scholarly journals Tail Probability of a Gaussian Fluid Queue under Finite Measurement of Input Processes

1998 ◽  
pp. 43-58 ◽  
Author(s):  
Kazutomo Kobayashi ◽  
Yukio Takahashi
Keyword(s):  
Tail Probability ◽  
Fluid Queue

A comparison of tail probability estimators for flood frequency analysis

1993 ◽  
Vol 151 (2-4) ◽  
pp. 343-363 ◽  
Author(s):  
Young-Il Moon ◽  
Upmanu Lall ◽  
Ken Bosworth
Keyword(s):  
Frequency Analysis ◽  
Tail Probability ◽  
Flood Frequency ◽  
Flood Frequency Analysis

scholarly journals Fluid Queue Driven by anM/M/1Queue Subject to Bernoulli-Schedule-Controlled Vacation and Vacation Interruption

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kolinjivadi Viswanathan Vijayashree ◽  
Atlimuthu Anjuka
Keyword(s):  
Death Process ◽  
Queueing Model ◽  
Fluid Queue ◽  
Stationary Analysis ◽  
Governing System ◽  
Matrix Geometric Method ◽  
Vacation Interruption ◽  
Model Subject ◽  
Steady State Probabilities ◽  
Bernoulli Schedule

This paper deals with the stationary analysis of a fluid queue driven by anM/M/1queueing model subject to Bernoulli-Schedule-Controlled Vacation and Vacation Interruption. The model under consideration can be viewed as a quasi-birth and death process. The governing system of differential difference equations is solved using matrix-geometric method in the Laplacian domain. The resulting solutions are then inverted to obtain an explicit expression for the joint steady state probabilities of the content of the buffer and the state of the background queueing model. Numerical illustrations are added to depict the convergence of the stationary buffer content distribution to one subject to suitable stability conditions.

Non-Markovian model for the study of pitting corrosion in a water pipe system

2015 ◽  
Vol 26 (10) ◽  
pp. 1550119 ◽  
Author(s):  
A. C. P. Rosa ◽  
P. Vaveliuk ◽  
M. A. Moret
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Extreme Values ◽  
Tail Probability ◽  
Markovian Model ◽  
Nonextensive Statistical Mechanics ◽  
Long Tail ◽  
Pipe System ◽  
Proposed Model ◽  
State Regime

The main studies on pitting consist in proposing Markovian stochastic models, based on the statistics of extreme values and focused on growing the depth of wells, especially the deepest one. We show that a non-Markovian model, described by a nonlinear Fokker–Planck (nFP) equation, properly depicts the time evolution of a distribution of depth values of pits that were experimentally obtained. The solution of this equation in a steady-state regime is a q-Gaussian distribution, i.e. a long-tail probability distribution that is the main characteristic of a nonextensive statistical mechanics. The proposed model, that is applied to data from four inspections conducted on a section of a line of regular water service in power water reactor (PWR) nuclear power plants, is in agreement with experimental results.

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