358 research outputs found

    On the Split Reliability of Graphs

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    A common model of robustness of a graph against random failures has all vertices operational, but the edges independently operational with probability pp. One can ask for the probability that all vertices can communicate ({\em all-terminal reliability}) or that two specific vertices (or {\em terminals}) can communicate with each other ({\em two-terminal reliability}). A relatively new measure is {\em split reliability}, where for two fixed vertices ss and tt, we consider the probability that every vertex communicates with one of ss or tt, but not both. In this paper, we explore the existence for fixed numbers nโ‰ฅ2n \geq 2 and mโ‰ฅnโˆ’1m \geq n-1 of an {\em optimal} connected (n,m)(n,m)-graph Gn,mG_{n,m} for split reliability, that is, a connected graph with nn vertices and mm edges for which for any other such graph HH, the split reliability of Gn,mG_{n,m} is at least as large as that of HH, for {\em all} values of pโˆˆ[0,1]p \in [0,1]. Unlike the similar problems for all-terminal and two-terminal reliability, where only partial results are known, we completely solve the issue for split reliability, where we show that there is an optimal (n,m)(n,m)-graph for split reliability if and only if nโ‰ค3n\leq 3, m=nโˆ’1m=n-1, or n=m=4n=m=4.Comment: 12 pages, 9 figure

    The Gross-Saccoman Conjecture is True

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    Consider a graph with perfect nodes but independent edge failures with identical probability ฯ. The reliability is the connectedness probability of the random graph. A graph with n nodes and e edges is uniformly optimally reliable (UOR) if it has the greatest reliability among all graphs with the same number of nodes and edges, for all values of ฯ. In 1997, Gross and Saccoman proved that the simple UOR graphs for e = n, e = n + 1 and e = n + 2 are also optimal when the classes are extended to include multigraphs [6]. The authors conjectured that the UOR simple graphs for e = n + 3 are optimal in multigraphs as well. A proof of the Gross-Saccoman conjecture is introduced.Agencia Nacional de Investigaciรณn e Innovaciรณ

    Reliability of Partial k-tree Networks

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    133 pagesRecent developments in graph theory have shown the importance of the class of partial k- trees. This large class of graphs admits several algorithm design methodologies that render efficient solutions for a large number of problems inherently difficult for general graphs. In this thesis we develop such algorithms to solve a variety of reliability problems on partial k-tree networks with node and edge failures. We also investigate the problem of designing uniformly optimal 2-trees with respect to the 2-terminal reliability measure. We model a. communication network as a graph in which nodes represent communication sites and edges represent bidirectional communication lines. Each component (node or edge) has an associated probability of operation. Components of the network are in either operational or failed state and their failures are statistically independent. Under this model, the reliability of a network G is defined as the probability that a given connectivity condition holds. The l-terminal reliability of G, Rel1 ( G), is the probability that any two of a given set of I nodes of G can communicate. Robustness of a network to withstand failures can be expressed through network resilience, Res( G), which is the expected number of distinct pairs of nodes that can communicate. Computing these and other similarly defined measures is #P-hard for general networks. We use a dynamic programming paradigm to design linear time algorithms that compute Rel1( G), Res( G), and some other reliability and resilience measures of a partial k-tree network given with an embedding in a k-tree (for a fixed k). Reliability problems on directed networks are also inherently difficult. We present efficient algorithms for directed versions of typical reliability and resilience problems restricted to partial k-tree networks without node failures. Then we reduce to those reliability problems allowing both node and edge failures. Finally, we study 2-terminal reliability aspects of 2-trees. We characterize uniformly optimal 2-trees, 2-paths, and 2-caterpillars with respect to Rel2 and identify local graph operations that improve the 2-terminal reliability of 2-tree networks

    Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

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    This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

    Solving the multistage PMU placement problem by integer programming and equivalent network design model

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    Recently, phasor measurement units (PMUs) are becoming widely used to measure the electrical waves on a power grid to determine the health of the system. Because of high expense for PMUs, it is important to place minimized number of PMUs on power grids without losing the function of maintaining system observability. In practice, with a budget limitation at each time point, the PMUs are placed in a multistage framework spanning in a long-term period, and the proposed multistage PMU placement problem is to find the placement strategies. Within each stage for some time point, the PMUs should be placed to maximize the observability and the complete observability should be ensured in the planned last stage. In this paper, the multistage PMU placement problem is formulated by a mixed integer program (MIP) with consideration of the zero-injection bus property in power systems. To improve the computational efficiency, another MIP, based on the equivalent network flow model for the PMU placement problem, is proposed. Numerical experiments on several test cases are performed to compare the two MIPs.12 month embargo; published online: 13 June 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

