• 한국지능시스템학회논문지
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• 제15권6호
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• pp.754-758
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• 2005

There have been several tipical methods being used tomeasure the fuzziness (entropy) of fuzzy sets. Pedrycz is the original motivation of this paper. Recently, Wang and Chiu [FSS103(1999) 443-455] and Pedrycz [FSS 64(1994) 21-30] showed the relationship(addition, subtraction, multiplication) between the entropies of the resultant fuzzy number and the original fuzzy numbers of same type. In this paper, using Lebesgue-Stieltjes integral, we generalize results of Wang and Chiu [FSS 103(1999) 443-455] concerning entropy arithmetic operations without the condition of same types of fuzzy numbers. And using this results and trade-off relationship between information energy and entropy, we study more properties of information energy of fuzzy numbers.

• 한국정밀공학회지
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• 제21권4호
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• pp.202-209
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• 2004

Quality control is essential to the final product in BGA-type PCB fabrication. So, many automatic vision systems have been developed to achieve speedy, low cost and high quality inspection. A multiple threshold selection algorithm is a very important technique for machine vision based inspection. In this paper, an inspected image is modeled by using fuzzy sets and then the parameters of specified membership functions are estimated to be in maximum fuzzy entropy with the probability of the fuzzy sets, using the exhausted search method. Fuzzy c-partitions with the estimated parameters are automatically generated, and then multiple thresholds are selected as the crossover points of the fuzzy sets that form the estimated fuzzy partitions. Several experiments related to flip chip BGA images show that the proposed algorithm outperforms previous ones using both entropy and variance, and also can be successfully applied to AVI systems.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.9-12
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• 2002

Representation and quantification of fuzziness are required for the uncertain system modelling and controller design. Conventional results show that entropy of fuzzy sets represent the fuzziness of fuzzy sets. In this literature, the relations of fuzzy enropy, distance measure and similarity measure are discussed, and distance measure is proposed. With the help of relations of fuzzy enropy, distance measure and similarity measure, fuzzy entropy is represented by the newly proposed distance measure. With simple fuzzy set, example is illustrated.

• 한국융합학회논문지
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• 제5권4호
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• pp.155-161
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• 2014

We survey the relation of fuzzy entropy measure and similarity measure. Each measure represents features of data uncertainty and certainty between comparative data group. With the help of one-to-one correspondence characteristics, distance measure and similarity measure have been expressed by the complementary characteristics. We construct similarity measure using distance measure, and verification of usefulness is proved. Furthermore analysis of similarity measure from fuzzy entropy measure is also discussed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.543-546
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• 2003

Representation and quantification of fuzziness are required for the uncertain system modelling and controller design. Conventional results show that entropy of fuzzy sets represent the fuzziness of fuzzy sets. In this literature, the relations of fuzzy enropy, distance measure and similarity measure are discussed, and distance measure is proposed. With the help of relations of fuzzy entropy, distance measure and similarity measure, fuzzy entropy is proposed by the distance measure. Finally, proposed entropy is applied to measure the fault signal of induction machine.

• 한국융합학회논문지
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• 제4권2호
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• pp.35-41
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• 2013

Data management with fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem. Calculation of certainty or uncertainty for data, fuzzy entropy and similarity measure are designed and proved. Proposed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration.Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.366-369
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• 2007

In this paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.

• 한국통신학회논문지
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• 제21권6호
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• pp.1390-1397
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• 1996

In this paper, in case of segmenting an image by a fuzzy entropy, an image segmentation algorithm is derived under an extended fuzzy entropy including the probabilistic including the probabilistic information in order to cover the toal uncertainty of information contained in fuzzy sets. By describing the image with fuzzysets, the total uncertainty of a fuzzy set consists of the uncertain information arising from its fuzziness and the uncertain information arising from the randomness in its ordinary set. To optimally segment all the boundary regions in the image, the total entropy function is computed by locally applving the fuzzy and Shannon entropies within the width of the fuzzy regions and the image is segmented withthe global maximum andlocal maximawhich correspond to the boundary regions. Comtional one by detecting theboundary regions more than 5 times.

• 한국지능시스템학회논문지
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• 제17권2호
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• pp.149-153
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• 2007

본 논문에서 우리는 Wang와 Li(1998)와 Turksen(1986)에 의해 소개된 구간치 퍼지집합을 생각하고 구간치 퍼지집합상에서 쇼케이적분에 의해 정의된 엔트로피를 조사한다. 더욱이, 이러한 엔트로피와 관련된 성질들을 토의하고 간단한 예들을 알아본다. 이 공식은 구간치 퍼지집합상의 결정이론 및 정보이론과 같은 응용 영역에서 중요한 역할을 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2876-2878
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• 1999

There have been several tipical methods being used to measure the fuzziness (entropy) of fuzzy sets. Pedrycz is the original motivation of this paper. This paper studies the entropy variation on the fuzzy numbers with arithmetic operations(addition, subtraction, multiplication). It is shown that through the arithmetic operations, the entropy of the resultant fuzzy number has the arithmetic relation with the entropy of each original fuzzy number. This paper generalize earlier results of Pedrycz [FSS 64(1994) 21-30] and Wang and Chiu [FSS 103(1999) 443-455].