• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.94-101
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• 2003

Recently, miniaturization and weight reduction is getting more attention due to various benefits in automotive components design. It is a trend that the design of experiment(DOE) and statical design method are frequently used for optimization. In this research, the safety of lower control arm is evaluated according to its material change form S45C to A16061 for the reduction of arm's weight. The variance analysis on the basis of structure analysis and DOE is applied to the lower control m. We have proposed a statistical design model to evaluate the effect of structural modification by performing the practical multi-objective optimization considering mass, stress and deflection.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.305-312
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• 2005

In the cluster analysis, Hotelling's $T^2$ can be used to estimate the unknown number of clusters based on the idea of multiple comparison procedure. Especially, its threshold is obtained according to the probability of committing the type one error. Examples are used to compare Hotelling's $T^2$ with other classical location test statistics such as Sum-of-Squared Error and Wilks' $\Lambda$ The hierarchical clustering is used to reveal the underlying structure of the data. Also related criteria are reviewed in view of both the between variance and the within variance.

• Korea Trade Review
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• v.46 no.5
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• pp.115-130
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• 2021

The main purpose of this study is to analyze the causal relationship between import-export goods and transportation modes. To this end, five major commodity groups were selected from 2010 to 2018 such as Machinery and transport equipment (SITC 7), manufactured goods classified chiefly by material (SITC 6), chemicals and related products, n.e.s. (SITC 5), mineral, fuels, lubricants, and related materials (SITC 3), and miscellaneous manufactured articles (SITC 8). And using the panel VECM, the difference between transportation modes such as ports and airports was compared and analyzed through panel granger causality, Impulse response function, Forecasting error variance decomposition. As a result, it is confirmed that the causal relationship between major product groups and transportation modes showed different causal relationships depending on the characteristics of port and air transportation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of Environmental Science International
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• v.20 no.2
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• pp.155-161
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• 2011

In application and discussion of population structure and phenotypic divergence in plant community, the classic Lotka-Volterra models of competition and spatial model are conceived as a mechanism that is composed by multiple interacting processes. Both the Lotka-Volterra and spatial simulation formulae predict that species diversity increases with genotypic richness (GR). The two formulae are also in agreement that species diversity generally decreases within increasing niche breadth (NB) and increases with increasing potential genotypic range (PGR). Across the entire parameter space in the Lotka-Volterra model and most of the parameter space in the spatial simulations, variance in community composition decreased with increasing genotypic richness. This was, in large part, a consequence of selecting genotypes randomly from a set pool.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.444-447
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• 1995

The satellite in a circular orbit about a planet with disturbances and a three-degree-of-freedom (3DOF) structure under seismic excitations are modeled by the linear stochastic differential equations. Then the risk-sensitive optimal control method is applied to those equations. The mean and the variance of the cost function varies with respect to the risk-sensitivity parameter, .gamma.$_{RS}$ . For a particular risk-sensitivity parameter value, risk-sensitive control reduces to LQG control. Furthermore, the derivation of the mean square value of the state and control action are given for a finite-horizon full-state-feedback risk-sensitive control system. The risk-sensitive controller outperforms a classical LQG controller in the mean square sense of the state and the control action.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1183-1187
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• 2011

In this paper, an optimal FIR (Finite-Impulse-Response) filter is proposed for discrete time-varying state-space models. The proposed filter estimates the current state using measured output samples on the recent time horizon so that the variance of the estimation error is minimized. It is designed to be linear, unbiased, with an FIR structure, and is independent of any state information. Due to its FIR structure, the proposed filter is believed to be robust for modeling uncertainty or numerical errors than other IIR filters, such as the Kalman filter. For a general system with system and measurement noise, the proposed filter is derived without any artificial assumptions such as the nonsingular assumption of the system matrix A and any infinite covariance of the initial state. A numerical example show that the proposed FIR filter has better performance than the Kalman filter based on the IIR (Infinite- Impulse-Response) structure when modeling uncertainties exist.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.1
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• pp.63-70
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• 2016

This study is the sequel of a companion paper (I. Algorithm) for assessment of the seismic performance evaluation of structure using ground motions selected by the proposed algorithm. To evaluate the effect of the correlation structures of selected ground motions on the seismic responses of a structure, three sets of ground motions are selected with and without consideration of the correlation structure. Nonlinear response history analyses of a 20-story reinforced concrete frame are conducted using the three sets of ground motions. This study shows that the seismic responses of the frames vary according to ground motion selection and correlation structures.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.