scholarly journals An improved algorithm for finding all upper boundary points in a stochastic-flow network

2016 ◽  
Vol 40 (4) ◽  
pp. 3221-3229 ◽  
Author(s):  
Majid Forghani-elahabad ◽  
Nezam Mahdavi-Amiri
Keyword(s):  
Stochastic Flow ◽  
Flow Network ◽  
Boundary Points ◽  
Upper Boundary ◽  
Improved Algorithm

A Comparative Study of Different Approaches for Finding the Upper Boundary Points in Stochastic-Flow Networks

2014 ◽  
Vol 10 (3) ◽  
pp. 13-23 ◽  
Author(s):  
Seyed Mehdi Mansourzadeh ◽  
Seyed Hadi Nasseri ◽  
Majid Forghani-elahabad ◽  
Ali Ebrahimnejad
Keyword(s):  
Information System ◽  
Comparative Study ◽  
Test Problems ◽  
Stochastic Flow ◽  
Flow Networks ◽  
Flow Network ◽  
Random Test ◽  
Boundary Points ◽  
Exclusion Method ◽  
Upper Boundary

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a number of random test problems and implementing the algorithms in MATLAB, the proposed algorithm is demonstrated to be more efficient than some existing ones in medium-sized networks. The performance profile introduced by Dolan and More is used for analyzing the output of programs.

scholarly journals Reliability Evaluation for a Stochastic Flow Network Based on Upper and Lower Boundary Vectors

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1115
Author(s):  
Huang ◽  
Huang ◽  
Lin
Keyword(s):  
Lower Boundary ◽  
Reliability Evaluation ◽  
Exclusion Principle ◽  
Stochastic Flow ◽  
Decomposition Approach ◽  
Flow Network ◽  
Boundary Points ◽  
Classic Problem ◽  
Upper Boundary

For stochastic flow network (SFN), given all the lower (or upper) boundary points, the classic problem is to calculate the probability that the capacity vectors are greater than or equal to the lower boundary points (less than or equal to the upper boundary points). However, in some practical cases, SFN reliability would be evaluated between the lower and upper boundary points at the same time. The evaluation of SFN reliability with upper and lower boundary points at the same time is the focus of this paper. Because of intricate relationships among upper and lower boundary points, a decomposition approach is developed to obtain several simplified subsets. SFN reliability is calculated according to these subsets by means of the inclusion-exclusion principle. Two heuristic options are then established in order to calculate SFN reliability in an efficient direction based on the lower and upper boundary points.

Modeling and Applications on Timed Stochastic-Flow Networks

2015 ◽  
Vol 22 (05) ◽  
pp. 1550023
Author(s):  
Shin-Guang Chen
Keyword(s):  
Network Reliability ◽  
Daily Life ◽  
Transportation Network ◽  
Stochastic Flow ◽  
Production Network ◽  
Flow Networks ◽  
Stochastic Behavior ◽  
Flow Network ◽  
Numerical Examples ◽  
Time Consumption

A stochastic-flow network (SFN) is a network whose flow has stochastic behavior or probabilistic multi-states. A timed stochastic-flow network (TSFN) is a SFN whose flow spends time to go through the network. Traditionally, the evaluation of network reliability does not consider time consumption for the flow to get through the network. However, there are lots of daily-life networks which can be regarded as TSFNs, such as the transportation network, the production network, etc. Their flow spends time to get through the network, and they are not yet explored in the literature. This paper proposes approaches to evaluate the reliability of such networks. Some numerical examples are discussed to illustrate the proposed method.

An Improved Iris Localization Algorithm Based on Curve Fitting

2013 ◽  
Vol 380-384 ◽  
pp. 1176-1179
Author(s):  
Yi Huang ◽  
Xiao Ping Zeng
Keyword(s):  
Curve Fitting ◽  
Outer Boundary ◽  
Regions Of Interest ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Iris Image ◽  
The Third ◽  
Boundary Points ◽  
Maximum Variance ◽  
Improved Algorithm

Iris localization is to detect outer-and-inner boundaries of iris in an iris image. In the paper, an improved algorithm was proposed to quickly and effectively locate outer-and-inner boundaries. As for this algorithm, the first is to block an iris image and extract its sub-image blocks which cover pupil; the second is to set a binary threshold of pupil by adopting the method of Maximum Variance between Clusters; the third is to get the value outer-boundary-points of iris, on the basis of gray gradient of key Regions-of-interest; the last is to select some characteristic pixels in regions of interest respectively and fit outer-and-inner boundaries of iris according to curve fitting.

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