The Experts below are selected from a list of 119550 Experts worldwide ranked by ideXlab platform
Wei-chang Yeh - One of the best experts on this subject based on the ideXlab platform.
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a new bat for acyclic multistate information Network Reliability evaluation
arXiv: Networking and Internet Architecture, 2020Co-Authors: Wei-chang YehAbstract:The acyclic multistate information Network (AMIN), which is a kind of MIN that does not require the conservation law of flow, plays an important role nowadays because many modern Network structures present AMIN as the construction such as social Networks, local area Networks (LANs), 4G/5G Networks, etc. To effectively evaluate the Network Reliability of AMIN, which indicates the reliable operation of the Network, showing a major and primary metrics for determining the performance and quality of the overall Network. The Network Reliability, which has been shown a NP-hard, has been successfully resolved and approached by the universal generation function method (UGFM). However, the UGFM can only solve small-scale problems due to the overflow in computer memory. To overcome the memory obstacle, an improved and enhanced binary-addition vectors tree algorithm (BAT) is proposed to effectively evaluate and analyze the Reliability of AMIN. The performance of the proposed BAT is validated on examples.
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Fuzzy System and Time Window Applied to Traffic Service Network Problems under a Multi-Demand Random Network
MDPI AG, 2019Co-Authors: Chialing Huang, Sin-yuan Huang, Wei-chang Yeh, Jinhai WangAbstract:The transportation Network promotes key human development links such as social production, population movement and resource exchange. As cities continue to expand, transportation Networks become increasingly complex. A bad traffic Network design will affect the quality of urban development and cause regional economic losses. How to plan transportation routes and allocate transportation resources is an important issue in today’s society. This study uses the Network Reliability method to solve traffic Network problems. Network Reliability refers to the probability of a successful connection between the source and sink nodes in the Network. There are many systems in the world that use Network architecture; therefore, Network Reliability is widely used in various practical problems and cases. In the past, some scholars have used Network Reliability to solve traffic service Network problems. However, the processing of time is not detailed enough to fully express the real user’s time requirements and does not consider that the route traffic will affect the Reliability of the entire Network. This study improves on past Network Reliability methods by using a fuzzy system and a time window to construct a Network model. Using the concept of fuzzy systems, according to past experience, data or expert predictions to define the degree of flow, time and Reliability, can also determine the relationship between these factors. The time window can be adjusted according to the time limit in reality, reaching the limit of the complete expression time. In addition, the Network Reliability algorithm used in this study is a direct algorithm. Compared with the past indirect algorithms, the computation time is greatly reduced and complex problems can be solved more efficiently
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a modified universal generating function algorithm for the acyclic binary state Network Reliability
IEEE Transactions on Reliability, 2012Co-Authors: Wei-chang YehAbstract:Network Reliability is an important part of planning, designing, and controlling Networks. Now, the most general binary-state Network (BSN) Reliability evaluation methods are based on Minimal Paths (MPs), or Minimal Cuts (MCs). The universal generating function method (UGFM) is a novel, efficient scheme for determining Network Reliability. Because the current best-known UGFM can only search for all MPs, it needs to be coupled with another routine such as Sum-of-Disjoint-Product method to calculate the final flow Network Reliability in terms of obtained MPs. In this study, a straightforward, novel UGFM is presented for calculating the acyclic BSN (ABSN) Reliability between the source node and the sink node (i.e. one-to-one Reliability). The proposed method is the first UGFM for the ABSN Reliability problem without searching for all MPs in advance, which can reduce computational complexity. The computational complexity of the proposed algorithm is analysed, and its efficiency is well illustrated by a numerical example.
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an improved method for multistate flow Network Reliability with unreliable nodes and a budget constraint based on path set
Systems Man and Cybernetics, 2011Co-Authors: Wei-chang YehAbstract:Evaluating multistate flow Network Reliability and reducing system cost are important tasks when planning and designing systems. Existing methods are based on (d, c)-minimal paths ((d, c)-MP), which are vectors, such that d units of flow transmit between two specified nodes with a total cost that does not exceed c. However, these methods only work for directed Networks. This correspondence paper finds all (d, c) -MPs before calculating Network Reliability under budget constraints using a novel method. The proposed algorithm is easier to understand and implement and is superior to existing algorithms. This correspondence paper analyzes and proves the correctness of the proposed algorithm, using two examples to demonstrate how to generate, verify, and implement all (d, c)-MPs to solve multistate flow Network reliabilities under budget constraints using the proposed algorithm.
