• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.289-297
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• 2007

Color image scale captures the knowledge of colorists and represents both adjectives and colors in the same adjective image scales in order to select color(s) corresponding to an adjective. Due to the difficulty of psychological experiment and statistical analysis, in general, only a limited number of colors are located in the color image scales. This can make color selection process hard especially to non-expert. In this paper, we propose an interpolation of color image scale based on the fuzzy K-nearest neighbor method, which provides continuous colors according to the coordinates of the image scales. The experimental results show that the interpolated image scales can be practically useful for color selection process.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.112-123
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• 1994

In this paper, inspection and subpixed alignment algorithms of SMD's (Surface Mounting Devices) using fuzzy morphology are proposed. First, camera calibration is performed and then the inspection algorithm detects defects such as lead bending and breaking using the ruler generated by fuzy morphology. The SMD having no defects is tested whether it is mounted in the specified position or not. The proposed subpixel alignment algorithm detects accurately orientation and position using subpixel interpolation. It consists of two parts: preprocessing and main processing steps, in which corner points and coarse orientation of a SMD are detected, and interpolation is used to obtain final parameters with wubpixel accuracy. The computer simulation shows that the proposed algorithms give more accurate parameters, and they can be applied to fast and accurate automatic surface mounting systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.568-572
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• 1998

This paper defines Fuzzy Logic Units(FLUs) which are piece wise finite elements in multidimension Euclidean space, and redefines triangular membership functions which are different from those defined in traditional literature. By analyzing FLUs, this paper gives a constructive method of fuzzy rules in fuzzy logic systems based on finite element method. The simulation results of single machine to infinite bus system show the effectiveness of the proposed method in this paper.

• Journal of Mechanical Science and Technology
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• v.14 no.11
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.286-290
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• 2005

We consider the poisson equation where the functions involved are periodic including the solution function. Let $R=[0,1]{\times}[0,l]{\times}[0,1]$ be the region of interest and let $\phi$(x,y,z) be an arbitrary periodic function defined in the region R such that $\phi$(x,y,z) satisfies $\phi$(x+1, y, z)=$\phi$(x, y+1, z)=$\phi$(x, y, z+1)=$\phi$(x,y,z) for all x,y,z. We describe a very simple method for solving the equation ${\nabla}^2u(x, y, z)$ = $\phi$(x, y, z) based on the cubic spline interpolation of u(x, y, z); using the requirement that each interval [0,1] is a multiple of the period in the corresponding coordinates, the Laplacian operator applied to the cubic spline interpolation of u(x, y, z) can be replaced by a square matrix. The solution can then be computed simply by multiplying $\phi$(x, y, z) by the inverse of this matrix. A description on how the storage of nearly a Giga byte for $20{\times}20{\times}20$ nodes, equivalent to a $8000{\times}8000$ matrix is handled by using the fuzzy rule table method and a description on how the shape preserving property of the Laplacian operator will be affected by this approximation are included.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.569-573
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• 2001

An approximate representation of discrete functions {f$_{i,j}\mid$｜i, j=-1, 0, 1, …, N+1}in two variables by a fuzzy system is described. We use the cubic B-splines as fuzzy sets for the input fuzzification and spike functions as the output fuzzy sets. The ordinal number of f$_{i,j}$ in the sorted list is taken to be the out put fuzzy set number in the (i, j) th entry of the fuzzy rule table. We show that the fuzzy system is an exact representation of the cubic spline function s(x, y)=$\sum_{N+1}^{{i,j}=-1}f_{i,j}B_i(x)B_j(y)$ and that the approximation error S(x, y)-f(x, y) is surprisingly O($h^2$) when f(x, y) is three times continuously differentiable. We prove that when f(x, y) is a gray scale image, then the fuzzy system is a smoothed representation of the image and the original image can be recovered exactly from its fuzzy system representation when it is a digitized image.e.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.4.2-4
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• 2003

In this paper, we describe how to approximate the solutions of partial differential equations by bicubic spline functions whose interpolation errors at non-grid points are smaller in general than those by linear interpolations of the original solution at grid points. We show that the bicubic spline function can be represented exactly or approximately by a fuzzy system, and that the resulting fuzzy rule table shows the contours of the solution function. Thus, the fuzzy rule table is identified as a digital image and the contours in the rule table provide approximate contours of the solution of partial differential equations. Several illustrative examples are included.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.