• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.43-54
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• 1997

Knowledge acquisition is one of the most difficult problems in designing fuzzy systems. As application domains of fuzzy systems become larger and more complex, it is more difficult to find the relations among the system's input- outpiit variables. Moreover, it takes a lot of efforts to formulate expert's knowledge about complex systems' control actions by linguistic variables. Another difficulty is to define and adjust membership functions properly. Soin conventional fuzzy systems, the membership functions should be adjusted to improve the system performance. This is time-consuming process. In this paper, we suggest a new approach to design a fuzzy system. We design a fuzzy system using two neural networks, Kohonen neural network and backpropagation neural network, which generate fuzzy rules automatically and construct inference network. Since fuzzy inference is performed based on fuzzy relation in this approach, we don't need the membership functions of each variable. Therefore it is unnecessary to define and adjust membership functions and we can get fuzzy rules automatically. The design process of fuzzy system becomes simple. The proposed approach is applied to a simulated automatic car speed control system. We can be sure that this approach not only makes the design process of fuzzy systems simple but also produces appropriate inference results.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.27-32
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• 2012

In this paper, we propose a fuzzy method that improves the existing problem of the fuzzy filtering algorithm. The proposed fuzzy filtering algorithm separates R, G, and B channels from the color image. Mask information was extracted from separated channels and the brightness of the mean value and median value for channels was applied in the function of the proposed fuzzy method to calculate the membership and achieve application in the inference rule. Also, the membership degrees of R, G, and B were used to distinguish the possibility of noise. The proposed fuzzy method selected three membership functions. If noise is distinguished, the noise is eliminated by selecting the median value or mean value as the relevant pixel value according to the degree of noise. By applying the proposed method in color images, it was verified that the proposed method is more effective in eliminating noise when compared with the conventional fuzzy filtering method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.1-12
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• 2000

In this paper we propose a design method of the adaptive fuzzy controller for autonomous navigation of mobile robots based on the fuzzy theory. We present two improvements. First, unnecessary rules in the fuzzy inference process make data processing time increase. We reduce this data processing time by generating suitable fuzzy inference rules and membership functions according to the current state of a mobile robot. It is implemented with the clustering method using input and output data pairs, and then it is possible for a mobile robot to navigate in shorter processing time with less fuzzy inference rules. Second, existing algorithms used fixed membership functions of input and output variables, hence converged slowly. We improve convergence time via scaling membership functions generated by the clustering method. To evaluate and compare the performance of the proposed method with the existing fuzzy navigation controller, computer simulations and navigation experiments of a mobile robot are Presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.299-302
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• 2003

In fuzzy control systems having large volumes of fuzzy data. one of the important problems is the improvement of execution speed in the fuzzy inference and defuzzification stages. In this paper, to improve the speedup of fuzzy controllers, we use an integer line mapping algorithm to convert [0, 1] real values in the fuzzy membership functions to integer pixels. U sing this, we propose a real-time high-speed fuzzy control system and implement a fast fuzzy processor and control system using FPGAs.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.979-985
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• 2006

In the conventional researches, the reliabilities of the fuzzy system are represented and analyzed by real values between zero and one, fuzzy numbers, intervals of confidence, etc. In this paper, we present a method to represent and analyze the reliabilities of the weighted components of the fuzzy system and the weights reflected on their importance based on vague sets defined in the universe of discourse [0, 1]. The vague set is represented as the interval consisted of the truth-membership functions and the false-membership functions, therefore it can allow the reliabilities and the weights of a fuzzy system to represent in a more flexible manner. The proposed method considers the weights of the weighted components in the fuzzy systems, its reliability analysis is more flexible and effective than the conventional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.115-117
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• 2004