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multistate Network Reliability evaluation under the maintenance cost constraint
International Journal of Production Economics, 2004Co-Authors: Wei-chang YehAbstract:Abstract Many real-world systems (such as electric power, transportation, etc.) are multistate systems composed of multistate components. Such systems may be regarded as flow Networks whose arcs have independent, discrete, limited and multivalued random capacities. Their Reliability can be computed in terms of minimal cut (MC) vectors to level (d,c) (named (d,c)-MCs here), using the probability that d units of flow can be transmitted from the source node to the sink node such that the total maintenance cost of each d-MCs is less than or equal to c. In this study, all MCs are assumed to be known in advance and we developed an intuitive algorithm based on some simple concepts that were found in this study to find the entire (d,c)-MCs before calculating the Reliability value of a Network. One example is illustrated to show how all (d,c)-MCs are generated by the proposed algorithm. Then the Reliability of this example is computed. The computational complexity of the proposed algorithm is also analyzed.
J B Dugan - One of the best experts on this subject based on the ideXlab platform.
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a continuous time bayesian Network Reliability modeling and analysis framework
IEEE Transactions on Reliability, 2006Co-Authors: Hichem Boudali, J B DuganAbstract:We present a continuous-time Bayesian Network (CTBN) framework for dynamic systems Reliability modeling and analysis. Dynamic systems exhibit complex behaviors and interactions between their components; where not only the combination of failure events matters, but so does the sequence ordering of the failures. Similar to dynamic fault trees, the CTBN framework defines a set of 'basic' BN constructs that capture well-defined system components' behaviors and interactions. Combining, in a structured way, the various 'basic' Bayesian Network constructs enables the user to construct, in a modular and hierarchical fashion, the system model. Within the CTBN framework, one can perform various analyses, including Reliability, sensitivity, and uncertainty analyses. All the analyses allow the user to obtain closed-form solutions.
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a discrete time bayesian Network Reliability modeling and analysis framework
Reliability Engineering & System Safety, 2005Co-Authors: Hichem Boudali, J B DuganAbstract:Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel Reliability modeling and analysis framework based on the Bayesian Network (BN) formalism. The overall approach is to investigate timed Bayesian Networks and to find a suitable Reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN Reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based Reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis.
David R. Karger - One of the best experts on this subject based on the ideXlab platform.
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a phase transition and a quadratic time unbiased estimator for Network Reliability
Symposium on the Theory of Computing, 2020Co-Authors: David R. KargerAbstract:We improve the time for approximating Network (un)Reliability to (n2). We do so not with a new algorithm, but with a deeper analysis and tweaking of algorithms from our previous work. In particular, we show that once a graph’s failure probability shrinks below 1/2, the graph rapidly transitions to a regime where even the expected number of cut failures is small, and in fact is almost exactly the same as the graph’s failure probability. That is, we are very unlikely to ever see more than one cut fail. This lets us treat these cut failures as essentially independent, making it easier to estimate their likelihood. The contribution of this paper is not just the improved time bound, but also this clearer understanding of the evolution of a graph’s Reliability. Our results rely on some new methods for analyzing the distribution of cut failures conditioned on the failure of a particular cut, as well as new insights into the evolution of a graph’s connectivity as edges are randomly added over time. Some of our results apply more broadly, to all monotone Reliability systems.
