• Journal of Power Electronics
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• v.17 no.3
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• pp.579-589
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• 2017

In this study a new high step-up dc-dc converter is presented. The operation of the proposed converter is based on the capacitor switching and coupled inductor with a single active power switch in its structure. A passive voltage clamp circuit with two capacitors and two diodes is used in the proposed converter for elevating the converter's voltage gain with the recovered energy of the leakage inductor, and for lowering the voltage stress on the power switch. A switch with a low $R_{DS}$ (on) can be adopted to reduce conduction losses. In the generalized mode of the proposed converter, to reach a desired voltage gain, capacitor stages with parallel charge and series discharge techniques are extended from both sides of secondary side of the coupled inductor. The proposed converter has the ability to alleviate the reverse recovery problem of diodes with circuit parameters. The operating principle and steady-states analyses are discussed in detail. A 40W prototype of the proposed converter is implemented in the laboratory to verify its operation.

• Journal of the Korean Mathematical Society
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• v.51 no.6
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• pp.1251-1267
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• 2014

The semicommutative property of rings was introduced initially by Bell, and has done important roles in noncommutative ring theory. This concept was generalized to one of nil-semicommutative by Chen. We first study some basic properties of nil-semicommutative rings. We next investigate the structure of Ore extensions when upper nilradicals are ${\sigma}$-rigid ${\delta}$-ideals, examining the nil-semicommutative ring property of Ore extensions and skew power series rings, where ${\sigma}$ is a ring endomorphism and ${\delta}$ is a ${\sigma}$-derivation.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.285-288
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• 1998

A least-squares identification method is studied that estimates a finite number of coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions, We will expand and generalize the orthogonal functions as basis functions for dynamical system representations. To this end, use is made of balanced realizations as inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. We show that the Laplace transform of the expansion for some sets$\Psi_{\kappa}(Z)$ is equivalent to a series expansion . Techniques based on this result are presented for obtaining the coefficients $c_{n}$ as those of a series. One of their important properties is that, if chosen properly, they can substantially increase the speed of convergence of the series expansion. This leads to accurate approximate models with only a few coefficients to be estimated. The set of Kautz functions is discussed in detail and, using the power-series equivalence, the truncation error is obtained.

• Korean Journal of Mathematics
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• v.30 no.3
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• pp.467-474
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• 2022

In this article, we consider the integral operator 𝓒^b,c, which is defined as follows: $${\mathcal{C}}^{b,c}(f)(z)={\displaystyle\smashmargin{2}{\int\nolimits_{0}}^z}{\frac{f(w)*F(1,1;c;w)}{w(1-w)^{b+1-c}}}dw,$$ where * denotes the Hadamard/ convolution product of power series, F(a, b; c; z) is the classical hypergeometric function with b, c > 0, b + 1 > c and f(0) = 0. We investigate the boundedness of the 𝓒^b,c operators on Bloch spaces.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.353-364
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• 1998

This paper presents a systematic approach to the modeling of power electronic circuits with systemlongrightarrowlevel simulation l languages. It is shown that a circuit model reduces to one of four basic types according to input/output conditions. The e elementary models for single series components and shunt components are derived which are integrated to develop a m model of given converter circuit. The constraints imposed on the model development-matching input/output conditions a and avoiding algebraic loop-are discussed in relation to the realization example of a buck converter circuit model. It is s shown that the constraints can always be fullfilled by introducing fictitious interface blocks, which is generalized to the c concept of model transformation.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.2
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• pp.102-108
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• 2017

Proper design guides are proposed for a practical dual-active bridge (DAB) converter based on the mathematical model on the steady state. The DAB converter is popular in bidirectional application due to its zero-voltage capability and easy bidirectional operation for seamless control, high efficiency, and performance. Some design considerations are taken to overcome the limitation of the DAB converter. The practical design methodology of power stage is discussed to minimize the conduction and switching losses of the DAB converter. Small-signal model and frequency response are derived and analyzed based on the generalized average method, which considers equivalent series resistance, to improve the dynamics, stability, and reliability with voltage regulation of the practical DAB converter. The design of closed-loop control is discussed by the derived small-signal model to obtain the pertinent gain and phase margin in steady-state operation. Experimental results of a 3.3 kW prototype of DAB converter demonstrate the validity and effectiveness of the proposed methods.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.475-483
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• 2009

In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.