• Title/Summary/Keyword: fuzzy-c means

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A Study on the Classification of Ports and its Characteristics using Fuzzy C-Means (FCM법에 의한 항만의 분류 및 그 특성 분석에 관한 연구)

  • 금종수;윤명오;양원재
    • Journal of Korean Port Research
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    • v.14 no.2
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    • pp.143-154
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    • 2000
  • In port management, the scale of facilities and port layouts are major factors characterizing the port, which influence port economics and productivities continuously through the port operation. Grouping ports in certain region by their characteristics could be used as the principal informations to establish national policy for port development or investment and also to analyze the competitiveness between ports. Currently Korean ports are divided into two groups such as the local port and the designated port containing foreign trade port and coastal port under the Korean port law. These divisions seem to be used for port administration as the matter of convenience but some qualitative grouping is needed for research of port problems. In this paper, 20 major Korean ports were clustered by the similar characteristics using Fuzzy C-Means and found to be classified 8 qualitative groups.

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Nonlinear Channel Equalization Using Adaptive Neuro-Fuzzy Fiter (적응 뉴로-퍼지 필터를 이용한 비선형 채널 등화)

  • 김승석;곽근창;김성수;전병석;유정웅
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.366-366
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    • 2000
  • In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

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Voice Activity Detection Algorithm using Fuzzy Membership Shifted C-means Clustering in Low SNR Environment (낮은 신호 대 잡음비 환경에서의 퍼지 소속도 천이 C-means 클러스터링을 이용한 음성구간 검출 알고리즘)

  • Lee, G.H.;Lee, Y.J.;Cho, J.H.;Kim, M.N.
    • Journal of Korea Multimedia Society
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    • v.17 no.3
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    • pp.312-323
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    • 2014
  • Voice activity detection is very important process that find voice activity from noisy speech signal for noise cancelling and speech enhancement. Over the past few years, many studies have been made on voice activity detection, it has poor performance for speech signal of sentence form in a low SNR environment. In this paper, it proposed new voice activity detection algorithm that has beginning VAD process using entropy and main VAD process using fuzzy membership shifted c-means clustering. We conduct an experiment in various SNR environment of white noise to evaluate performance of the proposed algorithm and confirmed good performance of the proposed algorithm.

Feature-Point Extraction by Dynamic Linking Model bas Wavelets and Fuzzy C-Means Clustering Algorithm (Gabor 웨이브렛과 FCM 군집화 알고리즘에 기반한 동적 연결모형에 의한 얼굴표정에서 특징점 추출)

  • 신영숙
    • Korean Journal of Cognitive Science
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    • v.14 no.1
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    • pp.11-16
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    • 2003
  • This Paper extracts the edge of main components of face with Gator wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

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Hybrid Fuzzy Association Structure for Robust Pet Dog Disease Information System

  • Kim, Kwang Baek;Song, Doo Heon;Jun Park, Hyun
    • Journal of information and communication convergence engineering
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    • v.19 no.4
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    • pp.234-240
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    • 2021
  • As the number of pet dog-related businesses is rising rapidly, there is an increasing need for reliable pet dog health information systems for casual pet owners, especially those caring for older dogs. Our goal is to implement a mobile pre-diagnosis system that can provide a first-hand pre-diagnosis and an appropriate coping strategy when the pet owner observes abnormal symptoms. Our previous attempt, which is based on the fuzzy C-means family in inference, performs well when only relevant symptoms are provided for the query, but this assumption is not realistic. Thus, in this paper, we propose a hybrid inference structure that combines fuzzy association memory and a double-layered fuzzy C-means algorithm to infer the probable disease with robustness, even when noisy symptoms are present in the query provided by the user. In the experiment, it is verified that our proposed system is more robust when noisy (irrelevant) input symptoms are provided and the inferred results (probable diseases) are more cohesive than those generated by the single-phase fuzzy C-means inference engine.

Analysis of Saccharomyces Cell Cycle Expression Data using Bayesian Validation of Fuzzy Clustering (퍼지 클러스터링의 베이지안 검증 방법을 이용한 발아효모 세포주기 발현 데이타의 분석)

  • Yoo Si-Ho;Won Hong-Hee;Cho Sung-Bae
    • Journal of KIISE:Software and Applications
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    • v.31 no.12
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    • pp.1591-1601
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    • 2004
  • Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.

An Enhanced Spatial Fuzzy C-Means Algorithm for Image Segmentation (영상 분할을 위한 개선된 공간적 퍼지 클러스터링 알고리즘)

  • Truong, Tung X.;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.2
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    • pp.49-57
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    • 2012
  • Conventional fuzzy c-means (FCM) algorithms have achieved a good clustering performance. However, they do not fully utilize the spatial information in the image and this results in lower clustering performance for images that have low contrast, vague boundaries, and noises. To overcome this issue, we propose an enhanced spatial fuzzy c-means (ESFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors in a $3{\times}3$ square window. To evaluate between the proposed ESFCM and various FCM based segmentation algorithms, we utilized clustering validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), and Xie-Bdni function ($V_{xb}$). Experimental results show that the proposed ESFCM outperforms other FCM based algorithms in terms of clustering validity functions.

A Study on Labeling Algorithm of ECG Signal using Fuzzy Clustering (퍼지 클러스터링을 이용한 심전도 신호의 구분 알고리즘에 관한 연구)

  • Kong, In-Wook;Kweon, Hyuk-Je;Lee, Jeong-Whan;Lee, Myoung-Ho
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.4
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    • pp.427-436
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    • 1999
  • This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.

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Defect Extraction of Ceramic Image using Fuzzy Clustering Based Enhanced Fuzzy Binarization (퍼지 클러스터링 기반 개선된 Fuzzy Binarization 기법을 이용한 세라믹 영상에서의 결함 추출)

  • Choi, Cheol Ho;Lee, Jin Yu;Park, Heon Sung;Kim, Kwang Baek
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.01a
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    • pp.23-26
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    • 2019
  • 본 논문에서는 X-Ray 영상에서 용접한 부분의 기공이나 균열 등의 결함 영역을 추출하는 새로운 방법을 제안한다. 제안된 방법은 세라믹 X-Ray 영상에서 비등방성 확산 필터를 적용하여 영상의 잡음을 제거하고, 수직 및 수평 히스토그램을 각각 적용하여 용접 영역을 추출한 후, 최소 자승법을 적용하여 배경 밝기를 제거하고, 사다리꼴 형태의 Fuzzy Stretching기법을 적용하여 명암 값을 강조하여 결함 영역과 그 외의 영역간의 명암 대비를 강조한다. 그리고 Fuzzy C_Means 알고리즘을 적용하여 결함 영역을 세분화한 후, Fuzzy C_Means을 적용하여 생성된 클러스터들의 중심 명암 값을 이용하여 ${\alpha}_-cut$을 설정한 후에 임계구간을 구하고 영상을 이진화하여 최종적으로 결함 영역을 추출한다. 제안된 방법의 결함 추출 성능을 확인하기 위하여 세라믹 X-Ray 영상을 대상으로 실험한 결과, 기존의 방법보다 결함 영역이 정확히 추출되는 것을 확인할 수 있었다.

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Optimization of Fuzzy Set Fuzzy Model by Means of Hierarchical Fair Competition-based Genetic Algorithm using UNDX operator (UNDX연산자를 이용한 계층적 공정 경쟁 유전자 알고리즘을 이용한 퍼지집합 퍼지 모델의 최적화)

  • Kim, Gil-Sung;Choi, Jeoung-Nae;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2007.04a
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    • pp.204-206
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    • 2007
  • In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

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