• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.331-333
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• 2001

본 논문에서는 퍼지집합의 소속함수에 대한 가중치 함수를 제안한다. 제안하는 가중치 함수는 퍼지집합의 소속함수에 곱해지는 형태로서 적용되어지며, 이것은 소속함수에 대한 사용자의 선호도를 의미한다. 제안하는 가중치 함수의 개념은 기본적으로 소속함수를 사용하는 어떤 퍼지 집합의 응용에서도 적용될 수 있을 것으로 보이나, 본 논문에서는 그 중 한가지 경우로 비퍼지화 방법을 적용 대상으로 선택하였다. 제안하는 가중치 함수가 비퍼지화 방법에 있어서 가지는 의미를 보이며, 기존의 비퍼지화 방법들에서 이러한 가중치 함수의 개념이 어떻게 적용되어 왔는지를 보인다. 또한 기존의 비퍼지화 방법들이 개녀멩 적용되지 않은 형태의 가중치 함수를 선택하여, 비퍼지화 방법에 특정 가중치 함수를 적용하였을 때의 특성 변화를 보인다. 이러한 일반적인 형태의 가중치 함수를 퍼지집합의 소속함수에 적용함으로서, 다양한 형태의 선호도를 퍼지집합의 형태에 반영할 수 있을 것으로 보인다.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.573-583
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• 2002

Pattern recognition process is made up of the feature extraction in the pre-processing, the pattern clustering by training and the recognition process. This paper presents the F-LVQ (Fuzzy Learning Vector Quantization) pattern classification model which is optimized by the fuzzy membership function for the OCR(Optical Character Recognition) system. We trained 220 numeric patterns of 22 Hangul and English fonts and tested 4840 patterns whose forms are changed variously. As a result of this experiment, it is proved that the proposed model is more effective and robust than other typical LVQ models.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.296-302
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• 2004

Acoustic experiments are performed for a seafloor classification from 19 May to 25 May 2003. The six different sites of bottom composition are settled and the bottom reflection losses with frequencies (30, 50, 80. 100, 120 kHz) are measured. Sediment samples were collected using gravity core and the sample was extracted for grain size analysis. The fuzzy logic is used to classify the seabed. In the fuzzy logic. Bottom 1083 model of frequency dependence is used as the input membership functions and the output membership functions are composed of the Wentworth grain size of the bottom. The possibility of the seafloor classification is verified comparing the inversed mean grain size using fuzzy logic with the results of the coring.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.1898-1909
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• 1993

In this paper, a convenient scheme for solving multi-objective optimization problems including fuzzy information in both objective functions and constraints is presented. At first, a multi-objective problem is converted into single objective problem based on the norm method, and a merbership function is constructed by selecting its type and providing the parameters defined by the norm method. Finally, this fuzzy programming problem is converted into an ordinary optimization problem which can be solved by usual nonlinear programming techniques. With this scheme, a designer can conveniently obtain pareto optimal solutions of a fuzzy system only by providing some parameters corresponding to the importance of the objectiv functions. Proposed scheme is simple and efficient in treating multi-objective fuzzy systems compared with and method by with membership function value is provided interactively. To show the validity of the scheme, a simple 3-bar truss example and optimal cutting problem are solved, and the results show that the scheme is very useful and easy to treat multi-objective fuzzy systems.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.1-8
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• 2006

Many researchers have been studied for the automatic chromosome karyotype classification and analysis. For the automatic classify the each chromosome which is the image in microscope, it is necessary to process the sub-procedure, ie. image pre-processing, implementing karyotype classifier. The image pre-processing proceeded the each chromosome separation, the noise exception and the feature parameter extraction. The extracted morphological feature parameter were the centromeric index(C.I.), the relative length ratio(R.L.), and the relative area ratio(R.A.). In this paper, the fuzzy classifier was implemented for the human chromosome karyotype classification. The extracted morphological feature parameter were used in the input parameter of fuzzy classifier. We studied about the selection of the membership function for the optimal fuzzy classifier in each chromosome groups.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.378-382
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• 2006

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.788-791
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• 2006

Fuzzy theory has been used effectively to control the nonlinear system since Mamdani successively adopted fuzzy theory in the steam-engine control problem in 1973. Truckbacker-upper control problem originally proposed by Nguyen and Widrow become a standard highly nonlinear control problem. In this paper, we designed adaptive fuzzy membership functions for speed control as well as steering control. In other words, an adaptive fuzzy control system for truck backer-upper problem useful for practical adaptation is proposed. Experimental results by simulations prove the effectiveness of the proposed system.

• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.37-54
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• 2004

Fuzzy theory provides an intelligence treatment model for judgement about information when it needs a solution or a decision making about vague problems. Therefore, fuzzy theory is used for appropriate evaluation and decision on obscure information as human's emotion in human factors, In previous study, fuzzy membership function is defined for judgement infOlmation as human's emotion then ultimate results are deducted through fuzzy inference model. This method uses general CWTent through literature review or max, min and average as representative statics value about considering variables. But, this method makes away with nonlinear's or inegular's factors of human sensibility. Accordingly, application of this method leads to considerable loss of information in the ultimate evaluation. For that reason, this method has a limitation in objective evaluation of human factors. So, this study focuses on development of fuzzy membership function, which evaluates human's emotion or feeling accurately and objectively. We used the regression analysis and reasoned a fuzzy membership function about the relation of the variables. Then we verified the adequacy with the reliability through the experiment after this.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.349-353
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• 2006

국내에서도 최근 이상기후 현상이 빈번하게 발생하고 있고, 이로 인해 매년 봄가뭄과 여름홍수가 반복적으로 발생하여 효율적 수자원 관리의 중요성이 더욱 강조되고 있다. 최적 저수지 운영을 통한 효율적인 수자원 이용으로 과다한 무효 방류와 같이 낭비되는 수자원을 절감시켜 신규 수자원 개발과 유사한 효과를 획득하고, 기존 시설에 의한 지역 용수의 안정적인 공급으로 신규 수자원 개발 억제에 의한 비용 절감의 필요성과 유역의 수자원 변화를 평가하기 위한 모형의 개발이 필요하다. 본 연구에서는 저수량 확보, 생활.농업.공업.하천유지용수 공급, 홍수조절, 수력발전 등의 다양한 목적들을 적절히 고려하고, 사용자의 요구에 따라 목적별 우선권을 변경할 수 있도록 적절한 membership 함수를 구축하여 fuzzy DP 모형을 개발하였다. 또한, 개발된 fuzzy DP 모형에 소양강 다목적댐의 기왕의 수문자료를 도입한 모형의 최적화 운영결과와 기왕의 실적자료를 비교 검토하여 최적화 운영의 우수성을 확인하였다. 본 연구의 결과는 향후 저수지의 효율적인 운영을 위한 지침으로 사용될 수 있을 것이며, 유역의 수자원 영향 평가에 활용할 수 있을 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.65-72
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• 2000

In this paper we propose the new binary image matching algorithm called the Fuzzy logic induced Hausdorff Distance(FHD) for finding the maximally matched image with the query image. The membership histogram is obtained by normalizing the cardinality of the subset with the corresponding radius after obtaining the distribution of the minimum distance computed by the Hausdroff distance between two binary images. in the proposed algorithm, The fuzzy influence method Center of Gravity(COG) is applied to calculate the best matching candidate in the membership function described above. The proposed algorithm shows the excellent results for the face image recognition when the noise is added to the query image as well as for the character recognition.