인간과 인간 사이에 컨텍스트의 역할이 중요한 것처럼 기계가 컨텍스트를 인식할 수 있는 능력을 갖추는 것은 중요하다. 특히 지능적인 서비스를 제공하기 위해서는 고수준 컨텍스트를 추출하는 것이 필요하고, 최근 베이지안 네트워크를 이용해 컨텍스트를 추출하려는 연구가 많이 있었다. 그러나 대부분은 단순한 컨텍스트를 추출하는 연구들이고, 상황이나 사용자에 따라 다른 특성을 보이는 경우에 대한 처리는 하지 못하고 있다. 본 논문은 퍼지 소속 함수를 통해 각 센서에서 오는 정보를 전 처리하고, 이를 베이지안 네트워크를 이용해 고수준 컨텍스트로 추출하는 방법을 제안한다. 특히 여러 개의 퍼지 노드가 있을 경우 퍼지 소속값의 곱을 사용하여 베이지안 추론에 적용하였다. 각 센서의 정보를 처리하는 퍼지 소속 함수는 사용자가 쉽게 설계할 수 있고, 컨텍스트 추출모듈과 별개로 설계가 가능하기 때문에 베이지안 네트워크의 유연하고 적응적인 특성을 유지하면서 개인화가 가능하다. 제안한 방법의 유용성을 보이기 위해 실제 세계의 문제를 모델링한 베이지안 네트워크의 예를 보이고 이를 분석한다.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.195-214
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• 2017

Statistical landslide susceptibility analysis, which is widely used among various landslide susceptibility analysis approaches, predicts the unstable area by analyzing statistical relationship between landslide occurrence locations and landslide controlling factors. However, uncertainties are involved in the procedures of the susceptibility analysis and therefore, fuzzy approach has been used to deal properly with uncertainties. The fuzzy approach used fuzzy set theory and fuzzy membership function to quantify uncertainties involved in landslide controlling factors. Various fuzzy approaches were suggested in the procedure of the membership value determination and fuzzy operation in the previous researches. However, few studies were carried out to compare the analysis results obtained from various approaches for membership function determination and fuzzy operation. Therefore, in this study, the authors selected Jinbu area, which a large number of landslides were occurred at in 2006, to apply two most commonly used methods, the frequency ratio and the cosine amplitude method to derive membership values for each controlling factor. In addition, the integration of different thematic layers to produce landslide susceptibility map was performed by several fuzzy operators such as AND, OR, algebraic product, algebraic sum and Gamma operator. The results of the landslide susceptibility analysis using two different methods for the determination of fuzzy membership values and various fuzzy operators were compared on the basis of ROC graph to check the feasibility of the fuzzy based landslide susceptibility analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.802-804
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• 2005

지능 시스템에 사용되는 퍼지 데이터를 고속으로 처리하기 위한 퍼지 제어시스템의 중요한 문제점들 중의 하나는 퍼지 추론 및 비퍼지화 단계에서의 수행속도의 개선이다. 특히 후건부의 계산 및 비퍼지화 단계에서의 고속 연산이 더욱 중요하다. 따라서 본 논문에서는 퍼지 제어기의 속도향상을 위해 후건부 단계에서 [0,1]의 실수 연산을 하지 않고, 퍼지 소속함수의 값을 정수형 격자 (400×30)에 매핑시켜 고속의 정수 덧셈 연산만으로 수행할 수 있는 알고리듬을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1269-1274
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• 2014

In computer vision research, binarization procedure is one of the most frequently used tools to discriminate target objects from background in grey level binary image. Fuzzy binarization is a reliable technique in environment with high uncertainty such as medical image analysis by setting the threshold as the average of minimum and maximum brightness with triangle type fuzzy membership function. However, this technique is also known as contrast sensitive method thus its discrimination power is not so great when the image has low contrast difference between objects and backgrounds and suffer from information loss as a result. Thus, in this paper, we propose a fuzzy binarization using ART2 algorithm to handle such low contrast image analysis. Proposed ART2 algorithm is applied to determine the medium point of membership function in the fuzzy binarization paradigm. The proposed methods shows low information loss rate in our experiment.