Results 11 to 20 of about 31,999 (310)

Homogeneous decomposition of stochastic systems

Theoretical Computer Science, 1985
Recently, concerning the homogeneous whirl decomposition of stochastic systems, \textit{S. Kikuchi} and \textit{E. Fujino} [IECE Japan Trans. J 61-D, 827-833 (1978)] have suggested a decomposition theory (for any stochastic system) using the technique of 'state splitting'. However, their theory has the weak point that in the component stochastic system
Taiho Kanaoka, Shingo Tomita
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The decomposition of stochastic automata

Information and Control, 1964
Finite-state stochastic sequential systems which are interconnections of two or more component systems exhibit a special structural property in their transition matrices as a consequence of the statistical independence of the next states of the component systems given the present states and the input to the interconnection. Along with the concept of a “
Bacon, Glenn C.
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Tutorial on Empirical Mode Decomposition: Basis Decomposition and Frequency Adaptive Graduation in Non-Stationary Time Series

IEEE Access, 2023
This tutorial explores the class of non-parametric time series basis decomposition methods particularly suited for nonstationary time series known as Empirical Mode Decomposition (EMD). In outlining a statistical perspective of the EMD method, it will be
Cole van Jaarsveldt, Gareth W. Peters, Matthew Ames, Mike Chantler   +3 more
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Variance decomposition in stochastic simulators [PDF]

The Journal of Chemical Physics, 2015
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized ...
Le Maître, O. P., Knio, O. M., Moraes, Alvaro   +2 more
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The decomposition of stochastic systems

Theoretical Computer Science, 1983
Using the deterministic framework of \textit{J. Hartmanis} and \textit{R. E. Stearns} [Algebraic structure theory of sequential machines (1966; Zbl 0154.417)] as a point of entry, the authors rely on use of an analogy to the substitution property to realize stochastic systems by the appropriate interconnection of smaller stochastic systems.
Taiho Kanaoka, Shingo Tomita
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Formally Verifying Decompositions of Stochastic Specifications

International Journal on Software Tools for Technology Transfer, 2022
AbstractAccording to the principles of compositional verification, verifying that lower-level components satisfy their specification ensures that the whole system satisfies its top-level specification. The key step is to ensure that the lower-level specifications constitute a correct decomposition of the top-level specification.
Anton Hampus, Mattias Nyberg
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Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

Machines, 2021
In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information.
Di Xu, Jianghua Ge, Yaping Wang, Junpeng Shao   +3 more
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A posteriori error estimation for stochastic static problems [PDF]

, 2014
To solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the
MAC, Hung, CLENET, Stephane
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A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

Energies, 2020
The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive
Sini Han, Hyeon-Jin Kim, Duehee Lee
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Domain decomposition for stochastic optimal control [PDF]

53rd IEEE Conference on Decision and Control, 2014
This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimization to approximate the value function, requiring a high polynomial degree to capture local phenomena.
Matanya B. Horowitz, Ivan Papusha, Joel W. Burdick   +2 more
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