• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2A
• /
• pp.383-390
• /
• 2006

In this paper, a stochastic field that is compatible with Monte Carlo simulation is suggested for an expansion-based stochastic analysis scheme of weighted integral method. Through investigation on the way of affection of stochastic field function on the displacement vector in the series expansion scheme, it is noticed that the stochastic field adopted in the weighted integral method is not compatible with that appears in the Monte Carlo simulation. As generally recognized in the field of stochastic mechanics, the response variability is not a linear function of the coefficient of variation of stochastic field but a nonlinear function with increasing variability as the intensity of uncertainty is increased. Employing the stochastic field suggested in this study, the response variability evaluated by means of the weighted integral scheme is reproduced with high precision even for uncertain fields with moderately large coefficient of variation. Besides, despite the fact that only the first-order expansion is employed, an outstanding agreement between the results of expansion-based weighted integral method and Monte Carlo simulation is achieved.

• Structural Engineering and Mechanics
• /
• v.52 no.3
• /
• pp.525-540
• /
• 2014

The Lyapunov exponent and moment Lyapunov exponents of Hill's equation with frequency and damping coefficient fluctuated by correlated wideband random processes are studied in this paper. The method of stochastic averaging, both the first-order and the second-order, is applied. The averaged $It\hat{o}$ differential equation governing the pth norm is established and the pth moment Lyapunov exponents and Lyapunov exponent are then obtained. This method is applied to the study of the almost-sure and the moment stability of the stationary solution of the thin simply supported beam subjected to time-varying axial compressions and damping which are small intensity correlated stochastic excitations. The validity of the approximate results is checked by the numerical Monte Carlo simulation method for this stochastic system.

• Structural Engineering and Mechanics
• /
• v.11 no.4
• /
• pp.373-392
• /
• 2001

The main idea behind the paper is to present two alternative methods of homogenization of the heat conduction problem in composite materials, where the heat conductivity coefficients are assumed to be random variables. These two methods are the Monte-Carlo simulation (MCS) technique and the second order perturbation second probabilistic moment method, with its computational implementation known as the Stochastic Finite Element Method (SFEM). From the mathematical point of view, the deterministic homogenization method, being extended to probabilistic spaces, is based on the effective modules approach. Numerical results obtained in the paper allow to compare MCS against the SFEM and, on the other hand, to verify the sensitivity of effective heat conductivity probabilistic moments to the reinforcement ratio. These computational studies are provided in the range of up to fourth order probabilistic moments of effective conductivity coefficient and compared with probabilistic characteristics of the Voigt-Reuss bounds.

• Structural Engineering and Mechanics
• /
• v.15 no.6
• /
• pp.669-683
• /
• 2003

The stochastic optimal nonlinear control of coupled adjacent building structures is studied based on the stochastic dynamical programming principle and the stochastic averaging method. The coupled structures with control devices under random seismic excitation are first condensed to form a reduced-order structural model for the control analysis. The stochastic averaging method is applied to the reduced model to yield stochastic differential equations for structural modal energies as controlled diffusion processes. Then a dynamical programming equation for the energy processes is established based on the stochastic dynamical programming principle, and solved to determine the optimal nonlinear control law. The seismic response mitigation of the coupled structures is achieved through the structural energy control and the dimension of the optimal control problem is reduced. The seismic excitation spectrum is taken into account according to the stochastic dynamical programming principle. Finally, the nonlinear controlled structural response is predicted by using the stochastic averaging method and compared with the uncontrolled structural response to evaluate the control efficacy. Numerical results are given to demonstrate the response mitigation capabilities of the proposed stochastic optimal control method for coupled adjacent building structures.

• Journal of applied mathematics & informatics
• /
• v.20 no.1_2
• /
• pp.119-132
• /
• 2006

This paper presents an approach for using right-truncated exponentially distributed random variables to model activity times in stochastic activity networks. The advantages of using the right-truncated exponential distribution are discussed. The moments of a project completion time using the proposed distribution are derived and compared with other estimated moments in literature.

• Korean Management Science Review
• /
• v.11 no.1
• /
• pp.107-115
• /
• 1994

Stochastic manufacturing systems are generally formulated as performance models of discrete event systems. In this paper, logical models(as opposed to performance models) of kanban systems are presented which are deterministic and untimed but not stochastic and timed. As a result, the first and second order properties of kanban systems are showed which can be fruitfully applied to the analysis and design of kanban systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.715-718
• /
• 2002

A control strategy for a dynamic system under Irregular disturbance by using stochastic controller is developed. In order to design stochastic controller. system dynamic model in real domain is transformed dynamic moment equation in stochastic domain by F-P-K approach. A study of real time control technique for stochastic controller is presented. The performance of stochastic controller is verified through experiment used by real time control technique method.

• Journal of the Chungcheong Mathematical Society
• /
• v.5 no.1
• /
• pp.103-110
• /
• 1992

In this paper, T. Kawata's result[2] is generalized, by means of Beurling's norm, in the case of periodic stochastic processes of the second order which belong to Lipschitz class.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.6
• /
• pp.89-96
• /
• 1989

An improved method on texture coding, which is a part of the region growing based image coding, is presented in this paper. An image is segmented into stochastic regions which can be described as a stochastic random field, and non-stochastic ones in order to efficiently represent texture. In the texture coding and reconstruction, an autoregressive model is used for the stochastic regions, while a two-dimensional polynomial approximation is used for the non-stochastic ones. This proposed method leads to a better subjective quality, relatively higher compression ratio and shorter processing time for coding and reconstructing than the conventional method which uses only two-dimensional polynomial approximation.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.8
• /
• pp.1822-1831
• /
• 1995

A general formulation of the design optimization problem with the random parameters is presented here. The formulation is based on the stochastic finite element second-order perturbation method ; it takes into full account of the stress and displacement constraints together with the rates of change of the random variables. A method of direct differentiation for calculating the sensitivity coefficients in regard to the governing equation and the second-order perturbed equation is derived. A gradient-based nonlinear programming technique is used to solve the problem. The numerical results are specifically noted, where the stiffness parameter and external load are treated as random variables.