• Journal of IKEEE
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• v.24 no.4
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• pp.954-960
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• 2020

This paper proposes a speed variable proportional resonant current control method for a single-phase permanent magnet synchronous motor(PMSM). Due to the electromagnetic characteristics of a single-phase PMSM, negative and zero torques are generated in the part corresponding to the phase difference between the stator current and the back electromotive force. In addition, overcurrent limitation is required because of the low stator resistance and inductance in sensorless operation. When using the vector control for current control of single-phase PMSM under these conditions, processes of coordinate transformation, inverse coordinate transformation, and generation of virtual dq-axis components are required. However, the proposed variable speed proportional resonant current control method does not need the coordinate transformation used for AC motors. In this paper, we have confirmed stable maneuverability by using variable proportional resonant current control algorithm, and proposed sensorless control based on a mathematical model of a single-phase PMSM without a position sensor when reaching a constant speed. The usefulness of the current control method was verified through several experiments.

• Wind and Structures
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• v.32 no.1
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• pp.11-17
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• 2021

In recent years, there has been a tendency towards renewable energy sources considering the damages caused by non-renewable energy resources to nature and humans. One of the renewable energy sources is wind and energy is obtained with the help of wind turbines. To determine the behavior of wind turbines under earthquake loads, dynamic characteristics are required. In this study, the differential transformation method is proposed to determine the free vibration analysis of wind turbines with a variable cross-section. The wind turbine is modeled as an equivalent variable continuous flexural beam and blade weight is considered as a point mass at the top of the structures. The differential equation representing the free vibration of the wind turbine is transformed into an algebraic equation with the help of differential transformation method and the angular frequencies and the mode shapes of the wind turbine are obtained by the help of the differential transformation method. In the study, a sample taken from the literature was solved with the presented method and the suitability of the method was investigated. The same wind turbine example also modeled by finite element modelling software, ABAQUS. Results of the finite element model and differential transformation method are compared with each other and the results are in good agreement.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.52.2-52
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• 2002

In this paper we consider an approach to a formal linearization for time-variant nonlinear systems. A time-variant nonlinear sysetm is assumed to be described by a time-variant nonlinear differential equation. For this system, we introduce a coordinate transformation function which is composed of the Chebyshev polynomials. Using Chebyshev expansion to the state variable and Laguerre expansion to the time variable, the time-variant nonlinear sysetm is transformed into the time-variant linear one with respect to the above transformation function. As an application, we synthesize a time-variant nonlinear observer. Numerical experiments are included to demonstrate the validity of...

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.201-213
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• 2003

In this paper we present a second order PVT (parallel variable transformation) algorithm converging to second order stationary points for minimizing smooth functions, based on the first order PVT algorithm due to Fukushima (1998). The corresponding stopping criterion, descent condition and descent step for the second order PVT algorithm are given.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.967-976
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• 2011

The Box-Cox transformation is a well known family of power transformations that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.

• ETRI Journal
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• v.35 no.4
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• pp.714-717
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• 2013

Peak power reduction techniques in orthogonal frequency division multiplexing (OFDM) has been an important subject for many researchers for over 20 years. In this letter, we propose a side-information-free technique that is based on the concept of random variable (RV) transformation. The suggested method transforms RVs into other RVs, aiming to reshape the constellation that will consequently produce OFDM symbols with a reduced peak-to-average power ratio. The proposed method has no limitation on the mapping type or the mapping order and has no significant effect on the bit error rate performance compared to other methods presented in the literature. Additionally, the computational complexity does not increase.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1222-1229
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• 2007

In this paper, we propose a new obstacle negotiation method for the rescue robot. The rescue robot has a variable geometry single-tracked mechanism, so it can maximize a contact length with ground for the adaptability to off-road and pursue a stable system due to the lower center of gravity. In this research, we add the basis of autonomous navigation, driving mode control based on obstacle detection, to the robot to realize automation of mode transformation. Obstacle detection using PSD(Position Sensitive Device) infrared sensors gives active transformation of the track shape. Finally, experimental results about mentioned are presented.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.599-609
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• 1999

Neural networks are explored as an alternative to a regres-sion model for prediction of the number of daily household vehicular trips. This study focuses on contrasting a neural network model with a regression model in term of variable selection as well as the appli-cation of these models for prediction of extreme observations, The differences in the models regarding data transformation variable selec-tion and multicollinearity are considered. The results indicate that the neural network model is a viable alternative to the regression model for addressing both messy data problems and limitation in variable structure specification.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.