Warm standby sparing is a fault-tolerance technique that attempts to improve system reliability while compromising the system energy consumption and recovery time. However, when the imperfect fault coverage effect (an...
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Warm standby sparing is a fault-tolerance technique that attempts to improve system reliability while compromising the system energy consumption and recovery time. However, when the imperfect fault coverage effect (an uncovered component fault can propagate and cause the whole system to fail) is considered, the reliability of a warm standby sparing can decrease with an increasing level of the redundancy. This article studies the reliability of a warm standby sparing subject to imperfect fault coverage, in particular, fault level coverage where the coverage probability of a component depends on the number of failed components in the system. The suggested approach is combinatorial and based on a generalized binary decision diagrams technique. The complexity for the binary decision diagram construction is analyzed, and several case studies are given to illustrate the application of the approach.
Propagated failures between components mean that the failure of one component may cause the failures of other components within the system. Because propagated failures contribute greatly to the system unavailability, ...
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Propagated failures between components mean that the failure of one component may cause the failures of other components within the system. Because propagated failures contribute greatly to the system unavailability, it is important to take into account propagated failures between components when modeling systems. Sometimes, it may be difficult to obtain the precise values of some parameters due to many reasons, such as the insufficiency of historical data and the randomness of statistical data. In this case, interval values can be given instead of precise values to represent the parametric uncertainty related to the values of parameters. This paper proposes explicit and implicit binary decision diagram-based methods to model systems subject to propagated failures and to evaluate the failure probability of systems under parametric uncertainty. The proposed methods are applied to a near head-to-head railway accident to evaluate the occurrence probability of accidents.
the primary aim of computer science is invention of new data structures and algorithms. Those data structures and algorithms could significantly help us to solve unsolved problems or let us give much better solutions ...
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ISBN:
(纸本)9780769535913
the primary aim of computer science is invention of new data structures and algorithms. Those data structures and algorithms could significantly help us to solve unsolved problems or let us give much better solutions for the already solved problems. In this paper we introduce BDD (binary decision diagram) and its variants OBDD (Ordered binary decision diagram) and ROBDD (Reduced Ordered binary decision diagram). Then we had a review on applications of BDD and it's variants to solving some problems. Finally we will introduce two another decisiondiagrams.
This paper proposes an on the fly algorithm for graph distribution. The algorithm uses a new distributed method combined with the binary decision diagram (BDD) for holding a global image of the system. Though, the pro...
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ISBN:
(纸本)9780956715753
This paper proposes an on the fly algorithm for graph distribution. The algorithm uses a new distributed method combined with the binary decision diagram (BDD) for holding a global image of the system. Though, the proposed approach is a distributed algorithm, it uses little or none communication between the different nodes(sites). Hence it increases the fault tolerance of an unreliable network. In addition it preserves the workload balancing property. As a result, the proposed algorithm attempts to balance the workload between the different sites of the network and reduces as much as possible the inter-processors communications overhead result of reducing the inter-sites edges.
Sequential pattern mining is an important problem in data mining. State of the art techniques for mining sequential patterns, such as frequent subsequences, are often based on the pattern-growth approach, which recurs...
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Sequential pattern mining is an important problem in data mining. State of the art techniques for mining sequential patterns, such as frequent subsequences, are often based on the pattern-growth approach, which recursively projects conditional databases. Explicitly creating database projections is thought to be a major computational bottleneck, but we will show in this paper that it can be beneficial when the appropriate data structure is used. Our technique uses a canonical directed acyclic graph as the sequence database representation, which can be represented as a binary decision diagram (BDD). In this paper, we introduce a new type of BDD, namely a sequence BDD (SeqBDD), and show how it can be used for efficiently mining frequent subsequences. A novel feature of the SeqBDD is its ability to share results between similar intermediate computations and avoid redundant computation. We perform an experimental study to compare the SeqBDD technique with existing pattern growth techniques, that are based on other data structures such as prefix trees. Our results show that a SeqBDD can be half as large as a prefix tree, especially when many similar sequences exist. In terms of mining time, it can be substantially more efficient when the support is low, the number of patterns is large, or the input sequences are long and highly similar.
A state space model for exact analysis of discrete time heterogeneous general standby systems applicable for hot, warm, and cold backups in any combination of them is pro-posed. The systems have multistate components ...
