• Title/Summary/Keyword: FCM clustering

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Blind Channel Equalization Using Conditional Fuzzy C-Means

  • Han, Soo-Whan
    • Journal of Korea Multimedia Society
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    • v.14 no.8
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    • pp.965-980
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    • 2011
  • In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire (강인한 움직임 영역 검출과 화재의 효과적인 텍스처 특징을 이용한 화재 감지 방법)

  • Nguyen, Truc Kim Thi;Kang, Myeongsu;Kim, Cheol-Hong;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.6
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    • pp.21-28
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    • 2013
  • This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

Design of Robust Face Recognition System Realized with the Aid of Automatic Pose Estimation-based Classification and Preprocessing Networks Structure

  • Kim, Eun-Hu;Kim, Bong-Youn;Oh, Sung-Kwun;Kim, Jin-Yul
    • Journal of Electrical Engineering and Technology
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    • v.12 no.6
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    • pp.2388-2398
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    • 2017
  • In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

Design of RBFNN-based Emotional Lighting System Using RGBW LED (RGBW LED 이용한 RBFNN 기반 감성조명 시스템 설계)

  • Lim, Sung-Joon;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.5
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    • pp.696-704
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    • 2013
  • In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

Design of Optimized pRBFNNs-based Night Vision Face Recognition System Using PCA Algorithm (PCA알고리즘을 이용한 최적 pRBFNNs 기반 나이트비전 얼굴인식 시스템 설계)

  • Oh, Sung-Kwun;Jang, Byoung-Hee
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.1
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    • pp.225-231
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    • 2013
  • In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

A Novel Approach towards use of Adaptive Multiple Kernels in Interval Type-2 Possibilistic Fuzzy C-Means (적응적 Multiple Kernels을 이용한 Interval Type-2 Possibilistic Fuzzy C-Means 방법)

  • Joo, Won-Hee;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.529-535
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    • 2014
  • In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

Design of Face Recognition algorithm Using PCA&LDA combined for Data Pre-Processing and Polynomial-based RBF Neural Networks (PCA와 LDA를 결합한 데이터 전 처리와 다항식 기반 RBFNNs을 이용한 얼굴 인식 알고리즘 설계)

  • Oh, Sung-Kwun;Yoo, Sung-Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.5
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    • pp.744-752
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    • 2012
  • In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

The Characteristic of the Disasters caused by Typhoons passing through the Sea Area around the Korean Peninsula (한반도 주변 해역을 통과한 태풍의 재해특성)

  • Ahn, Suk-Hee;Choi, Ki-Seon;Kim, Baek-Jo;Shin, Seung-Sook
    • 한국방재학회:학술대회논문집
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    • 2008.02a
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    • pp.109-112
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    • 2008
  • The purpose of this study is to find out the characteristics of disasters caused by typhoons passing through the sea area around the Korean Peninsula. It analyzed two cases, that is, in WEST and EAST cases. These include the typhoons passing through the Yellow Sea, west of the Peninsula and East Sea, east of the Peninsula without landing on the Peninsula. FCM (Fuzzy Clustering Method) analysis was performed on typhoons affecting the Korean Peninsula from 1951 to 2006. The analysis shows that WEST case's cluster has the curved track of NE-S, and EAST case's cluster has the straight track of NE-SW. Typhoons that pass through the Yellow Sea have little change in frequency and the weak intensity. On the other hand, the frequency and the intensity of typhoons passing through the East Sea show the increasing trend. The characteristic of disasters by typhoons affecting the Korean Peninsula from 1973 to 2006 appears differently for each case: EAST cases caused significant damage in flooding, while WEST cases did damage in houses, ships, roads, and bridges. Rainfall amount and maximum wind speed data are analyzed in order to understand the impact of the typhoons, and the result indicates that the WEST cases are influenced by the wind, and East cases by precipitation. The result of this study indicates that the characteristic of disasters is distinctive according to the Typhoon's track. If applied to establish the disaster prevention plan, this result could make a contribution to the damage reduction.

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Design of Digits Recognition System Based on RBFNNs : A Comparative Study of Pre-processing Algorithms (방사형 기저함수 신경회로망 기반 숫자 인식 시스템의 설계 : 전처리 알고리즘을 이용한 인식성능의 비교연구)

  • Kim, Eun-Hu;Kim, Bong-Youn;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.2
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    • pp.416-424
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    • 2017
  • In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.

Design of pRBFNNs Pattern Classifier-based Face Recognition System Using 2-Directional 2-Dimensional PCA Algorithm ((2D)2PCA 알고리즘을 이용한 pRBFNNs 패턴분류기 기반 얼굴인식 시스템 설계)

  • Oh, Sung-Kwun;Jin, Yong-Tak
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.1
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    • pp.195-201
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    • 2014
  • In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.