    High-reliability architectures for networks under stress

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 157-165).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.In this thesis, we develop a methodology for architecting high-reliability communication networks. Previous results in the network reliability field are mostly theoretical in nature with little immediate applicability to the design of real networks. We bring together these contributions and develop new results and insights which are of value in designing networks that meet prescribed levels of reliability. Furthermore, most existing results assume that component failures are statistically independent in nature. We take initial steps in developing a methodology for the design of networks with statistically dependent link failures. We also study the architectures of networks under extreme stress.by Guy E. Weichenberg.S.M

    ์ˆ˜๋„๊ถŒ ํ†ตํ•ฉ๋Œ€์ค‘๊ตํ†ต์ฒด๊ณ„ ๊ตํ†ต์นด๋“œ ์ž๋ฃŒ ๊ธฐ๋ฐ˜ ์ˆ˜์ž…๊ธˆ ์‚ฐ์ • ๋ฐฉ์•ˆ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2022.2. ๊ณ ์Šน์˜.The Integrated Fare System for the Seoul Metropolitan Public Transportation Network) is one in which the metropolises of Seoul, Gyeonggi, Incheon, However the Smartcard data indicates the movement of passengers based on subway terminal IDs. Therefore, it is difficult to analyze the exact route of passengers in the presence of transfer stations. This unknown information in turn leads to the problem of fare calculation and revenue allocation by transportation agencies. In the case of the passenger's undetermined route, the fare is calculated by assuming that the fare is based on the minimum travel distance, not based on the actual route of the passenger. As these routes and fare routes appear as different results, the settlement between bus and subway is established on day-to-day settlement between operating agencies of the current metropolitan integrated fare system. However, settlement between subway operating agencies is carried out every to 5 years by establishing a model that estimates the actual travel route of passengers. Currently, the issue of settlement of subway transport agencies has become a very important role in the operation of transport agencies. Also, due to the agreement on transfer loss compensation between local governments, the situation of the dispute is expanding to a problem that is extended at the government level. This problem is expected to be exacerbated by the entry of new private transportation means such as GTX, light rail, and KTX, and regional expansion of the fare system. This study, estimate the fares charged to passengers as settlement fees, additional fees, separate fees, and independent fees by modeling the minimum time route search and minimum distance route search linking the departure and arrival terminal IDs of the Smartcard. In this case, the minimum time route was assumed to represent the actual movement of passengers and applied as a method for distribution of revenues by transport agencies along with separate fares from privately financed agencies. The minimum distance route was applied to simulate the added fare method currently applied in the subway. As a route search technique, the metropolitan subway was recognized as a network composed of terminal IDs, and Big Node was applied as a station name composed of a set of terminal IDs. Also, due to the need to consider transfers and multiple stages in the subway network, we proposed a method to ensure an optimal solution without network expansion by introducing link-label method. Theories and case studies that are used in the allocation of revenues of actual transportation organizations are presented.