• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.317-320
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• 2002

In this paper, an automatic histogram specification method is proposed for image enhancement, Fuzzy membership value is adopted for the representation of image histogram. The desired PDF is automatically constructed by the fuzzy membership value. Fuzzy membership value is extracted from dark membership, bright membership function and original histogram. The effectual results are demonstrated by desired PDF which meet the image enhancement requirements. The performance and effectiveness are shown by the analysis and the resultant image in comparison with histogram equalization method.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.125-129
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• 2004

There are several kinds of fuzzy set extensions in the fuzzy set theory. Among them, this paper is concerned with interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets. In interval-valued fuzzy sets, membership degrees are represented by an interval value that reflects the uncertainty in assigning membership degrees. In intuitionistic fuzzy sets, membership degrees are described with a pair of a membership degree and a nonmembership degree. In bipolar-valued fuzzy sets, membership degrees are specified by the satisfaction degrees to a constraint and its counter-constraint. This paper investigates the similarities and differences among these fuzzy set representations.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.12-15
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• 2001

There are several kinds of fuzzy set extensions in the fuzzy set theory. Among them, this paper is concerned with interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets. In interval-valued fuzzy sets, membership degrees are represented by an interval value that reflects the uncertainty in assigning membership degrees. In intuitionistic sets, membership degrees are described with a pair of a membership degree and a nonmembership degree. In bipolar-valued fuzzy sets, membership degrees are specified by the satisfaction degrees to a constraint and its counter-constraint. This paper investigates the similarities and differences among these fuzzy set representations.

• 한국지능시스템학회논문지
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• 제5권1호
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• pp.15-20
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• 1995

삼각 멤버쉽함수는 적용의 간편성으로 인하여 가장 절리 쓰이는 멤버쉽함수이다. 그러므로, 각 삼각형의 밑변의 길이가 퍼지 추론의 결과에 영향을 주는 이유에 대한 해석이 필요하다. 본 논문에서는 일정 비의 규칙성을 갖는 삼각 멤버쉽함수가 결과에 어떠한 영향을 미치는 지에 대하여 기하하적인 접근 방법으로 해석해 보았다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.211-216
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• 2004

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권1호
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• pp.4-9
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• 2009

Fuzzy entropy is designed for non convex fuzzy membership function using well known Hamming distance measure. Design procedure of convex fuzzy membership function is represented through distance measure, furthermore characteristic analysis for non convex function are also illustrated. Proof of proposed fuzzy entropy is discussed, and entropy computation is illustrated.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.21-22
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• 2008

Fuzzy entropy is designed for non-convex fuzzy membership function using well known Hamming distance measure. Design procedure of convex fuzzy membership function is represented through distance measure, furthermore characteristic analysis for non-convex function are also illustrated. Proof of proposed fuzzy entropy is discussed, and entropy computation is illustrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.255-258
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• 2000

We prepose fuzzy statistical test of fuzzy hypotheses membership function with fuzzy number data. Finding the maximum grade of the meeting point for fuzzy hypotheses membership function and membership function of confidence interval. By the maximum grade, we obtain the results to acceptance or reject for the test of fuzzy hypotheses.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.842-845
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• 2004

The aim of this study is to compare the classification results for choosing the fuzzy membership function within fuzzy rules. There are various methods of extracting rules from training data in the process of fuzzy rules generation. Pattern distribution characteristics are considered to produce fuzzy rules. The accuracy of classification results are depended on not only considering the characteristics of fuzzy subspaces but also choosing the fuzzy membership functions. This paper shows how to produce various type of fuzzy rules from the partitioning the pattern spaces and results of land cover classification in satellite remote sensing images by adopting various fuzzy membership functions. The experiments of this study is applied to Landsat TM image and the results of classification are compared by fuzzy membership functions.