The human factor: A challenge for network reliability design

2015 ◽  
Author(s):  
Magreth Mushi ◽  
Emerson Murphy-Hill ◽  
Rudra Dutta
Keyword(s):  
Human Factor ◽  
Network Reliability ◽  
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.

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.

A Methodology to Estimate the Reliability of Passageway system in Naval Ship Design

2009 ◽  
Vol 419-420 ◽  
pp. 721-724
Author(s):  
Chong Liu ◽  
Chang Hua Qiu ◽  
Zhi Qiang Xie
Keyword(s):  
Operating Procedure ◽  
Human Factor ◽  
Evaluation Method ◽  
Network Reliability ◽  
Ship Design ◽  
Simulation Software ◽  
Accurate Evaluation ◽  
Naval Ship ◽  
Systematic Methodology ◽  
The Impact

In this paper, we describe and demonstrate a general methodology to evaluate the reliability of the passageway system in warship design. The reliability of the passageway system is the most important component of naval ship survivability evaluation. Currently, most of the passageway evaluations focus on human factor or physical distribution; integrated calculates is lacking to estimate the reliability of the passageway system. The challenge for naval architects is to develop a systematic methodology that allows accurate evaluation of the passageway reliability associated with crews movement and related material operating procedure. The paper consults the concept of network reliability and lays out an evaluation method of passageway system based on task reliability. The method integrated analyses the impact of ship crew‘s location and distribution of the shipborne material. As it was difficult to achieve the reality data, the methodology is demonstrated using a hypothetical warship combining with the simulation software on the basis of two different evaluation scenarios.

An Application of the HFACS Method to Aviation Accidents in Africa

2016 ◽  
Vol 6 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Isaac Munene
Keyword(s):  
Human Factors ◽  
Physical Environment ◽  
Human Factor ◽  
Flight Training ◽  
African Countries ◽  
Causal Factors ◽  
Aviation Accidents ◽  
Factors Analysis ◽  
Adverse Weather ◽  
Decision Errors

Abstract. The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

Review of Flightdeck Performance: The Human Factor.

1991 ◽  
Vol 36 (8) ◽  
pp. 730-730
Author(s):  
No authorship indicated
Keyword(s):  
Human Factor
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