 방법들은 비용과 작업기간 두 요소들 중 한 요소에 집중하게 된다. 또한 스케줄 작성의 중요한 요소인 작업기간 결정이 전문가의 경험에 의존하여 결정됨으로써 결정된 작업기간의 불확실성을 초래할 수 있으며 따라서 이 불확실한 작업기간을 이용하여 구성된 스케줄의 불확실성이 증대되는 문제를 내포하고 있다. 이와 같은 문제 즉 스케줄 구성에 있어 작업기간의 불확실성과 작업비용을 함께 고려하지 못하는 문제를 해결하기 위해 이 논문에서는 Fuzzy 개념을 이용한 작업기간의 정형화를 시도하며 아울러 정형된 퍼지 작업 기간과 결합 가능한 퍼지 작업비용을 도입하여 작업기간과 작업비용을 함께 고려하는 Fuzzy-MOEH 스케줄링 알고리즘을 제안한다. 아울러 이 논문에서 제안한 Fuzzy-MOEH 알고리즘과 기존 MOEH 알고리즘의 수행 결과에 대한 비교를 통해 Fuzzy-MOEH 알고리즘의 효용성 및 특성을 분석한다.

• Journal of Advanced Marine Engineering and Technology
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• 제26권4호
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• pp.415-424
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• 2002

In this paper, a robust fuzzy precompensated PID controller using immune algorithm for load frequency control of 2-area power system is proposed. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic based precompensation approach for PID controller. This scheme is easily implemented by adding a fuzzy precompensator to an existing PID controller. We optimize the fuzzy precompensator with an immune algorithm for complementing the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and fuzzy rules. Simulation results show that the proposed robust load frequency controller can achieve good performance even in the presence of generation rate constraints.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.13-16
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• 2000

In fuzzy expert systems or database systems that have huge volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environment. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy rules or data, the parallel fuzzy inference algorithm extracts effective parallel ism and achieves a good speed factor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 추계학술대회 논문집 학회본부
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• pp.118-121
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• 1988

Load forecasting is an important issue as for the economic dispatch and there have been many researches which are classfied into two classes, time series method and factor analysis method. But the former is not adaptive for a sudden change of a correlated factor and the latter is not inefficient as the factor estimation is not easy. To make matters worse, both of them are not good for the estimation of special days. It is because the load forecasting is not a problem modeled precisely in mathematics, but a problem requires experience and knowledge those can solve it case by case. In this viewpoint, an expert system is proposed which can use complicated experience of an expert by use of fuzzy decision.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 콘크리트학회논문집
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• 제17권1호
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• pp.157-166
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• 2005

This article involves architecting prototype-fuzzy expert system for designing the nominal cover thickness by means of fuzzy inference for quantitatively representing the environment affecting factor to reinforced concrete in chloride-induced corrosion environment. In this work, nominal cover thickness to reinforcement in concrete was determined by the sum of minimum cover thickness and tolerance to that defined from skill level, constructability and the significance of member. Several variables defining the quality of concrete and environment affecting factor (EAF) including relative humidity, temperature, cyclic wet and dry, and the distance from coast were treated as fuzzy variables. To qualify EAF the environment conditions of cycle degree of wet-dry, relative humidity, distance from coast and temperature were used as input variables. To determine the nominal cover thickness a qualified EAF, concrete grade, and water-cement ratio were used. The membership functions of each fuzzy variable were generated from the engineering knowledge and intuition based on some references as well as some international codes of practice.

• Nuclear Engineering and Technology
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• 제53권11호
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• pp.3665-3674
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• 2021

One of the important parts of the in-core fuel management is loading pattern optimization (LPO). The loading pattern optimization as a reasonable design of the in-core fuel management can improve both economic and safe aspects of the nuclear reactor. This work proposes the hybrid of fuzzy logic controller with harmony search algorithm (HS) for loading pattern optimization in a pressurized water reactor. The music improvisation process to find a pleasing harmony is inspiring the harmony search algorithm. In this work, the adjustment of the harmony search algorithm parameters such as the bandwidth and the pitch adjustment rate are increasing performance of the proposed algorithm which is done through a fuzzy logic controller. Hence, membership functions and fuzzy rules are designed to improve the performance of the HS algorithm and achieve optimal results. The objective of the method is finding an optimum core arrangement according to safety and economic aspects such as reduction of power peaking factor (PPF) and increase of effective multiplication factor (Keff). The proposed approach effectiveness has been tried in two cases, Michalewicz's bivariate function problem and NEACRP LWR core. The results show that by using fuzzy harmony search algorithm the value of the fitness function is improved by 15.35%. Finally, with regard to the new solutions proposed in this research it could be used as a trustworthy method for other optimization issues of engineering field.

• 드라이브 ㆍ 컨트롤
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• 제17권1호
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.51-56
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• 1998

In this paper, we analyze the effects of scaling factors on the performance of a fuzzy logic controller(FLC). The quantitative relation between input and output variables of FLC is obtained by using a qualsi-linear fuzzy model, and an approximate transfer function of FLC is dervied from the comparison of it with the conventional PID controller. Then we analyze in detail the effects of scaling factor using this approximate transfer function and root locus method. Also we suggest an on-line tuning method for scaling factors which employs an sample performance function and a variable reference for tuning index.