Structural reliability computations with the use of the DOProC method and parallel processing

2019 ◽  
Author(s):  
J. Koktan ◽  
M. Krejsa ◽  
J. Brozovsky ◽  
P. Janas
Keyword(s):  
Parallel Processing ◽  
Structural Reliability

Parallel processing in structural reliability

2009 ◽  
Vol 32 (1) ◽  
pp. 95-126 ◽  
Author(s):  
M.F. Pellissetti
Keyword(s):  
Parallel Processing ◽  
Structural Reliability

Editorial Special Issue on parallel processing in GIS

1996 ◽  
Vol 10 (6) ◽  
pp. 667-668 ◽  
Author(s):  
R. G. Healey
Keyword(s):  
Parallel Processing ◽  
Special Issue ◽  
Editorial Special Issue

Temporal ventriloquism effect in European starlings: Evidence for two parallel processing pathways.

2017 ◽  
Vol 131 (4) ◽  
pp. 337-347 ◽  
Author(s):  
Gesa Feenders ◽  
Yoko Kato ◽  
Katharina M. Borzeszkowski ◽  
Georg M. Klump
Keyword(s):  
Parallel Processing ◽  
European Starlings ◽  
Temporal Ventriloquism ◽  
Processing Pathways

The preview effect in the object-naming task: Evidence for parallel processing of multiple objects

2009 ◽  
Author(s):  
Chen Chen ◽  
Manizeh Khan ◽  
Jesse Snedeker
Keyword(s):  
Parallel Processing ◽  
Naming Task ◽  
Object Naming ◽  
Multiple Objects

Serial and Parallel Processing Across Perceptual Groups

1994 ◽  
Author(s):  
Robert S. Mccann ◽  
David C. Foyle ◽  
James C. Johnston
Keyword(s):  
Parallel Processing

Comparison of sequential vs. parallel processing for imagery and rote material: Effect of varying cue position

1972 ◽  
Author(s):  
Lee Ann Elliott ◽  
Daniel J. Cox
Keyword(s):  
Parallel Processing

Parallel processing network for adaptive controllers

1993 ◽  
Vol 140 (1) ◽  
pp. 25 ◽  
Author(s):  
Y.-Z. Chang ◽  
R.W. Daniel
Keyword(s):  
Parallel Processing ◽  
Adaptive Controllers ◽  
Processing Network

Functional and operational risk as a criterion for assessing the durability of an autonomous energy system

2019 ◽  
Vol 12 (1) ◽  
pp. 56-62 ◽  
Author(s):  
A. O. Nedosekin ◽  
A. V. Smirnov ◽  
D. P. Makarenko ◽  
Z. I. Abdoulaeva
Keyword(s):  
Service Life ◽  
Structural Reliability ◽  
Residual Life ◽  
Fuzzy Random Variable ◽  
Operational Risk ◽  
Energy System ◽  
Technical System ◽  
Residual Service Life ◽  
Technological Parameters ◽  
Residual Service

The article presents new models and methods for estimating the residual service life of an autonomous energy system, using the functional operational risk criterion (FOR). The purpose of the article is to demonstrate a new method of durability evaluation using the fuzzy logic and soft computing framework. Durability in the article is understood as a complex property directly adjacent to the complex property of system resilience, as understood in the Western practice of assessing and ensuring the reliability of technical systems. Due to the lack of reliable homogeneous statistics on system equipment failures and recoveries, triangular fuzzy estimates of failure and recovery intensities are used as fuzzy functions of time based on incomplete data and expert estimates. The FOR in the model is the possibility for the system availability ratio to be below the standard level. An example of the evaluation of the FOR and the residual service life of a redundant cold supply system of a special facility is considered. The transition from the paradigm of structural reliability to the paradigm of functional reliability based on the continuous degradation of the technological parameters of an autonomous energy system is considered. In this case, the FOR can no longer be evaluated by the criterion of a sudden failure, nor is it possible to build a Markov’s chain on discrete states of the technical system. Assuming this, it is appropriate to predict the defi ning functional parameters of a technical system as fuzzy functions of a general form and to estimate the residual service life of the technical system as a fuzzy random variable. Then the FOR is estimated as the possibility for the residual life of the technical system to be below its warranty period, as determined by the supplier of the equipment.

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