Network reliability design via joint probabilistic constraints

2009 ◽  
Vol 21 (2) ◽  
pp. 213-226 ◽  
Author(s):  
P. Beraldi ◽  
M. E. Bruni ◽  
F. Guerriero
Keyword(s):  
Network Reliability ◽  
Probabilistic Constraints ◽  
Reliability Design ◽  
Network Reliability Design

Impact Analysis of Communication Network Reliability Based on Node Failure

2013 ◽  
Vol 347-350 ◽  
pp. 2100-2105
Author(s):  
Fei Zhao ◽  
Ning Huang ◽  
Jia Xi Chen
Keyword(s):  
Communication Network ◽  
Impact Analysis ◽  
Failure Modes ◽  
Network Reliability ◽  
Node Failure ◽  
Reliability Design ◽  
Complex Network Theory ◽  
And Performance ◽  
Network Reliability Design ◽  
Operation Cost

Node failure is an important factor causing network faults. Through research on node failures, mastering the effect laws of failure is a reasonable and effective method to improve network reliability. This paper summarized and classified the node failure modes of communication network. Meanwhile, combined with the classic BA network model in complex network theory, the effect laws of nodes function failure and performance failure on network reliability were investigated with the design of simulations using MATLAB and OPNET. The results have great guidance value for the simulation of network reliability and network reliability design under limited operation cost.

scholarly journals Percolation Analysis of Brain Structural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shu Guo ◽  
Xiaoqi Chen ◽  
Yimeng Liu ◽  
Rui Kang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Failure Modes ◽  
Critical Infrastructure ◽  
Network Reliability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Features ◽  
Brain Network ◽  
Structure Design ◽  
Reliability Design ◽  
The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

An Integrated Framework for Optimization Under Uncertainty Using Inverse Reliability Strategy

2004 ◽  
Vol 126 (4) ◽  
pp. 562-570 ◽  
Author(s):  
Xiaoping Du ◽  
Agus Sudjianto ◽  
Wei Chen
Keyword(s):  
Combustion Engine ◽  
Search Algorithm ◽  
Traditional Approach ◽  
Optimization Under Uncertainty ◽  
Probabilistic Constraints ◽  
Reliability Design ◽  
Integrated Framework ◽  
Design Objective ◽  
Reliability Assessments ◽  
Objective Robustness

In this work, we propose an integrated framework for optimization under uncertainty that can bring both the design objective robustness and the probabilistic design constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective robustness and probabilistic constraints. The percentile formulation for objective robustness provides us an accurate evaluation of the variation of an objective performance and a probabilistic measurement of the robustness. We can obtain more reasonable compound noise combinations for a robust design objective compared to using the traditional approach proposed by Taguchi. The proposed formulation is very efficient to solve since it only needs to evaluate the constraint functions at the required reliability levels. The other major development of this work is a new search algorithm for the Most Probable Point of Inverse Reliability (MPPIR) that can be used to efficiently evaluate percentile performances for both robustness and reliability assessments. Multiple strategies are employed in the MPPIR search, including using the steepest ascent direction and an arc search. The algorithm is applicable to general non-concave and non-convex performance functions of random variables following any continuous distributions. The effectiveness of the MPPIR search algorithm is verified using example problems. Overall, an engineering example on integrated robust and reliability design of a vehicle combustion engine piston is used to illustrate the benefits of our proposed method.

Network System Reliability and Unit Probability Importance Based on Monte Carlo Method

2014 ◽  
Vol 1049-1050 ◽  
pp. 1458-1462
Author(s):  
Rao Bin
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Network Reliability ◽  
Approximate Algorithm ◽  
Network System ◽  
Reliability Design ◽  
Precise Calculation ◽  
Improve Calculation ◽  
Factor Decomposition ◽  
State Enumeration

The network system reliability research contains a number of problems, such as: Reliability analysis and reliability design, reliability, maintenance and a lot of problems so on. The calculation of reliability of the network is the important area of network reliability analysis, State enumeration method and principle of a class, don't pay the product and method, the factor decomposition method is a classic accurate algorithm of computing network reliability. Due to the difficulty of precise calculation, in the method, appeared and bound method, Monte carol method, the reliability of the approximate algorithm. Compared with the accurate algorithm, approximate algorithm is still under development. So far, no recognized classic algorithms, so the method to improve calculation accuracy, reduce the complexity of the target of the researchers.

An Integrated Framework for Probabilistic Optimization Using Inverse Reliability Strategy

2003 ◽  
Author(s):  
Xiaoping Du ◽  
Agus Sudjianto ◽  
Wei Chen
Keyword(s):  
Combustion Engine ◽  
Search Algorithm ◽  
Probabilistic Constraints ◽  
Probabilistic Measure ◽  
Reliability Design ◽  
Probabilistic Optimization ◽  
Integrated Framework ◽  
Engine Piston ◽  
Major Development ◽  
Objective Robustness

In this work, we propose an integrated framework for probabilistic optimization that can bring both the design objective robustness and the probabilistic constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective robustness and probabilistic constraints. The percentile formulation for objective robustness provides an accurate probabilistic measure for robustness and more reasonable compound noise combinations. For the probabilistic constraints, compared to a traditional probabilistic model, the proposed formulation is more efficient since it only evaluates the constraint functions at the required reliability levels. The other major development of this work is a new search algorithm for the Most Probable Point of Inverse Reliability (MPPIR) that can be used to efficiently evaluate the performance robustness and percentile performance in the proposed formulation. Multiple techniques are employed in the MPPIR search, including the steepest decent direction and an arc search. The algorithm is applicable to general non-concave and non-convex functions of system performance with random variables following any continuous distributions. The effectiveness of the MPPIR search algorithm is verified using example problems. Overall, an engineering example on integrated robust and reliability design of a vehicle combustion engine piston is used to illustrate the benefits of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document