One-Dimensional Markov-Functional Models Driven by Non-Gaussian Markov Processes

2017 ◽  
Author(s):  
Jaka Gogala
Keyword(s):  
Markov Processes ◽  
One Dimensional ◽  
Functional Models ◽  
Non Gaussian

A simple model of a one-dimensional, randomly rough, non-Gaussian surface

2016 ◽  
Author(s):  
E. R. Méndez ◽  
G. D. Jiménez ◽  
A. A. Maradudin
Keyword(s):  
Simple Model ◽  
One Dimensional ◽  
Non Gaussian ◽  
Gaussian Surface

scholarly journals Development of the logical part of the intellectual multi-parameter relay protection

2019 ◽  
Vol 139 ◽  
pp. 01060 ◽  
Author(s):  
Anton Loskutov ◽  
Pavel Pelevin ◽  
Mile Mitrovoc
Keyword(s):  
Markov Processes ◽  
Software Package ◽  
Statistical Data ◽  
Reactive Power ◽  
Relay Protection ◽  
One Dimensional ◽  
Active And Reactive Power ◽  
Logical Part ◽  
Fault Detectors

The issues of increasing the sensitivity and reliability of multi-parameter relay protection by sharing more than one information feature (current module, voltage module and phase, active and reactive power) are considered. In this case, the response parameters of individual one-dimensional measuring fault detectors based on the accumulation of statistical data during simulation in the Matlab / Similink software package are determined. A method for combining the signals of one-dimensional measuring fault detectors to increase the sensitivity of protection is proposed. The reliability of the organization of the logical part of multi-parameter relay protection was estimated using the theory of Markov processes, the principles of “2 out of 3” and “1 out of 2”.

A comparison theorem for conditioned Markov processes

1991 ◽  
Vol 28 (01) ◽  
pp. 74-83 ◽  
Author(s):  
G. O. Roberts
Keyword(s):  
Markov Processes ◽  
Comparison Theorem ◽  
Hitting Time ◽  
Time Dependent ◽  
One Dimensional

Intuitively, the effect of conditioning a one-dimensional process to remain below a certain (possibly time-dependent) boundary is to ‘push' the process downwards. This paper investigates the effect of such conditioning, and finds the class of processes for which our intuition is accurate. It is found that ordinary stochastic inequalities are in general unsuitable for making statements about such conditioned processes, and that a stronger type of inequality is more appropriate. The investigation is motivated by applications in estimation of boundary hitting time distributions.

scholarly journals Discretizing nonlinear, non-Gaussian Markov processes with exact conditional moments

10.3982/qe737 ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 651-683 ◽  
Author(s):  
Leland E. Farmer ◽  
Alexis Akira Toda
Keyword(s):  
Markov Processes ◽  
Conditional Moments ◽  
Non Gaussian
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