• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.620-624
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• 2004

Th is paper presents a systematical approach to study carrier based PWM techniques (CPWM) in diode-clamped and cascade multilevel inverters by using the proposed multi-modulating pattern method. This method is based on the vector correlation between CPWM and space vector PWM (SVPWM) and applicable to both multilevel inverter topologies. The CPWM technique can be described in a general mathematical equation, and obtain the same outputs similarly as of corresponding SVPWM. Control of the fundamental voltage, vector redundancies and phase redundancies in multilevel inverter can be formulated separately in the CPWM equation. The deduced CPWM can obtain a full vector redundancy control, and fully utilize phase redundancy in a cascade inverter. In the paper, CPWM equations and corresponding algorithm for generating multi-modulating signals will be performed, in which SVPWM attributes will be presented by corresponding controllable factors.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.625-629
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• 2004

This paper presents a systematical approach to study carrier based PWM techniques (CPWM) in diode-clamped and cascade multilevel inverters by using a proposed named multi-modulating pattern method. This method is based on the vector correlation between CPWM and the space vector PWM (SVPWM) and applicable to both multilevel inverter topologies. A CPWM technique can be described in a general mathematical equation, and obtain the same outputs similarly as of the corresponding SVPWM. Control of the fundamental voltage, vector redundancies and phase redundancies in multilevel inverter can be formulated separately in the CPWM equation. The deduced CPWM can obtain the full vector redundancy control, and fully utilize phase redundancy in a cascade inverter In this continued part, it will be deduced correlation between CPWM equations in multi-carrier system and single carrier system, present the mathematical model of voltage source inverter related to the common mode voltage and propose a general algorithm for multi-modulating modulator. The obtained theory will be demonstrated by simulation results.

• Progress in Medical Physics
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• v.7 no.1
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• pp.53-64
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• 1996

Purpose: The purposes of this paper are to develop a theoretical basis that the beam directions should be considered when the mechanical accuracy of teletherapy machine is evaluated by the star pattern test, to develop methods using asymmetric field in length to simulate beam direction for the case that beam direction does not appear on film. Method: In evaluating mechanical rotational accuracy of the gantry of teletherapy unit by the star pattern test, the direction of radiation beams was considered. A star pattern using some narrow beams was made. Density profiles at 10cm far from estimated gantry axis on the star pattern were measured using an optical densitometer. On each profile, one coordimate of a beam axis was determined. A pair of coordinates on a beam axis form an equation of the axis. Assume that a unit vector equation omitted is with same direction as radiation beam and a vector equation omitted is a vector directing to the beam axis from the estimated gantry axis. Then, a vector product equation omitted ${\times}$ equation omitted is an area vector of which the absolute value is equal to the distance from the estimated gantry axis to the beam axis. The coordinate of gantry axis was obtained by using least-square method for the area vectors relative to the average of whole area vectors. For the axis, the maximum of absolute value of area vectors would be an accuracy of the gantry rotation axis. For the evaluation of mechanical accuracies of collimator and couch axes for which beam direction could not be depicted on a star pattern test film, narrow beams asymmetric in field length was used to simulate beam direction. Result: For a star test pattern to evaluate the mechanical accuracy of rotational axes of a telectherapy machine, the result considering beam direction was different from that ignoring beam direction. For the evaluation of mechanical accuracies of collimator and couch axes by means of a star pattern test, narrow asymmetric beams could simulate beam direction. Conclusion: When a star pattern test is used to evaluate the mechanical accuracy of a teletherapy unit, beam direction must be considered or simulated, and quantitatively evaluated.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1509-1518
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• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.983-986
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• 2002

Support Vector Machine (SVM) is one of the methods of pattern recognition that separate input data using hyperplane. This method has high capability of pattern recognition by using the technique, which says kernel trick, and the Radial basis function (RBF) kernel is usually used as a kernel function in kernel trick. In this paper we propose using the q-normal distribution to the kernel function, instead of conventional RBF, and compare two types of the kernel function.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.749-752
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• 2008

Various Motion Estimation (ME) algorithms have been proposed since ME requires large computational complexity. The proposed algorithm employs Enhanced Cross Search Pattern (ECSP) using motion vector of neighbor-blocks to search the motion vector. The experimental results show that proposed algorithm reduces the search point up to 35% compared to conventional methods.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.320-322
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• 2009

Support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. In this pater, we design the controller using support vector regression which has good properties in comparison with multi-layer perceptron or radial basis function. The applicability of the presented method is illustrated via an example simulation.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.357-359
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• 1995

In this paper, vector control of induction motor drives using space current vector PWM is presented. The scheme gives advantages, besides robustness to inverter arm-shoot, sinusoidal input current and voltage for induction motors. In addition, space vector PWM for CSI produces faster transient response than conventional pattern PWM. Also, a modulation index control is proposed.