• Title/Summary/Keyword: Stochastic Process

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BACKWARD SELF-SIMILAR STOCHASTIC PROCESSES IN STOCHASTIC DIFFERENTIAL EQUATIONS

  • Oh, Jae-Pill
    • Korean Journal of Mathematics
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    • v.6 no.2
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    • pp.259-279
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    • 1998
  • For the forward-backward semimartingale, we can define the backward semimartingale flow which is generated by the backward canonical stochastic differential equation. Therefore, we define the backward self-similar stochastic processes, and we study the backward self-similar stochastic flows through the canonical stochastic differential equations.

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BERRY-ESSEEN BOUND FOR MLE FOR LINEAR STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY FRACTIONAL BROWNIAN MOTION

  • RAO B.L.S. PRAKASA
    • Journal of the Korean Statistical Society
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    • v.34 no.4
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    • pp.281-295
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    • 2005
  • We investigate the rate of convergence of the distribution of the maximum likelihood estimator (MLE) of an unknown parameter in the drift coefficient of a stochastic process described by a linear stochastic differential equation driven by a fractional Brownian motion (fBm). As a special case, we obtain the rate of convergence for the case of the fractional Ornstein- Uhlenbeck type process studied recently by Kleptsyna and Le Breton (2002).

REFLECTED BSDE DRIVEN BY A L$\acute{E}$VY PROCESS WITH STOCHASTIC LIPSCHITZ COEFFICIENT

  • Lu, Wen
    • Journal of applied mathematics & informatics
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    • v.28 no.5_6
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    • pp.1305-1314
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    • 2010
  • In this paper, we deal with a class of one-dimensional reflected backward stochastic differential equations driven by a Brownian motion and the martingales of Teugels associated with an independent L$\acute{e}$vy process having a stochastic Lipschitz coefficient. We derive the existence and uniqueness of solutions for these equations via Snell envelope and the fixed point theorem.

Applications of Stochastic Process in the Quadrupole Ion traps

  • Chaharborj, Sarkhosh Seddighi;Kiai, Seyyed Mahmod Sadat;Arifina, Norihan Md;Gheisari, Yousof
    • Mass Spectrometry Letters
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    • v.6 no.4
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    • pp.91-98
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    • 2015
  • The Brownian motion or Wiener process, as the physical model of the stochastic procedure, is observed as an indexed collection random variables. Stochastic procedure are quite influential on the confinement potential fluctuation in the quadrupole ion trap (QIT). Such effect is investigated for a high fractional mass resolution Δm/m spectrometry. A stochastic procedure like the Wiener or Brownian processes are potentially used in quadrupole ion traps (QIT). Issue examined are the stability diagrams for noise coefficient, η=0.07;0.14;0.28 as well as ion trajectories in real time for noise coefficient, η=0.14. The simulated results have been obtained with a high precision for the resolution of trapped ions. Furthermore, in the lower mass range, the impulse voltage including the stochastic potential can be considered quite suitable for the quadrupole ion trap with a higher mass resolution.

Stochastic control approach to reliability of elasto-plastic structures

  • Au, Siu-Kui
    • Structural Engineering and Mechanics
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    • v.32 no.1
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    • pp.21-36
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    • 2009
  • An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

Analysis on random vibration of a non-linear system in flying vehicle due to stochastic disturbances (불규칙 교란을 받는 비행체에 장착된 비선형 시스템의 난진동 해석)

  • 구제선
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.14 no.6
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    • pp.1426-1435
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    • 1990
  • Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

Application of Hidden Markov Model Using AR Coefficients to Machine Diagnosis (AR계수를 이용한 Hidden Markov Model의 기계상태진단 적용)

  • 이종민;황요하;김승종;송창섭
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.13 no.1
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    • pp.48-55
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    • 2003
  • Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

The Construct of the Program Control with Probability is Equaled to 1 for the Some Class of Stochastic Systems

  • Chalykh, Elena
    • Journal of Ubiquitous Convergence Technology
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    • v.2 no.2
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    • pp.105-110
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    • 2008
  • The definition of the program control is introduced on the theory of the basis of the first integrals SDE system. That definition allows constructing the program control gives opportunity to stochastic system to remain on the given dynamic variety. The program control is considered in terms of dynamically invariant for stochastic process.

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Bounding Methods for Markov Processes Based on Stochastic Monotonicity and Convexity (확률적 단조성과 콘벡스성을 이용한 마코프 프로세스에서의 범위한정 기법)

  • Yoon, Bok-Sik
    • Journal of Korean Institute of Industrial Engineers
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    • v.17 no.1
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    • pp.117-126
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    • 1991
  • When {X(t), t ${\geq}$ 0} is a Markov process representing time-varying system states, we develop efficient bounding methods for some time-dependent performance measures. We use the discretization technique for stochastically monotone Markov processes and a combination of discretization and uniformization for Markov processes with the stochastic convexity(concavity) property. Sufficient conditions for stochastic monotonocity and stochastic convexity of a Markov process are also mentioned. A simple example is given to demonstrate the validity of the bounding methods.

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Discrete Event Simulation for the Initial Capacity Estimation of Shipyard Based on the Master Production Schedule (대일정 생산 계획에 따른 조선소 생산 용량의 초기 평가를 위한 이산사건 시뮬레이션)

  • Kim, Kwang-Sik;Hwang, Ho-Jin;Lee, Jang-Hyun
    • Korean Journal of Computational Design and Engineering
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    • v.17 no.2
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    • pp.111-122
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    • 2012
  • Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.