• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.504-512
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• 1994

A simple proof for the random central limit theorem is given for a family of stationary linear lattice processes, which belogn to a class of 2 dimensional random fields, applying the Beveridge and Nelson decomposition in time series context. The result is an extension of Fakhre-Zakeri and Fershidi (1993) dealing with the linear process in time series to the case of the linear lattice process with 2 dimensional indices.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.1901-1906
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• 1995

The frequency response functions of systems incorporating a non-linear cubic stiffness subject to sinusoidal excitation are derived using the Volterra series and the convergence characteristics investigated. It is shown that the series representation of the frequency response functions converges only when the sinewave input amplitude is within a certain range. Within the range of convergence the frequency response function based on the Volterra series approaches the analytical one as more higher order frequency response function terms are included. Proposed is a criterion for the studies systems to predict approximately the range of sinewave input amplitude for which the series representation of the frequency response functions converges.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.305-311
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• 2020

This study presents a probabilistic time series forecast of ship structural response using Bayesian inference combined with Volterra linear model. The structural response of a ship exposed to irregular wave excitation was represented by a linear Volterra model and unknown uncertainties were taken care by probability distribution of time series. To achieve the goal, Volterra series of first order was expanded to a linear combination of Laguerre functions and the probability distribution of Laguerre coefficients is estimated using the prepared data by treating Laguerre coefficients as random variables. In order to check the validity of the proposed methodology, it was applied to a linear oscillator model containing damping uncertainties, and also applied to model test data obtained by segmented hull model of 400,000 DWT VLOC as a practical problem.

• Wind and Structures
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• v.6 no.2
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• pp.107-126
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• 2003

This paper describes a simple and practical approach through the application of Linear Stochastic Estimation (LSE) to reconstruct wind-induced pressure time series from the covariance matrix for structural load analyses on a low building roof. The main application of this work would be the reduction of the data storage requirements for the NIST aerodynamic database. The approach is based on the assumption that a random pressure field can be estimated as a linear combination of some other known pressure time series by truncating nonlinear terms of a Taylor series expansion. Covariances between pressure time series to be simulated and reference time series are used to calculate the estimation coefficients. The performance using different LSE schemes with selected reference time series is demonstrated by the reconstruction of structural load time series in a corner bay for three typical wind directions. It is shown that LSE can simulate structural load time series accurately, given a handful of reference pressure taps (or even a single tap). The performance of LSE depends on the choice of the reference time series, which should be determined by considering the balance between the accuracy, data-storage requirements and the complexity of the approach. The approach should only be used for the determination of structural loads, since individual reconstructed pressure time series (for local load analyses) will have larger errors associated with them.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1608-1615
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• 2003

This paper studies on the linearization of a looper system in hot strip mills, that plays an important role in regulating a strip tension or a strip width. Nonlinear dynamic equations of the looper system are analytically linearized by a static feedback linearization algorithm with a compensator. The proposed linear model of the looper is validated by a comparison with a linear model using Taylor's series. It is shown that the linear model by static feedback well describes nonlinearities of the looper system than one using Taylor's series. Furthermore, it is shown from the design of an ILQ controller that the linear model by static feedback is very useful in designing a linear controller of the looper system.

• Journal of the Korean Mathematical Society
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• v.44 no.3
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• pp.565-583
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• 2007

We offer a refinement of the classical Clifford inequality about special linear series on smooth irreducible complex curves. Namely, we prove about curves of genus g and odd gonality at least 5 that for any linear series $g^r_d$ with $d{\leq}g+1$, the inequality $3r{\leq}d$ holds, except in a few sporadic cases. Further, we show that the dimension of the set of curves in the moduli space for which there exists a linear series $g^r_d$ with d<3r for $d{\leq}g+l,\;0{\leq}l{\leq}\frac{g}{2}-3$, is bounded by $2g-1+\frac{1}{3}(g+2l+1)$.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.141-145
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• 2010

Most of the industrial loads includes the non-linear load as well as the linear load because there are many kinds of power conversion equipments at the input stage of the load in distribution network. The non-linear load causes the distortion of voltage waveform at PCC because the non-linear load generates the harmonic current. As a result, various voltage harmonics are existed at PCC depending on the current harmonics from the non-linear load. And, a series reactor is generally connected to the power capacitor in series to attenuate the distortion of voltage waveform and to reduce an inrush current of power capacitor. Also, harmonic current of power capacitor is highly dependent on the series reactor because it is operated with the power capacitor as a passive filter against nonlinear loads. Then, these capacitors might be damaged by the excessive voltage and current harmonic components. In this paper, we presented how to select the capacitor and series reactor to meet the requirement of the voltage distortion at PCC and analyzed the voltage, current and capacity rating of the power capacitor by the computer simulation to ensure the safe operation of power capacitor when the voltage harmonics at PCC are existed. Also, the analysis data were compared with the experimental measurements for the verification.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.573-589
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• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• Journal of Advanced Marine Engineering and Technology
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• v.8 no.1
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• pp.100-111
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• 1984

The methods solving the 2nd order linear ordinary differential equations of the form y"+H(x)y'+G(x)y=P(x) using Fourier series are presented in this paper. These methods are applied to the differential equations of which the exact solutions are known, and the solutions by Fourier series are compared with the exact solutions. The main results obtained in these studies are summarized as follows; 1) The product and the quotient of two functions expressed in Fourier series can be expressed also in Fourier series and the relations between the Fourier coefficients of the series are obtained by multiplying term by term. 2) If the solution of the 2nd order lindar ordinary differential equation exists in a certain interval, the solution can be obtained using Fourier series and can be expressed in Fourier series. 3) The absolute errors of Fourier series solutions are generally less in the center of the interval than in the end of the interval. 4) The more terms are considered in Fourier series solutions, the less the absolute errors.rors.