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a randomized fully polynomial time approximation scheme for the all terminal Network Reliability problem
Siam Review, 2001Co-Authors: David R. KargerAbstract:The classic all-terminal Network Reliability problem posits a graph, each of whose edges fails independently with some given probability. The goal is to determine the probability that the Network becomes disconnected due to edge failures. This problem has obvious applications in the design of communication Networks. Since the problem is ${\sharp {\cal P}}$-complete and thus believed hard to solve exactly, a great deal of research has been devoted to estimating the failure probability. In this paper, we give a fully polynomial randomized approximation scheme that, given any n-vertex graph with specified failure probabilities, computes in time polynomial in n and $1/\epsilon$ an estimate for the failure probability that is accurate to within a relative error of $1\pm\epsilon$ with high probability. We also give a deterministic polynomial approximation scheme for the case of small failure probabilities. Some extensions to evaluating probabilities of $k$-connectivity, strong connectivity in directed Eulerian graphs and $r$-way disconnection, and to evaluating the Tutte polynomial are also described. This version of the paper corrects several errata that appeared in the previous journal publication [D. R. Karger, SIAM J. Comput., 29 (1999), pp. 492--514].
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a randomized fully polynomial time approximation scheme for the all terminal Network Reliability problem
SIAM Journal on Computing, 1999Co-Authors: David R. KargerAbstract:The classic all-terminal Network Reliability problem posits a graph, each of whose edges fails independently with some given probability. The goal is to determine the probability that the Network becomes disconnected due to edge failures. This problem has obvious applications in the design of communication Networks. Since the problem is $\SP$-complete and thus believed hard to solve exactly, a great deal of research has been devoted to estimating the failure probability. In this paper, we give a fully polynomial randomized approximation scheme that, given any n-vertex graph with specified failure probabilities, computes in time polynomial in n and $1/\epsilon$ an estimate for the failure probability that is accurate to within a relative error of $1\pm\epsilon$ with high probability. We also give a deterministic polynomial approximation scheme for the case of small failure probabilities. Some extensions to evaluating probabilities of k-connectivity, strong connectivity in directed Eulerian graphs and r-way disconnection, and to evaluating the Tutte polynomial are also described.
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a randomized fully polynomial time approximation scheme for the all terminal Network Reliability problem
Symposium on the Theory of Computing, 1995Co-Authors: David R. KargerAbstract:The classic all-terminal Network Reliability problem posits a graph, each of whose edges fails independently with some given probability. The goal is to determine the probability that the Network becomes disconnected due to edge failures. This problem has obvious ap- plications in the design of communication Networks. Since the problem isP-complete and thus believed hard to solve exactly, a great deal of research has been devoted to estimating the failure probability. In this paper, we give a fully polynomial randomized approxima- tion scheme that, given any n-vertex graph with specified failure probabilities, computes in time polynomial in n and 1/� an estimate for the failure probability that is accurate to within a relative error of 1 ± � with high probability. We also give a deterministic polyno- mial approximation scheme for the case of small failure probabilities. Some extensions to evaluating probabilities of k-connectivity, strong connectivity in directed Eulerian graphs and r-way disconnection, and to evaluating the Tutte polynomial are also described. This version of the paper corrects several errata that appeared in the previous journal publication (D. R. Karger, SIAM J. Comput., 29 (1999), pp. 492-514).
Hichem Boudali - One of the best experts on this subject based on the ideXlab platform.
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a continuous time bayesian Network Reliability modeling and analysis framework
IEEE Transactions on Reliability, 2006Co-Authors: Hichem Boudali, J B DuganAbstract:We present a continuous-time Bayesian Network (CTBN) framework for dynamic systems Reliability modeling and analysis. Dynamic systems exhibit complex behaviors and interactions between their components; where not only the combination of failure events matters, but so does the sequence ordering of the failures. Similar to dynamic fault trees, the CTBN framework defines a set of 'basic' BN constructs that capture well-defined system components' behaviors and interactions. Combining, in a structured way, the various 'basic' Bayesian Network constructs enables the user to construct, in a modular and hierarchical fashion, the system model. Within the CTBN framework, one can perform various analyses, including Reliability, sensitivity, and uncertainty analyses. All the analyses allow the user to obtain closed-form solutions.
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a discrete time bayesian Network Reliability modeling and analysis framework
Reliability Engineering & System Safety, 2005Co-Authors: Hichem Boudali, J B DuganAbstract:Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel Reliability modeling and analysis framework based on the Bayesian Network (BN) formalism. The overall approach is to investigate timed Bayesian Networks and to find a suitable Reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN Reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based Reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis.