์Šค๋งˆํŠธ์นด๋“œ์ž๋ฃŒ๋Š” ์ˆ˜๋„๊ถŒ ์ง€ํ•˜์ฒ ์˜ ์Šน๊ฐ์˜ ์ด๋™์„ ๋‹จ๋ง๊ธฐID๋กœ ๋‚˜ํƒ€๋‚ด๊ธฐ ๋•Œ๋ฌธ์— ํ™˜์Šน์—ญ์ด ์กด์žฌํ•˜๋Š” ์ƒํ™ฉ์—์„œ ์Šน๊ฐ์˜ ์ด๋™๊ฒฝ๋กœ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ์ง€ํ•˜์ฒ  ์Šน๊ฐ์˜ ์ด๋™์€ ์š”๊ธˆ์‚ฐ์ • ๋ฐ ์šด์†ก๊ธฐ๊ด€์˜ ์ˆ˜์ž…๊ธˆ๋ฐฐ๋ถ„๊ณผ ๊ด€๋ จ๋˜์–ด ์Šน๊ฐ์ด๋™๊ฒฝ๋กœ์˜ ๋ฏธํ™•์ •๋ฌธ์ œ๊ฐ€ ํ™•๋Œ€๋˜๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์Šค๋งˆํŠธ์นด๋“œ์˜ ์ถœ๋ฐœ-๋„์ฐฉ ๋‹จ๋ง๊ธฐID๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” ์ตœ์†Œ์‹œ๊ฐ„๊ฒฝ๋กœํƒ์ƒ‰๊ณผ ์ตœ์†Œ๊ฑฐ๋ฆฌ๊ฒฝ๋กœํƒ€์ƒ‰์„ ์‹œํ–‰ํ•˜์—ฌ ์Šน๊ฐ์—๊ฒŒ ๋ถ€๊ณผ๋˜๋Š” ์š”๊ธˆ์„ ์ถ”๊ฐ€์š”๊ธˆ, ๋ณ„๋„์š”๊ธˆ์œผ๋กœ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ•˜์˜€๋‹ค. ์ด๋•Œ ์ตœ์†Œ์‹œ๊ฐ„๊ฒฝ๋กœ๋Š” ์Šน๊ฐ์˜ ์‹ค์ œ์ด๋™์„ ๋Œ€๋ณ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ฐ€์ •ํ•˜๊ณ  ๋ฏผ์ž๊ธฐ๊ด€์˜ ๋ณ„๋„์š”๊ธˆ๊ณผ ํ•จ๊ป˜ ์šด์†ก๊ธฐ๊ด€์˜ ์ˆ˜์ž…๊ธˆ๋ฐฐ๋ถ„ ๋ฐฉ์•ˆ์œผ๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ์ตœ์†Œ๊ฑฐ๋ฆฌ๊ฒฝ๋กœ๋Š” ํ˜„์žฌ ์ง€ํ•˜์ฒ ์—์„œ ์ ์šฉํ•˜๋Š” ์ถ”๊ฐ€์š”๊ธˆ๋ถ€๊ณผ๋ฐฉ์‹์„ ๋ชจ์‚ฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ ์šฉํ•˜์˜€๋‹ค. ๊ฒฝ๋กœํƒ์ƒ‰๊ธฐ๋ฒ•์œผ๋กœ ์ˆ˜๋„๊ถŒ ์ง€ํ•˜์ฒ ์„ ๋‹จ๋ง๊ธฐID๋กœ ๊ตฌ์„ฑ๋œ ๋„คํŠธ์›Œํฌ๋กœ ์ธ์‹ํ•˜๊ณ  ๋น…๋…ธ๋“œ๋ฅผ\ ๋‹จ๋ง๊ธฐID ์ง‘ํ•ฉ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์—ญ์‚ฌ๋ช…์œผ๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋น…๋„๋“œ๋กœ ๊ตฌ์ถ•๋œ ์ง€ํ•˜์ฒ  ๋„คํŠธ์›Œํฌ๊ฐ€ ํ™˜์Šน๊ณผ ๋‹ค์ˆ˜๋‹จ์„ ๊ณ ๋ คํ•  ํ•„์š”์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ๋งํฌํ‘œ์ง€๋ฅผ ๋„์ž…ํ•˜์—ฌ ๋„คํŠธ์›Œํฌํ™•์žฅ์—†์ด ์ตœ์ ํ•ด๋ฅผ ๋ณด์žฅํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ํƒ์ƒ‰๋œ ์ตœ์†Œ๊ฑฐ๋ฆฌ ๋ฐ ์‹œ๊ฐ„๊ฒฝ๋กœ๋ฅผ ํ†ตํ•˜์—ฌ ์š”๊ธˆ์„ ์‚ฐ์ •ํ•˜๊ณ  ๋ˆ„๋ฝ๋œ ๋ฏผ์ž๊ธฐ๊ด€์˜ ๋ณ„๋„์š”๊ธˆ์„ ๊ฐœ์„ ํ•˜๋ฉฐ ๋ฌด์ž„์Šน์ฐจ์— ๋Œ€ํ•œ ์šด์†ก๊ธฐ๊ด€์˜ ์ˆ˜์ž…๊ธˆ ๋ฐฐ๋ถ„๊ธฐ์—ฌ๋„๋ฅผ ์‚ฐ์ •ํ•˜์—ฌ ์‚ฌ๋ก€์—ฐ๊ตฌ๋กœ์„œ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 4 Chapter 2. Literature Review 6 2.1. Metropolitan Smart Card Data 6 2.2. Integrated Fare System for the Metropolitan Public Transportation Network 9 2.3. Limits of the Smart Card Data 12 2.4. Review of related literature on Fare and Revenue allocation using Smart Card Data 17 2.5. International cases of Transit Fare and Revenue allocation 19 2.6. Smart Card based Subway Route Choice Method 20 2.7. Subway Information Provision Platform 26 2.8. Research Contribution 27 Chapter 3. Methodology 29 3.1. Smart Card Data Trip Chain Subway Mode Route Choice Model 29 3.2. Smart Card Data Trip Chain Fare Calculation Model 34 3.3. Smart Card Data Trip Chain IFS Revenue Calculation Model 35 3.4. Smart Card Data Trip Chain Subway Revenue Allocation Model 36 Chapter 4. Result 37 4.1. Input data 37 4.2. Subway Passenger Route, Fare, and Revenue Allocation 43 4.3. Smart Card Data Trip Chain Fare Analysis 45 4.4. Verification of Optimal Route Search Accuracy 47 4.5. Estimation of omitted extra fare for subway private agencies 48 4.6. Case Stuy 49 Chapter 5. Conclusion 75 5.1. Conclusion 75 5.2. Future Research 76 Bibliography 77 Abstract in Korean 80๋ฐ•
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