• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.493-495
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• 1998

Robust design is one method to make manufacturing less sensitive to manufacturing process. Also it is cost effective technique to improve the quality process. This method uses statistically planned experiments to vary settings of important process control parameters. In this paper we apply fuzzy optimization and fuzzy logic system to robust design concept. First a method which uses fuzzy optimization in obtaining optimum settings by measured data from experiments will be presented. Second, fuzzy logic system is made to reduce experiments using experiments results consisted with key control parameter combinations. Then optimum parameter set points are obtained by the descrebed first fuzzy optimization method after prediction the results of each parameter combinations considering each control parameter variations by nonsingleton fuzzy logic system concept.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1856-1859
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• 1997

This paper describes the arousal measurement and the control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Membership function and rule base were determined form the modfied arousal level criteria. The output of fuzzy inference tracked well the change of subject's arousal level. When IRI(Inter-SIR interval) was under the 60sec, maximum output of three step warning method was medium sound, but that of fuzzy logic system was changed from medium to big. Furthermore, the output of the fuzzy inference was highly correlated with $N_{z}$(r=0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving sityation than three step warning method.ning method.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2005년도 춘계학술대회 논문집
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• pp.5-9
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• 2005

One of the imminent problems to be solved within wireless sensor network is to balance out energy dissipation among deployed sensor nodes. In this paper, we present a transmission relay method of communications between BS (Base Station) and CHs (Cluster Heads) for balancing the energy consumption and extending the average lifetime of sensor nodes by the fuzzy logic application. The proposed method is designed based on LEACH protocol. The area deployed by sensor nodes is divided into two groups based on distance from BS to the nodes. RCH (Relay Cluster Head) relays transmissions from CH to BS if the CH is in the area far away from BS in order to reduce the energy consumption. RCH decides whether to relay the transmissions based on the threshold distance value that is obtained as a output of fuzzy logic system. Our simulation result shows that the application of fuzzy logic Provides the better balancing of energy depletion and Prolonged lifetime of the nodes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• 설비공학논문집
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• 제13권9호
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• pp.810-817
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• 2001

Performance of a water-to-water multi-type heat pump system using R22 which has tow indoor units has been investigated experimentally. The refrigerant flow rate of each indoor unit was regulated by an electronic expansion valve and the total refrigerant flow rate of the system was controlled by a variable speed compressor. In the system, evaporator outlet pressure of refrigerant and outlet temperatures of secondary fluid from indoor units were selected as control variables. Experiments were executed for both cooling and heating modes using PID control method with fuzzy logic, and results of the test are compared with a classical PID method. In the case of PID control with fuzzy logic, the fuzzy control rules corrects PID parameters each time. Results show that PID control with fuzzy logic has the merits of quick response and reduced overshoot.

• Journal of information and communication convergence engineering
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• 제4권1호
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• pp.36-40
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• 2006

A new method of digital image analysis technique for medical images of cancer cell is presented. This paper deals with the cancer cell discrimination. The object images were the Thyroid Gland cell images that were diagnosed as normal and abnormal. This paper proposes a new discrimination method based on fuzzy logic algorithm. The focus of this paper is an automatic discrimination of cells into normal and abnormal of medical images by dominant feature parameters method with fuzzy algorithm. As a consequence of using fuzzy logic algorithm, the nucleus were successfully diagnosed as normal and abnormal. As for the experimental result, average recognition rate of 64.66% was obtained by applying single parameter of 16 feature parameters at a time. The discrimination rate of 93.08% was obtained by proposed method.

• 한국지능시스템학회논문지
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• 제16권3호
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• pp.303-308
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• 2006

본 논문에서는 다입력 퍼지 로직 시스템에서 생기는 퍼지 규칙수의 기하급수적 증가를 막기 위하여, 주어진 퍼지 시스템의 THEN 부분을 이용하여 퍼지 규칙 벡터를 정의하고, 이를 이용하는 2계층의 계층 퍼지 시스템으로 변환하는 방법을 제시한다. 여기에서, 1번째 계층에서는 주어진 퍼지 시스템으로부터 생성되는 일차독립의 퍼지 규칙 벡터를 사용하고, 2계층에서는 1계층에서 사용된 퍼지 규칙 벡터들의 선형 합을 사용한다. 변환된 2계층의 퍼지 시스템은 주어진 퍼지 시스템과 동일한 근사 능력을 가질 뿐 아니라, 더 적은 수의 퍼지 규칙을 가짐을 보인다.

• 한국정보통신학회논문지
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• 제5권7호
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• pp.1374-1383
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• 2001

기존의 제어방식 보다 강인성이 우수한 퍼지 논리 제어방식에서 최적화되지 않은 제어규칙을 이용하여, 오프라인 상에서 소속함수 관계와 스케일링 팩터를 유전자알고리즘으로 최적화한 후, 온라인으로 퍼지제어기를 구성하는 제어방식을 제안하였다. 제안한 방식을 단일 링크 매니률레이터의 추종제어에 적용하여 기존 퍼지제어 방식과 비교 검토한 결과 퍼지제어규칙의 수도 감소하고 제어성능도 우수함을 확인하였다.