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A state space model for exact analysis of discrete time heterogeneous general standby systems applicable for hot, warm, and cold backups in any combination of them is pro-posed. The systems have multistate components whose lifetimes follow independent dis-crete phase-type distributions. The approach is by incorporating a deceleration matrix into the survival matrix of a component while as a backup and proving that such an approach results in a discrete phase-type representation of the system lifetime distribution. The method is applicable for dynamic reliability analyses of general structure systems having combinations of series, parallel, and standby structures. A binary decision diagram model for the same systems is also proposed by representing a multistate component as a single node. The performance of the two models in generating system reliability measures are compared numerically and qualitatively. Applications for a real-world system and finding optimal backup orderings are given.(c) 2022 Elsevier Inc. All rights reserved.
As the central nervous system of modern information warfare, the command-and-control network becomes the brain of command and the strategic target of the enemy's primary attack during the operation. In this paper,...
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As the central nervous system of modern information warfare, the command-and-control network becomes the brain of command and the strategic target of the enemy's primary attack during the operation. In this paper, a method based on edge extension diagram (EED) and binary decision diagram (BDD) is proposed to evaluate the reliability of connectivity within the basic command post of command-and-control network, which is solving the problem that the conventional methods failed to model failure probabilities of node links and with low calculation efficiency. First, graph simplification of network topology, extension of nodes under reliable state, construction of path functions, replacement of associated edges, variable sorting, construction of binarydecision graphs, and recursive calculation to evaluate the reliability of the connection between the two ends of the command-and-control network. Then, further analyze and sort the variable importance of the command-and-control network and analyze the sensitivity of the network topology. This paper contributes to the reliability assessment of command-and-control network system, the identification and maintenance of key network nodes, and the design of network topology structure.
Multi-state systems (MSS) are systems in which both the systems, and/or their components may exhibit multiple performance levels or states. MSS can model complex behaviors such as shared loads, performance degradation...
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Multi-state systems (MSS) are systems in which both the systems, and/or their components may exhibit multiple performance levels or states. MSS can model complex behaviors such as shared loads, performance degradation, imperfect fault coverage, standby redundancy, and limited repair resources. The non-binary state property of MSS, and their components makes the analysis of MSS challenging. In this paper, we propose efficient logarithmically-encoded binary decision diagram (LBDD)-based methods for analysing MSS. The application and advantages of the proposed LBDD-based approaches, as compared to the existing binary decision diagram-based approaches, are demonstrated through the analyses of practical MSS examples, and a set of benchmark examples.
binary decision diagram (BDD) has been shown to be an efficient data structure for image coding. This paper presents novel manipulation techniques for BDD encoded image. Image translation can be expressed as operation...
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binary decision diagram (BDD) has been shown to be an efficient data structure for image coding. This paper presents novel manipulation techniques for BDD encoded image. Image translation can be expressed as operation of BDDs. The concept of transition branch which represents displacement of translation is introduced. In addition to image translation, a technique to perform orthogonal rotation of image encoded by BDD is presented. These geometric transformations are applied directly on BDD representation of image. (c) 2007 Elsevier B.V. All rights reserved.
The distributed operating room (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this pr...
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The distributed operating room (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this problem where surgery durations are considered to be uncertain. In order to obtain solutions for the challenging stochastic model, we use sample average approximation and develop two enhanced decomposition frameworks that use logic-based Benders (LBBD) optimality cuts and binary decision diagram based Benders cuts. Specifically, to the best of our knowledge, deriving LBBD optimality cuts in a stochastic programming context is new to the literature. Our computational experiments on a hospital data set illustrate that the stochastic formulation generates robust schedules and that our algorithms improve the computational efficiency. Summary of Contribution: We propose a new model for an important problem in healthcare scheduling, namely, stochastic distributed operating room scheduling, which is inspired by a current practice in Toronto, Ontario, Canada. We develop two decomposition methods that are computationally faster than solving the model directly via a state-of-the-art solver. We present both some theoretical results for our algorithms and numerical results for the evaluation of the model and algorithms. Compared with its deterministic counterpart in the literature, our model shows improvement in relevant evaluation metrics for the underlying scheduling problem. In addition, our algorithms exploit the structure of the model and improve its solvability. Those algorithms also have the potential to be used to tackle other planning and scheduling problems with a similar structure.
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