• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.8
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• pp.98-104
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• 2013

AC machines are in wide use in industry and d-q transformation from 3 phase of a, b, c is commonly used to analyze these kinds of machines. The equivalent circuits of d and q axis are, however, generally cross coupled and difficult to analyze. In this study, a modeling technique of AC machine including induction and PM synchronous motors using matrix vector is proposed. With that model, it can not only explain the AC machines physically but also make it simple to analyze them. The separating process of d and q components is not needed in this model and this model can be applied to analyze asymmetric motors like IPMSM machine. With this technique, the model becomes simple, easy to understand physically, and yields results that are the same as those from other models. These simulation results of the proposed model for induction motor are compared with those of other models to verify the method proposed.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1093-1101
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• 2009

Quantile regression has become a more widely used technique to describe the distribution of a response variable given a set of explanatory variables. This paper proposes a novel modelfor quantile regression using doubly penalized kernel machine with support vector machine iteratively reweighted least squares (SVM-IRWLS). To make inference about the shape of a population distribution, the widely popularregression, would be inadequate, if the distribution is not approximately Gaussian. We present a likelihood-based approach to the estimation of the regression quantiles that uses the asymmetric Laplace density.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.107-115
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• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1596-1603
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• 2018

This paper presents the new design and validation process of the linear transverse flux machine of the stelzer machine for hybrid vehicle application. A linear transverse flux machine is a novel electric machine that has higher force density and power than conventional electric machine. The process concentrates on 2-dimensional and 3-dimensional analysis using equivalent magnetic circuit method considering leakage elements and it is verified by finite element analysis. Besides the force characteristics of all axis of each direction are analyzed. The study is considered by dividing the transverse flux electric excited type and the transverse flux permanent magnet excited type. Additionally three-dimensional analysis in this machine is accomplished due to asymmetric structure with another three axes. Finally, it suggests the new design and validation process of linear transverse flux machine for stelzer machine.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.165-170
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• 2011

Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1537-1545
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• 2015

Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.71-82
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• 2022

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

• Journal of IKEEE
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• v.26 no.2
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• pp.265-270
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• 2022

This paper proposes the detection algorithm for switch open fault of asymmetric 6-phase PMSM based on stationary reference frame dq-axis currents. In this paper, target motor has an asymmetric structure in which two upper three windings have an electrical phase difference of 30° and a neutral point is separated. As a result, dual 3-phase PWM inverters and the detection techniques due to open failures of switch are definitely required. In this paper, the dual dq-axis current control method is used for driving the asymmetric 6-phase PMSM and the open fault switch should be detected by using variable all pass filter and low pass filter in order to detect the current amplitude. The effectiveness and usefulness of the proposed method is verified by several experiments.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.31-38
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• 2021

The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R² value of the artificial neural network and random forest algorithms is observed to be more than 0.9.