• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.488-491
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• 2003

A motion compression method by min s-norm composite fuzzy relational equations (dual-MCF) is proposed, where a motion sequence is divided into intra-pictures (I-pictures) and predictive-pictures (P-pictures). The I-pictures and the P-pictures are compressed by using uniform coders and non-uniform coders, respectively. A design method of non-uniform coders is proposed to perform an efficient compression and reconstruction of the P-pictures, based on the dual overlap level of fuzzy sets and a fuzzy equalization. An experiment using 10 P-pictures confirms that the root means square errors of the proposed method is decreased to 82.9％ of that of the uniform coders, under the condition that the compression rate is 0.0055. An experiment of motion compression and reconstruction is also presented to confirm the effectiveness of the dual-MCF based on the non-uniform coders.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.407-412
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적함수(fitness function)와 가우시안 가중치가 적용된 멤버쉽 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 벡터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing (SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.462-470
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• 2008

This study applies fuzzy functions to improve image quality under the assumption that uncertainty of image information due to low contrast is based on vagueness and ambiguity of the brightness pixel values. To solve the problem of low contrast images whose brightness distribution is inclined, we use the k-means algorithm as a parameter of the fuzzy function, through which automatic critical points can be found to differentiate objects from background and contrast between bright and dark points can be improved. The fuzzy function is presented at the three main stages presented to improve image quality: fuzzification, contrast enhancement and defuzzification. To measure improved image quality, we present the fuzzy index and entropy index and in comparison with those of histogram equalization technique, it shows outstanding performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.13-20
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• 2005

This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.131-136
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• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as 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 problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. 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 three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of 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 the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.152-159
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• 2005

For high-speed data communication in band-limited channels, main of the bit error are fading and ISI(Inter-Symbol Interference). The common way of dealing with ISI is using equalization in the receiver. In this thesis, channel adaptive equalizer which uses Fuzzy Stochastic Gradient(FSG) and Constant Modulus Algorithm(CMA) is nonlinear equalizer, or Blind equalizer, that works directly on the signals with no training sequences required. This equalizer employs Takagi-Sugeno's fuzzy model that uses the FSG algorithm, to automatically regulate the step size of the descent gradient vector, combining fast convergence rate and low mean square error(MSE), and the CMA which is a special case of Godard's algorithm, to having multiple dispersion constants($R_p$).