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Книги з теми "Availability Modeling"

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Availability Modeling.

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

), Travel Model Improvement Program (U S. Vehicle availability modeling. [Washington, D.C.?]: U.S. Dept of Transportation, Federal Highway Administration, Federak Transit Administration, Bureau of Transportation Statistics, Addistance Secretary for Transportation Policy, 2000.

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2

Schneeweiss, Winfrid G. Reliability modeling: (modeling in the fields of dependability, availability, maintainability, and safety engineering). Hagen: LiLoLe-Verlag, 2001.

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3

French, Timothy F. Comparison of operational availability modeling by TIGER and SESAME. Monterey, Calif: Naval Postgraduate School, 1994.

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4

Vergnaud, J. M. Assessing bioavailability of drug delivery systems: Mathematical modeling. Boca Raton, FL: Taylor & Francis/CRC Press, 2005.

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5

Wilson, Steven D. Hydrogeology and ground-water availability in southwest McLean and southeast Tazewell Counties: Aquifer modeling and final report. Champaign, IL: Illinois State Water Survey, 1998.

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6

United States. Environmental Protection Agency. Office of Transportation and Air Quality. EPA's approval of the MOVES2010a and EMFAC2007 emissions models for transportation conformity hot-spot analyses, and availability of modeling guidance: Questions and answers. 2nd ed. Washington, D.C.]: U.S. Environmental Protection Agency, Office of Transportation and Air Quality, 2010.

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7

Shooman, Martin L. Computer aided reliability, availability, and safety modeling for fault-tolerant computer systems with commentary on the HARP program: Final report research grant, NAG-1-1001. Farmingdale, NY: School of Electrical Engineering and Computer Science, Dept. of Computer Science, Long Island Center, 1991.

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8

Trivedi, Kishor S., and Andrea Bobbio. Reliability and Availability Engineering: Modeling, Analysis, and Applications. Cambridge University Press, 2017.

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9

Trivedi, Kishor S., and Andrea Bobbio. Reliability and Availability Engineering: Modeling, Analysis, and Applications. Cambridge University Press, 2017.

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10

Trivedi, Kishor S., and Andrea Bobbio. Reliability and Availability Engineering: Modeling, Analysis, and Applications. Cambridge University Press, 2017.

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11

Maciel, Paulo Romero Martins. Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2: Reliability, Availability Modeling, Measuring, and Data Analysis. CRC Press LLC, 2022.

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12

Maciel, Paulo Romero Martins. Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2: Reliability, Availability Modeling, Measuring, and Data Analysis. Taylor & Francis Group, 2022.

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13

Maciel, Paulo Romero Martins. Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2: Reliability, Availability Modeling, Measuring, and Data Analysis. Taylor & Francis Group, 2022.

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14

Maciel, Paulo Romero Martins. Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2: Reliability, Availability Modeling, Measuring, and Data Analysis. Taylor & Francis Group, 2022.

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15

Modeling Diminishing Marginal Returns: An Application to the Aircraft Availability Model. Storming Media, 1996.

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16

Basu, Sanjay. Modeling in R. Edited by Sanjay Basu. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.003.0007.

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Spreadsheet are straightforward to work with and do not require extensive knowledge of programming; they are easy to communicate with among people with varying levels of expertise; are convenient for optimization problems, given the availability of prepackaged Solver algorithms; and are commonly used for modeling in business management, nonprofit organization budgeting, consulting, and academia. But spreadsheets have their limitations, particularly when modeling large populations, long time periods, or complex situations with many states or conditions and equations. For larger scale models that need more flexibility than can be offered by a spreadsheet, the free statistical program R provides a straightforward approach to modeling and has the advantages of being free, fast, available on any operating system, commonly used, and widely supported by an online community. This chapter provides a detailed introduction to R that will be useful for the more advanced modeling methods introduced in Chapters 8 through 11.

17

An Analysis of Operational Availability of Brazilian Navy and Argentine Air Force A-4 Fleets Using Simulation Modeling. Storming Media, 1999.

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18

National Aeronautics and Space Administration (NASA) Staff. Computer Aided Reliability, Availability, and Safety Modeling for Fault-Tolerant Computer Systems with Commentary on the Harp Program. Independently Published, 2018.

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19

Hatton, TJ. Catchment Scale Recharge Modelling - Part 4. CSIRO Publishing, 1998. http://dx.doi.org/10.1071/9780643105362.

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This paper addresses the need to model recharge to groundwater systems at the scale of whole catchments. It looks at developing the right conceptual model of how water moves through a given landscape for both homogeneous and heterogeneous catchments. One-dimensional recharge models and three-dimensional recharge models are considered. Discussion of which recharge modelling approach to use take in consideration of the availability of data, the nature of the questions being asked, and the expertise of the investigators.

20

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

21

Maoz, Zeev. The Past and Future of the Scientific Study of International Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190846626.013.341.

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The scientific study of international processes (SSIP) has made substantial progress over the past twenty years, establishing itself as the mainstream research community in the field of international relations (IR) and attracting more and more attention from other disciplines. This was due to the convergence of several revolutions that have taken place in the field, including the data revolution, the formal modeling revolution, the methods revolution, the substantive revolution, and the epistemological revolution. In addition to the dramatic increase in the number of the community of scholars who use scientific logic, systematic methods, and empirical data to study IR, there was a significant improvement in the quality of research. This research has yielded important contributions to our understanding of international processes. Some of these contributions went far beyond the field; they have attracted the attention of policy makers as well as quite a few scholars from other disciplines. Some of the key findings that emerged from this research have become—correctly or incorrectly—a key component of the discourse of political leaders. Growing data availability, increased methodological sophistication, and greater scientific discipline within the profession have converged to open new research frontiers, but important challenges remain, such as the disconnect between theory and empirical tests that exists in many cases, and the almost exclusive reliance on the dyadic level of analysis. It is important to make our understanding of international processes translated into broader policy implications.

22

Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.

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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in improving model projections is comparisons with observations. Exceeding uncertainties associated with climate model simulation are uncertainties due to projections of future emissions of CO2 and other greenhouse gases. Humanity’s choices in emissions pathways will have profound effects on climate, especially after the mid-century.The African Sahel is a transition zone characterized by strong meridional precipitation and temperature gradients. Over West Africa, the Sahel marks the northernmost extent of the West African monsoon system. The region’s climate is known to be sensitive to sea surface temperatures, both regional and global, as well as to land surface conditions. Increasing atmospheric greenhouse gases are already causing amplified warming over the Sahara Desert and, consequently, increased rainfall in parts of the Sahel. Climate model projections indicate that much of this increased rainfall will be delivered in the form of more intense storm systems.The complicated and highly regional precipitation regimes of East Africa present a challenge for climate modeling. Within roughly 5º of latitude of the equator, rainfall is delivered in two seasons—the long rains in the spring, and the short rains in the fall. Regional climate model projections suggest that the long rains will weaken under greenhouse gas forcing, and the short rains season will extend farther into the winter months. Observations indicate that the long rains are already weakening.Changes in seasonal rainfall over parts of subtropical southern Africa are observed, with repercussions and challenges for agriculture and water availability. Some elements of these observed changes are captured in model simulations of greenhouse gas-induced climate change, especially an early demise of the rainy season. The projected changes are quite regional, however, and more high-resolution study is needed. In addition, there has been very limited study of climate change in the Congo Basin and across northern Africa. Continued efforts to understand and predict climate using higher-resolution simulation must be sustained to better understand observed and projected changes in the physical processes that support African precipitation systems as well as the teleconnections that communicate remote forcings into the continent.