• 대한기계학회논문집
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• 제14권6호
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• pp.1426-1435
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• 1990

본 연구에서는 확률론적 등가선형화 기법을 사용하여 비선형 랜덤 시스템을 선형화하였다.또 이 선형화된 시스템을 최근에 새로이 제안된 방법을 적용하여 비 백색잡음형태의 랜덤 가진을 받을 때 그 거동을 구하였다.

• 대한전자공학회논문지
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• 제18권2호
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• pp.44-53
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• 1981

Stochastic feference를 가진 두개의 양자화 시스템을 분석하고 비교하였다. 양자화된 출력신호에 대하여 invariance 성격을 갖기 위한 조건이 서로 같다는 것이 보여졌다. Stochastic fererence 신호를 이용한 polarity coincidence 방법에 의한 상관관계 함수 추정이 여기서 구한 일반적 성질의 특수한 경우이다. 과거의 stochastic computing은 여기서 고려한 첫 번째 system으로부터 나오고 그리고 LG Robert가 이용한 특성은 두 번째 시스템의 일반적 성질의 특수한 경우를 이용했다는 것을 보였다.

• Nuclear Engineering and Technology
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• 제43권6호
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• pp.523-530
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• 2011

In a previous study, the stochastic space-dependent kinetics model (SSKM) based on the forward stochastic model in stochastic kinetics theory and the Ito stochastic differential equations was proposed for treating monoenergetic space-time nuclear reactor kinetics in one dimension. The SSKM was tested against analog Monte Carlo calculations, however, for exemplary cases of homogeneous slab reactors with only one delayed-neutron precursor group. In this paper, the SSKM is improved and evaluated with more realistic and complicated cases regarding several delayed-neutron precursor groups and heterogeneous slab reactors in which the extraneous source or reactivity can be introduced locally. Furthermore, the source level and the initial conditions will also be adjusted to investigate the trends in the variances of the neutron population and fission product levels across the reactor. The results indicate that the improved SSKM is in good agreement with the Monte Carlo method and show how the variances in population dynamics can be controlled.

• 한국정밀공학회지
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• 제12권4호
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• pp.127-134
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• 1995

The dynamic characteristics of a system can be critically influenced by system uncertainty, so the dynamic system must be analyzed stochastically in consideration of system uncertainty. This study presents a generalized stochastic model of dynamic system subjected to bot external and parametric nonstationary stochastic input. And this stochastic system is analyzed by a new stochastic process closure method and moment equation method. The first moment equation is numerically evaluated by Runge-Kutta method. But the second moment equation is founded to constitute an infinite coupled set of differential equations, so this equations are numerically evaluated by cumulant neglect closure method and Runge-Kutta method. Finally the accuracy of the present method is verified by Monte Carlo simulation.

• Smart Structures and Systems
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• 제9권3호
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• pp.231-251
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• 2012

The stochastic optimal control for a piezoelectric spherically symmetric shell subjected to stochastic boundary perturbations is constructed, analyzed and evaluated. The stochastic optimal control problem on the boundary stress output reduction of the piezoelectric shell subjected to stochastic boundary displacement perturbations is presented. The electric potential integral as a function of displacement is obtained to convert the differential equations for the piezoelectric shell with electrical and mechanical coupling into the equation only for displacement. The displacement transformation is constructed to convert the stochastic boundary conditions into homogeneous ones, and the transformed displacement is expanded in space to convert further the partial differential equation for displacement into ordinary differential equations by using the Galerkin method. Then the stochastic optimal control problem of the piezoelectric shell in partial differential equations is transformed into that of the multi-degree-of-freedom system. The optimal control law for electric potential is determined according to the stochastic dynamical programming principle. The frequency-response function matrix, power spectral density matrix and correlation function matrix of the controlled system response are derived based on the theory of random vibration. The expressions of mean-square stress, displacement and electric potential of the controlled piezoelectric shell are finally obtained to evaluate the control effectiveness. Numerical results are given to illustrate the high relative reduction in the root-mean-square boundary stress of the piezoelectric shell subjected to stochastic boundary displacement perturbations by the optimal electric potential control.