Tadashi Wadayama - One of the best experts on this subject based on the ideXlab platform.
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probabilistic analysis of the Network Reliability problem on a random graph ensemble
International Symposium on Information Theory and its Applications, 2012Co-Authors: Akiyuki Yano, Tadashi WadayamaAbstract:In the field of computer science, the Network Reliability problem for evaluating the Network failure probability has been extensively investigated. For a given undirected graph G, the Network failure probability is the probability that edge failures (i.e., edge erasures) make G unconnected. Edge failures are assumed to occur independently with the same probability. The main contributions of the present paper are the upper and lower bounds on the expected Network failure probability. We herein assume a simple random graph ensemble that is closely related to the Erdős-Renyi random graph ensemble. These upper and lower bounds exhibit the typical behavior of the Network failure probability. The proof is based on the fact that the cutset space of G is a linear space over F 2 spanned by the incident matrix of G. The present study shows a close relationship between the ensemble analysis of the expected Network failure probability and the ensemble analysis of the average weight distribution of LDGM codes with column weight 2.
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probabilistic analysis of the Network Reliability problem on a random graph ensemble
International Symposium on Information Theory and its Applications, 2012Co-Authors: Akiyuki Yano, Tadashi WadayamaAbstract:In the field of computer science, the Network Reliability problem for evaluating the Network failure probability has been extensively investigated. For a given undirected graph G, the Network failure probability is the probability that edge failures (i.e., edge erasures) make G unconnected. Edge failures are assumed to occur independently with the same probability. The main contributions of the present paper are the upper and lower bounds on the expected Network failure probability. We herein assume a simple random graph ensemble that is closely related to the Erdős-Renyi random graph ensemble. These upper and lower bounds exhibit the typical behavior of the Network failure probability. The proof is based on the fact that the cutset space of G is a linear space over F 2 spanned by the incident matrix of G. The present study shows a close relationship between the ensemble analysis of the expected Network failure probability and the ensemble analysis of the average weight distribution of LDGM codes with column weight 2.
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probabilistic analysis of the Network Reliability problem on a random graph ensemble
arXiv: Information Theory, 2011Co-Authors: Akiyuki Yano, Tadashi WadayamaAbstract:In the field of computer science, the Network Reliability problem for evaluating the Network failure probability has been extensively investigated. For a given undirected graph $G$, the Network failure probability is the probability that edge failures (i.e., edge erasures) make $G$ unconnected. Edge failures are assumed to occur independently with the same probability. The main contributions of the present paper are the upper and lower bounds on the expected Network failure probability. We herein assume a simple random graph ensemble that is closely related to the Erd\H{o}s-R\'{e}nyi random graph ensemble. These upper and lower bounds exhibit the typical behavior of the Network failure probability. The proof is based on the fact that the cut-set space of $G$ is a linear space over $\Bbb F_2$ spanned by the incident matrix of $G$. The present study shows a close relationship between the ensemble analysis of the Network failure probability and the ensemble analysis of the error detection probability of LDGM codes with column weight 2.
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probabilistic analysis of the Network Reliability problem on a random graph ensemble
arXiv: Information Theory, 2011Co-Authors: Akiyuki Yano, Tadashi WadayamaAbstract:In the field of computer science, the Network Reliability problem for evaluating the Network failure probability has been extensively investigated. For a given undirected graph $G$, the Network failure probability is the probability that edge failures (i.e., edge erasures) make $G$ unconnected. Edge failures are assumed to occur independently with the same probability. The main contributions of the present paper are the upper and lower bounds on the expected Network failure probability. We herein assume a simple random graph ensemble that is closely related to the Erd\H{o}s-R\'{e}nyi random graph ensemble. These upper and lower bounds exhibit the typical behavior of the Network failure probability. The proof is based on the fact that the cut-set space of $G$ is a linear space over $\Bbb F_2$ spanned by the incident matrix of $G$. The present study shows a close relationship between the ensemble analysis of the Network failure probability and the ensemble analysis of the error detection probability of LDGM codes with column weight 2.