• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.19-24
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• 2010

The number of automobiles are continuously increasing in Korea since 1990's and it causes frustrating commuting traffic and holyday traffic. Meanwhile, the obsolete traffic signal control system is still under static control based on the aggregated traffic statistics thus it is not sufficiently adaptive in real world traffic situation that changes in real time. Thus, in this paper, we propose an adaptive signal control system using fuzzy control technology that can react to real time traffic situations. The method computes the priority of signal phases based on the number of waiting automobiles and occupying time on intersection using fuzzy membership functions. The phase with highest priority obtains "proceed" signal. Also, the duration of this "proceed" signal is determined based on the ratio of number of waiting automobiles of given phase and total number of waiting automobiles on intersection. In experiment, we show that the proposed fuzzy control system is better than the static control system for all sorts of traffic congestion situations by simulation.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.501-512
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• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.209-216
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• 2005

The controller that can control the smart base isolation system consisting of M damper and friction pendulum systems(FPS) is developed in this study. A fuzzy logic controller (FLC) is used to modulate the M damper force because the FLC has an inherent robustness and ability to handle non-linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. When earthquake excitations are applied to the structures equipped with smart base isolation system, the relative displacement at the isolation level as well as the acceleration of the structure should be regulated under appropriate level. Thus, NSGA-II(Non-dominated Sorting Genetic Algorithm) is employed in this study as a multi-objective genetic algorithm to meet more than two control objectives, simultaneously. NSGA-II is used to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can efficiently find Pareto optimal sets that can reduce both structural acceleration and base drift from numerical studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1457-1464
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• 2011

In this paper, we go one step further in that the number of competitive layers is not determined by experience but can be determined by fuzzy control rules based on input pattern information. In our method, we design a set of membership functions and corresponding rules and used Max-Min reasoning proposed by Mamdani. Also, we use centroid method as a defuzzification. In experiment that has various patterns of English inputs, this new method works beautifully to determine the number of competitive layers and also efficient in overall accuracy as a result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.773-780
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• 2009

This study proposed a face region detection algorithm using fuzzy inference of pixel hue and intensity. The proposed algorithm is composed of light compensate and face detection. The light compensation process performs calibration for the change of light. The face detection process evaluates similarity by generating membership functions using as feature parameters hue and intensity calculated from 20 skin color models. From the extracted face region candidate, the eyes were detected with element C of color model CMY, and the mouth was detected with element Q of color model YIQ, the face region was detected based on the knowledge of an ordinary face. The result of experiment are conducted with frontal face color images of face as input images, the method detected the face region regardless of the position and size of face images.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2891-2893
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• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.181-190
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• 2003

This paper presents the design of a fuzzy power system stabilizer (FPSS) using an adaptive evolutionary algorithm (AEA). AEA consists of genetic algorithm (GA) for a global search capability and evolution strategy (ES) for a local search in an adaptive manner when the present generation evolves into the next generation. AEA is used to optimize the membership functions and scaling factors of the FPSS. To evaluate the usefulness of the FPSS, we applied it to a single-machine infinite bus system (SIBS) and a power system simulator at the Korea Electrotechnology Research Institute. The FPSS displays better control performance than the conventional power system stabilizer (CPSS) for a three-phase fault in heavy load, which is used when tuning FPSS. To show the robustness of the FPSS, it is applied with disturbances such as change of mechanical torque and three-phase fault in nominal and heavy load, etc. The FPSS also demonstrates better robustness than the CPSS. Experimental results indicate that the FPSS has good system damping under various disturbances such as one-line to ground faults, line parameter changes, transformer tap changes, etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.