• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.30-40
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• 1997

This paper presents a design method of fuzzy control systems by clustering the data in the subspace of the input-output produyct space. In the case of servo control, most input-outputdata are concentrated in thye steady-state region, and the the clustering will result in only steady-state fuzzy rules. To overcome this problem, we divide the input-output product space into some subspaces according to the state of input variables. The fuzzy control system designed by the subspace clustering showed good transient response and smaller steady-state error, which is comparable with the reference fuzzy system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.35-44
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• 1997

In this paper, a an algorithm that detects the endocardial boundary, expanding the region from endocardial cavity using fuzzy inference, is proposed. This algorithm decides the ventricular cavity by fuzzy inference in process of searching each pixel from the inside of left ventricle in echocardial image and expands it. Uncertainty and fuzziness exists in decision of endocardial boundary. Therefore, we convert the lingustic representation of mean, standard deviation, and threshold value that are characteristic variables of endocardial boundary to fuzzy input and output variables. And, we extract proposed method is robuster to noise than radial searching method that is highly dependent on center position. To prove the similarity of detected boundary by fuzzy nference, we used the measures of SIZE, correlation coefficient, MSD, and RMSE and had acquired reasonable results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.176-179
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• 2000

This paper addresses a fuzzy controller for nonlinear systems control using a pole placement in a specified disk and fuzzy controller is redesign for Intelligent digital redesign method. for nonlinear system, we obtain continuous time state feedback gain that guarantee stability of globally TS fuzzy system. The feedback gain is satified pole placement in a specified disk region so that the closed loop system is stable, For digital control redesgin of continuous time TS fuzzy model, we does state matching and obtain feedback gain of digital controller. Finally, it is shown that the proposed method is feasible through a computer simulation.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.204-206
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• 2008

In this paper, we proposed the recognition method that automatically extracts cracks from a surface image acquired by a digital camera and recognizes the directions (horizontal, vertical, -45 degree, and 45 degree) of cracks using the fuzzy single layer perceptron. We compensate an effect of light on a concrete surface image by applying the closing operation, which is one of the morphological techniques, extract the edges of cracks by Sobel masking, and binarize the image by applying the iterated binarization technique. Two times of noise reduction are applied to the binary image for effective noise elimination. After the specific regions of cracks are automatically extracted from the preprocessed image by applying Glassfire labeling algorithm to the extracted crack image, the cracks of the specific region are enlarged or reduced to $30{\times}30$ pixels and then used as input patterns to the fuzzy single layer perceptron. The experiments using concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the fuzzy single layer perceptron was effective in the recognition of the extracted cracks directions.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.781-784
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAx clusterng algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action. But current segmentation methods based edge extraction methods generally need the mask information for the algebraic model, and take long run times at mask operation, wheras the proposed algorithm has single operation ccording to active searching of fuzzy worms. In addition, we also genetic min-max clustering using genetic algorithm to complete clustering and fuzyz searching on grey-histogram of image for the optimum solution, which can automatically determine the size of rnages and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2302-2304
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• 2000

This paper addresses a fuzzy controller for nonlinear systems control using a pole placement in a specified disk. In the method, we linearize a nonlinear plant about nominal operating points and represent it using TS fuzzy model and formulate the controller rules. A feedback control law for a local model is determined using a pole placement in a specified disk(${\alpha}$:center ${\gamma}$:radius} region so that the closed loop system is stable. A nonlinear system can be controlled by combining fuzzy controller with a pole placement scheme which can be used to modify the transient response such as damping ratio and overshoot. A stability of overall fuzzy control system is guaranteed in the Lyapunov sense. Finally, it is shown that the proposed method is feasible through a computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1222-1225
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• 1993

The purpose of this paper is to construct a prediction system on the chance of rain in a local region using a fuzzy relational model. The prediction system consists of two parts. One is a prediction part on the chance of rain. The compositional law of fuzzy inference, proposed by Zadeh, is applied to predict the chance of rain. The other is a learning part of a fuzzy relational model using input-output data. A simple and fast learning algorithm is used in this part. Simulations are carried out by the actual weather data in our city and their results show the validity of prediction by the fuzzy relational approach.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.72-78
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• 1997

This paper presents a fuzzy relaxation algorithm, which is based on the possibility and FAM instead of he probability and compatibility coefficients used in most of existing probabilistic relaxation algorithms, Because of eliminating stages for estimating of compatibility coefficients and normalization of the probability estimates, the proposed fuzzy relaxation algorithms increases the parallelism and has a simple iteration scheme. The construction of fuzzy relaxation scheme consists of the following three tasks: (1) definition of in/output linguistic variables, their term sets, and possibility. (2) Definition of FAM rule bases for relaxation using fuzzy compound relations. (3) Construction of the iteration scheme for calculating the new possibility estimate. Applications to region segmentation an ege detectiojn algorithms show that he proposed method can be used for not only reducing the image ambiguity and segmentation errors, but also enhancing the raw edge iteratively.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.417-423
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• 2005

A fuzzy approach using an EM algorithm for image classification is presented. In this study, a double compound stochastic image process is assumed to combine a discrete-valued field for region-class processes and a continuous random field for observed intensity processes. The Markov random field is employed to characterize the geophysical connectedness of a digital image structure. The fuzzy classification is an EM iterative approach based on mixture probability distribution. Under the assumption of the double compound process, given an initial class map, this approach iteratively computes the fuzzy membership vectors in the E-step and the estimates of class-related parameters in the M-step. In the experiments with remotely sensed data, the MRF-based method yielded a spatially smooth class-map with more distinctive configuration of the classes than the non-MRF approach.