• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.3
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1015-1019
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• 2001

This paper presents a novel three-phase current-fed Pulse Width Modulation converter with switched capacitor type resonant DC link commutation circuit operating PWM pattern strategy under a design consideration of low-pass filter, which can operate on the basis of the principle of zero current soft switching commutation. In the first place, the steady state operating principle of this converter with a new resonant DC link snubber circuit is described in connection with the equivalent operation circuit, together with the practical design procedure of the switched-capacitor type resonant DC link circuit is discussed from a theoretical viewpoint on the basis of a design example for high-power applications. The actively delayed time correction method to compensate distorted currents due to a relatively long resonant commutation time is newly implemented in the open loop control scheme so as to acquire the new optimum PWM pattern. Finally, the experiment of set-up in laboratory system of this converter is concretely demonstrated herein to confirm a zero current soft-switching commutation of this converter. The comparative evaluations between current-fed hard switching PWM and soft-switching PWM converters are carried out from a viewpoint of their PWM converter characteristics.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.333-339
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• 2013

This paper discusses a method for getting a basis set of estimable functions of less than full rank linear model. Since model parameters are not estimable estimable functions should be identified for making inferences proper about them. So, it suggests a method of using full rank factorization of model matrix to find estimable functions in easy way. Although they might be obtained in many different ways of using model matrix, the suggested full rank factorization technique could be one of much easier methods. It also discusses how to use projection matrix to identify estimable functions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.8
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• pp.340-348
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• 2003

In this paper, we propose a novel formulation to solve a time-domain combined field integral equation (CFIE) for analyzing the transient electromagnetic scattering response from closed conducting bodies. Instead of the conventional marching-on in time (MOT) technique, tile solution method in this paper is based on the moment method that involves separate spatial and temporal testing procedures. Triangular patch vector functions are used for spatial expansion and testing functions for three-dimensional arbitrarily shaped closed structures. The time-domain unknown coefficient is approximated as a basis function set that is derived from tile Laguerre functions with exponentially decaying functions. These basis functions are also used as the temporal testing. Numerical results computed by the proposed method arc stable without late-time oscillations and agree well with the frequency-domain CFIE solutions.

• Journal of the Korean Mathematical Society
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• v.41 no.6
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• pp.945-955
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• 2004

In this paper, we establish a number system in $R^n$ which arises from a Haar wavelet basis in connection with decompositions of certain Cuntz algebra representations on $L^2$( $R^n$). Number systems in $R^n$ are also of independent interest [9]. We study radix-representations of $\chi$ $\in$ $R^n$: $\chi$:$\alpha$$_{ι}$ $\alpha$$_{ι-1}$…$\alpha$$_1$$\alpha$$_{0}$ ㆍ$\alpha$$_{-1}$ $\alpha$$_{-2}$ … as $\chi$= $M^{ι}$$\alpha$$_{ι}$ $\alpha$＋…M$\alpha$$_1$＋$\alpha$$_{0}$ ＋ $M^{-1}$ $\alpha$$_{-1}$ ＋ $M^{-2}$ $\alpha$$_{-2}$ ＋… where each $\alpha$$_{k}$ $\in$ D, and D is some specified digit set. Our analysis uses iteration techniques of a number-theoretic flavor. The view-point is a dual one which we term fractals in the large vs. fractals in the small,illustrating the number theory of integral lattice points vs. fractions.s vs. fractions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1150-1156
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• 2009

In this paper, we propose the gradual encryption method of image using LFSR(Linear Feedback Shift Register) and 2D CAT(Two-Dimensional Cellular Automata Transform). First, an LFSR is used to create a PN(pseudo noise) sequence, which is identical to the size of the original image. Then the created sequence goes through an XOR operation with the original image resulting to the first encrypted image. Next, the gateway value is set to produce a 2D CAT basis function.The created basis function multiplied with the first encrypted image produces the 2D CAT encrypted image which is the final output. Lastly, the stability analysis verifies that the proposed method holds a high encryption quality status.

• Journal of the Korean Institute of Rural Architecture
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• v.12 no.2
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• pp.101-108
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• 2010

In this study, designated as scenic in the heart of Darangyi rice field in village of Garchon architectural status to preserve the rural town of quantitative analysis of the characteristics village of Garchon traditional rural villages to build the basis for landscape management are the goal. Most of the buildings housing the seting village of Garchon flavor appears in the form of the natural terrain accept the wall which is exposed on the outside of the roof, wall materials and colors, such as the town without a regular basis to undermine the image of the landscape and so Darangyi rice field standard set for maintenance of landscapes that are needed. Regional officer of the private property of individuals, but local and national recognition of the shared property, and only when done in this regard the establishment of asset and as a scenic area that has to cherish the history and culture between the people who live where the feedback through the exchange of sensitive areas and to the people who live in it will be an alternative to a variety of ways.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.11-25
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• 2001

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.