• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1093-1102
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• 2010

Principal component analysis (PCA) is a well-known method for dimension reduction while maintaining most of the variation in data. Although PCA has been applied to many areas successfully, it is sensitive to outliers. Several variants of PCA have been proposed to resolve the problem and, among the variants, robust fuzzy PCA (RF-PCA) demonstrated promising results. RF-PCA uses fuzzy memberships to reduce the noise sensitivity. However, there are also problems in RF-PCA and the convergence property is one of them. RF-PCA uses two different objective functions to update memberships and principal components, which is the main reason of the lack of convergence property. The difference between two functions also slows the convergence and deteriorates the solutions of RF-PCA. In this paper, a variant of RF-PCA, called RF-PCA2, is proposed. RF-PCA2 uses an integrated objective function both for memberships and principal components. By using alternating optimization, RF-PCA2 is guaranteed to converge on a local optimum. Furthermore, RF-PCA2 converges faster than RF-PCA and the solutions found are more similar to the desired solutions than those of RF-PCA. Experimental results also support this.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.125-132
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• 2007

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 eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.527-530
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• 2001

영상이 시각적인 해석을 위해 처리될 때, 퍼지 이론이 영상 대비 강화에 많이 사용되고 있다. 적응적 퍼지 클러스터링 기법을 사용하여 자동적으로 영상의 명암도에 대한 다중 클래스를 형성하고 여기에 각각의 명암도를 속성 공간으로 전환시키는 퍼지함수를 사용하여 각 픽셀의 명암도에 부합하는 퍼지 소속도를 구한다. 영상 대비 향상을 위하여 구한 퍼지 소속도에 강화 연산자를 반복적 적용한다. 본 논문에서 제안한 방법을 히스토그램 평활화와 비교하기 위해 흑백 영상에 적용하였다.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.25-31
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• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself-the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.373-378
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• 2001

The optimization method based on an enhanced genetic algorithms is for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is a global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by single point method in reconstructive search space. Four numerical examples are also presented in this papers to comparing with conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.853-862
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• 2003

With the data base subject of first grade paper test of information handling technician, We proposed special method of evaluating learning ability directivity to judge that student can understand the contents of each chapter exactly or not, using assigned function and fuzzy deduction in this thesis. Using fuzzy logic, the proposed method of evaluating learning ability is dividing the presenting frequency of setting questions for examination about the subject of database into three rank and we can define this as the important. We applied the fuzzy assigned rate about the number of times of studying through the important of studying and the fuzzy assigned rate about formative evaluation to each of nine fuzzy deduction theories and than evaluated comprehension rate of learning. With the fuzzy grade about learning comprehension of each chapter and assigned rate about the score of generalized evaluation; We applied these two thing to the deduction rule of fuzzy and made it as defuzzifier and finally evaluated learning. We made that the result of eventual evaluating learning is very useful for learners to diagnosis learned contents by themselves and also it can be great material to judge that learners can get the goal of learning or not synthetically.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.1-9
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• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.110-113
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• 2005

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted fuzzy Membership Funcstions, NEWFM)을 이용하여 심전도 신호로부터 조기심실수축(Premature Ventricular Contraction, PVC)을 판별하는 퍼지규칙을 추출하고 있다. NEWFM은 자기적응적(self adaptive) 가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이를 기반으로 정상 파형과 PVC 파형을 구분한다. 분류 성능 평가를 위하여 MIT/BIH 부정맥 데이터 베이스를 사용하였으며, NEWFM의 입력은 심전도의 파형에 웨이블릿 변환을 적용하여 추출된 웨이블릿 계수를 사용하였다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 계수를 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 추출된 2개의 웨이블릿 계수를 사용한 퍼지규칙은 $96\%$의 PVC 분류성능을 보여준다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.35-38
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• 2000

본 논문에서는 퍼지 제어기의 설계를 위한 다중 돌연변이 연산자를 갖는 Niche Meta 유전 알고리즘을 제안한다. 제안된 알고리즘에서 유전자는 유전 알고리즘에 사용되는 교배율이나 돌연변이율과 같은 구조 매개변수와 퍼지 제어기의 입, 출력 소속함수를 나타내는 매개변수로 구성된다. 제안된 알고리즘은 부개체군들에 대해 퍼지 제어기의 소속함수의 매개변수를 최적화시키는 지역적 탐색을 수행하면서 전체 개체군에 대해서 최적의 구조 매개변수에 대한 전역적인 탐색을 수행한다. 다중 돌연변이 연산자는 지역적 진화의 결과에 따라 진화에 가장 적합한 돌연변이 방법으로 선택된다. 제안된 알고리즘의 효율성을 입증하기 위해 2 자유도를 구륜이동 로봇에 대한 모의 실험을 수행한다.