• Wind and Structures
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• 제32권1호
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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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• 제어로봇시스템학회 2002년도 ICCAS
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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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• 제11권1_2호
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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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• 제22권5호
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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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• 제35권4호
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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.

• 음성과학
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• 제11권4호
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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.

• 제어로봇시스템학회논문지
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• 제13권12호
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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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• 제6권2호
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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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• 제24권1호
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• pp.33-39
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• 2013

반응변수의 변환을 고려하는 부분선형모형에서 이상치 문제는 선형모형에서와 마찬가지로 반응변수 변환모수의 추정에 왜곡된 결과를 초래할 수 있다. 이를 해결하기 위해서는 부분선형모형에서 반응변수 변환 모수 추정과 이상치 탐지 과정이 수행되어야 하지만 모형에 포함된 비모수 함수의 비정형성에 따른 어려움이 크다. 본 연구에서는 부분선형모형의 비모수함수에 대한 추정과 순차적 검정, 최대절사우도추정 등과 같은 이상치 제거방법의 적용을 통하여 부분선형모형에서 이상치에 강건한 반응변수 변환 과정을 제안한다. 제안된 방법들은 모의실험과 예제를 통해 효과를 비교 검증한다.

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.563-571
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• 2001

In this paper, we study selecting a transformation so that the transformed variable is nearly symmetrically distributed. The large sample properties of an M-estimator of transformation parameter that is obtained by minimizing the integrated square of the imaginary part of the empirical characteristic function are investigated when a random sample is selected from some unspecified distribution. According to influence function calculations and Monte Carlo simulations, these estimates are less sensitive, than the normal model maximum likelihood estimates, to a few outliers.