scholarly journals Alternative network robustness measure using system-wide transportation capacity for identifying critical links in road networks

2017 ◽  
Vol 9 (4) ◽  
pp. 168781401769665 ◽  
Author(s):  
Muqing Du ◽  
Xiaowei Jiang ◽  
Lin Cheng
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
A. E. Schweikert ◽  
G. F. L’Her ◽  
M. R. Deinert

AbstractCritical infrastructure failures from natural hazard events affect the economic and social well-being of communities. This is particularly true in lower income countries, where infrastructure may be less resistant to natural hazards and disaster recovery is often limited by available resources. The interconnectivity of these systems can strongly affect the services they deliver, and the failure of one infrastructure system can result in cascade failures with wide-reaching consequences. Unfortunately, interconnectivity has been particularly difficult to measure. We present a method for identifying service-oriented interdependencies in interconnected networks. The approach uses well-established methods for network analysis and is demonstrated for healthcare services in the Commonwealth of Dominica, a small island state in the Caribbean. We show that critical links in road networks necessary for healthcare service delivery are important for more than just patient access to a facility, but also on the supply chains that enable the hospitals to function (e.g., water, fuel, medicine). Once identified, the critical links can be overlaid with known hazard vulnerabilities to identify the infrastructure segments of highest priority, based on the risk and consequences of failure. An advantage of the approach presented is that it requires relatively little input data when compared to many network prioritization models and can be run using open-source geospatial data such as OpenStreetMap. The method can be expanded beyond road networks to assess the service-oriented criticality of any infrastructure network.


1992 ◽  
Vol 03 (03) ◽  
pp. 291-299 ◽  
Author(s):  
MO-YUEN CHOW ◽  
SUE OI YEE

The relative robustness of artificial neural networks subject to small input perturbations (e.g. measurement noises) is an important issue in real world applications. This paper uses the concept of input-output sensitivity analysis to derive a relative network robustness measure for different feedforward neural network configurations. For illustration purposes, this measure is used to compare different neural network configurations designed for detecting incipient faults in induction motors. Analytical and simulation results are presented to show that the relative network robustness measure derived in this paper is an effective indicator of the relative performance of different feedforward neural network configurations in noisy environments and that this measure should be considered in the design of neural networks for real time applications. The concept of input-output sensitivity analysis and relative network robustness measure presented can be extended to analyze other neural networks designed for on-line applications.


2010 ◽  
Vol 33 (8) ◽  
pp. 1396-1404 ◽  
Author(s):  
Liang ZHAO ◽  
Luo CHEN ◽  
Ning JING ◽  
Wei LIAO

2010 ◽  
Vol 30 (7) ◽  
pp. 1947-1949
Author(s):  
Bao-wen WANG ◽  
Jing-jing HAN ◽  
Zi-jun CHEN ◽  
Wen-yuan LIU

Sign in / Sign up

Export Citation Format

Share Document