• 한국경영과학회지
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• 제41권3호
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• pp.23-36
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• 2016

We consider a model that minimizes the total cost incurred by assigning available weapons to existing targets in order to reduce enemy threats, which is called the weapon target assignment problem (WTAP). This study addresses the stochastic versions of WTAP, in which data, such as the probability of destroying a target, are given randomly (i.e., data are identified with certain probability distributions). For each type of random data or parameter, we provide a stochastic optimization model on the basis of the expected value or scenario enumeration. In particular, when the probabilities of destroying targets depending on weapons are stochastic, we present a stochastic programming formulation with a simple recourse. We show that the stochastic model can be transformed into a deterministic equivalent mixed integer programming model under a certain discrete probability distribution of randomness. We solve the stochastic model to obtain an optimal solution via the mixed integer programming model and compare this solution with that of the deterministic model.

• 대한토목학회논문집
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• 제26권2A호
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• pp.383-390
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• 2006

본 연구에서는 급수전개를 이용한 추계론적 유한요소해석법의 개선을 위한 등가몬테카를로 추계장함수를 제안하고 1차 Taylor전개를 이용한 추계론적 유한요소해석법인 가중적분법에 적용하였다. 일반적으로 1차 Taylor전개를 이용하는 수치해석법에서의 응답변화도는 고려하고 있는 추계장의 분산계수에 대하여 선형거동을 보인다. 그러나 몬테카를로 해석의 경우 추계장 분산계수에 대하여 비선형 거동을 나타낸다. 이는 급수전개법의 1차 Taylor전개에 따른 선형특성에 기인한다. 따라서, 가중적분법에서 사용되는 Taylor전개된 변위벡터와 몬테카를로 해석에서의 변위벡터를 비교하고 이들 두 변위벡터 사이에 상호 불일치 하는 점을 고찰하여 몬테카를로 해석에서의 변위벡터와 등가의 변위벡터를 구성하고 이를 가중적분법에 적용하였다. 제안한 등가몬테카를로 추계장은 본래의 추계장 함수에 대한 고차함수로 주어진다. 평면구조에 대한 수치해석을 통하여 제안한 등가몬테카를로 추계장을 이용한 정식화의 타당성을 고찰하였다 새로운 정식화는 기존의 l차 가중적분법을 위한 정식화 과정과 유사하게 수행할 수 있었다.

• 소음진동
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• 제9권3호
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• pp.502-508
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• 1999

Newly developed control methodology applied to dynamic system under random disturbance is investigated and its performance is verified experimentall. Flexible cantilever beam sticked with piezofilm sensor and piezoceramic actuator is modelled in physical domain. Dynamic moment equation for the system is derived via Ito's stochastic differential equation and F-P-K equation. Also system's characteristics in stochastic domain is analyzed simultaneously. LQG controller is designed and used in physical and stochastic domain as wall. It is shown experimentally that randomly excited beam on the base is controlled effectively by designed LQG controller in physical domain. By comparing the result with that of LQG controller designed in stochastic domain, it is shown that new control method, what we called $\ulcorner$Heo-stochastic controller design technique$\lrcorner$, has better performance than conventional ones as a controller.

• Journal of applied mathematics & informatics
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• 제18권1_2호
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• pp.73-93
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• 2005

In this paper, we attempt to present a new numerical approach to solve non-linear backward stochastic differential equations. First, we present some definitions and theorems to obtain the conditions, from which we can approximate the non-linear term of the backward stochastic differential equation (BSDE) and we get a continuous piecewise linear BSDE correspond with the original BSDE. We use the relationship between backward stochastic differential equations and stochastic controls by interpreting BSDEs as some stochastic optimal control problems, to solve the approximated BSDE and we prove that the approximated solution converges to the exact solution of the original non-linear BSDE in two different cases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller