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Книги з теми "Non-linear dynamic analyses"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Singh, Rajendra. Non-linear dynamic analysis of geared systems. [Columbus, Ohio]: The Ohio State University, Dept. of Mechanical Engineering, 1990.

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2

Singh, Rajendra. Non-linear dynamic analysis of geared systems. [Columbus, Ohio]: The Ohio State University, Dept. of Mechanical Engineering, 1990.

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3

Rajendra, Singh. Non-linear dynamic analysis of geared systems. Cleveland, Ohio: Lewis Research Center, 1990.

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4

J, Shaker Francis, Fertis Demeter G, and Lewis Research Center, eds. Dynamic analysis of space-related linear and non-linear structures. Cleveland, Ohio: NASA Lewis Research Facility, 1990.

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5

J, Shaker Francis, Fertis Demeter G, and Lewis Research Center, eds. Dynamic analysis of space-related linear and non-linear structures. Cleveland, Ohio: NASA Lewis Research Facility, 1990.

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6

Lakis, A. A. Non-linear dynamic analysis of anisotropic cylindrical shells. Montréal, Québec: École polytechnique de Montréal, Dept. of Mechanical Engineering, Campus de lʼUniversité de Montréal, 1995.

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7

Bogdanovich, Alexander. Non-linear dynamic problems for composite cylindrical shells. London: Elsevier Applied Science, 1993.

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8

Non-linear time series: A dynamical system approach. Oxford: Clarendon, 1990.

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9

Tong, Howell. Non-linear time series: A dynamical system approach. Oxford: Clarendon Press, 1990.

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10

Tong, Howell. Non-linear time series: A dynamical system approach. Oxford: Clarendon Press, 1993.

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11

Barghian, M. Non-linear static and dynamic analysis of bar element structures. Manchester: UMIST, 1996.

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12

Piórek, Michał. Analysis of Chaotic Behavior in Non-linear Dynamical Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-94887-4.

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13

Mavriplis, Dimitri. An assessment of linear versus non-linear multigrid methods for unstructured mesh solver. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2001.

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14

Donley, M. G., and P. D. Spanos. Dynamic Analysis of Non-Linear Structures by the Method of Statistical Quadratization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-46715-8.

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15

Donley, M. G. Dynamic analysis of non-linear structures by the method of statistical quadratization. Berlin: Springer-Verlag, 1990.

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16

Mavriplis, Dimitri. Multigrid approaches to non-linear diffusion problems on unstructured meshes. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2001.

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17

Easaw, Joshy. Government expenditure and electoral security: A non-linear analysis of short-run dynamics. Leicester: University of Leicester, Department of Economics, 1997.

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18

Mohasseb, Sassan. Non-linear seismic analysis of fully base isolated structures on flexible soils. Zürich: Institut für Baustatik und Konstruktion Eidgenössische Technische Hochschule, 1988.

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19

N, Perry J., ed. Chaos in real data: Analysis of non-linear dynamics from short ecological time-series. London: Chapman & Hall, 1997.

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20

N, Perry Joe, ed. Chaos in real data: The analysis of non-linear dynamics from short ecological time series. Dordrecht: Kluwer Academic Publishers, 2000.

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21

Center, Langley Research, ed. The analysis of non-linear dynamic behavior (including snap-through) of postbuckled plates by simple analytical solution. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1988.

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22

Center, Langley Research, ed. The analysis of non-linear dynamic behavior (including snap-through) of postbuckled plates by simple analytical solution. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1988.

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23

Casadei, F. EURDYN-ID: A computer code for the one-dimensional non-linear dynamic analysis of structural systems : description a users manual (release 1). Luxembourg: Commission of the European Communities, 1985.

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24

Germany) International Conference on p-adic Functional Analysis (13th 2014 Paderborn. Advances in non-Archimedean analysis: 13th International Conference on p-adic Functional Analysis, August 12-16, 2014, University of Paderborn, Paderborn, Germany. Edited by Glöckner Helge 1969 editor, Escassut Alain editor, and Shamseddine Khodr 1966 editor. Providence, Rhode Island: American Mathematical Society, 2016.

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25

Non-linear dynamic analysis of geared systems. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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26

Donley, M. G., and Pol Spanos. Dynamic Analysis of Non-Linear Structures by the Method of Statistical Quadratization. Springer London, Limited, 2012.

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27

Raydugin, Yuri G. Modern Risk Quantification in Complex Projects. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844334.001.0001.

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Анотація:

There are multiple complaints that existing project risk quantification methods—both parametric and Monte Carlo—fail to produce accurate project duration and cost-risk contingencies in a majority of cases. It is shown that major components of project risk exposure—non-linear risk interactions—pertaining to complex projects are not taken into account. It is argued that a project system consists of two interacting subsystems: a project structure subsystem (PSS) and a project delivery subsystem (PDS). Any misalignments or imbalances between these two subsystems (PSS–PDS mismatches) are associated with the non-linear risk interactions. Principles of risk quantification are developed to take into account three types of non-linear risk interactions in complex projects: internal risk amplifications due to existing ‘chronic’ project system issues, knock-on interactions, and risk compounding. Modified bowtie diagrams for the three types of risk interactions are developed to identify and address interacting risks. A framework to visualize dynamic risk patterns in affinities of interacting risks is proposed. Required mathematical expressions and templates to factor relevant risk interactions to Monte Carlo models are developed. Business cases are discussed to demonstrate the power of the newly-developed non-linear Monte Carlo methodology (non-linear integrated schedule and cost risk analysis (N-SCRA)). A project system dynamics methodology based on rework cycles is adopted as a supporting risk quantification tool. Comparison of results yielded by the non-linear Monte Carlo and system dynamics models demonstrates a good alignment of the two methodologies. All developed Monte Carlo and system dynamics models are available on the book’s companion website.

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28

Tong, Howell, and Tong. Non-Linear Time Series ' A Dynamical System Approach ' (Oxford Statistical Science Series, 6). Oxford University Press(UK), 1993.

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29

Newman, Mark. Dynamical systems on networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0017.

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An introduction to the theory of dynamical systems on networks. This chapter starts with a short introduction to classical (non-network) dynamical systems theory, including linear stability analysis, fixed points, and limit cycles. Dynamical systems on networks are introduced, focusing initially on systems with only one variable per node and progressing to multi-variable systems. Linear stability analysis is developed in detail, leading to master stability conditions and the connection between stability and the spectral properties of networks. The chapter ends with a discussion of synchronization phenomena, the stability of limit cycles, and master stability conditions for synchronization.

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30

Piórek, Michał. Analysis of Chaotic Behavior in Non-Linear Dynamical Systems: Models and Algorithms for Quaternions. Springer International Publishing AG, 2018.

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31

Piórek, Michał. Analysis of Chaotic Behavior in Non-Linear Dynamical Systems: Models and Algorithms for Quaternions. Springer International Publishing AG, 2018.

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32

Perry, J. N., I. P. Woiwod, D. R. Morse, and R. H. Smith. Chaos in Real Data: The Analysis of Non-Linear Dynamics from Short Ecological Time Series. Springer, 2012.

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33

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Nonlinear Time Series Analysis with R. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.001.0001.

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Анотація:

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

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34

The analysis of non-linear dynamic behavior (including snap-through) of postbuckled plates by simple analytical solution. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1988.

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35

Chaos in Real Data: Analysis of Non-Linear Dynamics from Short Ecological (Chapman & Hall Population & Community Biology). International Thomson Publishing Services, 1997.

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36

Non-Linear Partial Differential Equations, Mathematical Physics, and Stochastic Analysis: The Helge Holden Anniversary Volume. American Mathematical Society, 2018.

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37

Hilliges, Otmar. Input Recognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0004.

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Sensing of user input lies at the core of HCI research. Deciding which input mechanisms to use and how to implement them such that they work in a way that is easy to use, robust to various environmental factors and accurate in reconstruction of the users intent is a tremendously challenging problem. The main difficulties stem from the complex nature of human behavior which is highly non-linear, dynamic and context dependent and can often only be observed partially. Due to these complexities, research has turned its attention to data-driven techniques in order to build sophisticated and robust input recognition mechanisms. In this chapter we discuss the most important aspects that constitute data-driven signal analysis approaches. The aim is to provide the reader with an overall understanding of the process irrespective of the exact choice of sensor or machine learning algorithm.

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38

Furst, Eric M., and Todd M. Squires. Microrheology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199655205.001.0001.

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We present a comprehensive overview of microrheology, emphasizing the underlying theory, practical aspects of its implementation, and current applications to rheological studies in academic and industrial laboratories. Key methods and techniques are examined, including important considerations to be made with respect to the materials most amenable to microrheological characterization and pitfalls to avoid in measurements and analysis. The fundamental principles of all microrheology experiments are presented, including the nature of colloidal probes and their movement in fluids, soft solids, and viscoelastic materials. Microrheology is divided into two general areas, depending on whether the probe is driven into motion by thermal forces (passive), or by an external force (active). We present the theory and practice of passive microrheology, including an in-depth examination of the Generalized Stokes-Einstein Relation (GSER). We carefully treat the assumptions that must be made for these techniques to work, and what happens when the underlying assumptions are violated. Experimental methods covered in detail include particle tracking microrheology, tracer particle microrheology using dynamic light scattering and diffusing wave spectroscopy, and laser tracking microrheology. Second, we discuss the theory and practice of active microrheology, focusing specifically on the potential and limitations of extending microrheology to measurements of non-linear rheological properties, like yielding and shear-thinning. Practical aspects of magnetic and optical tweezer measurements are preseted. Finally, we highlight important applications of microrheology, including measurements of gelation, degradation, high-throughput rheology, protein solution viscosities, and polymer dynamics.

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39

Berger, Tobias. Global Norms and Local Courts. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198807865.001.0001.

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What happens to transnational norms when they travel from one place to another? How do norms change when they move; and how do they affect the place where they arrive? This book develops a novel theoretical account of norm translation that is located in-between theories of norm diffusion and norm localization. It shows how such translations do not follow linear trajectories from ‘the global’ to ‘the local’. Instead, they unfold in a recursive back and forth movement between different actors located in different contexts. As norms are translated, their meaning changes; and only if their meaning changes in ways that are intelligible to people within a specific context, the social and political dynamics of this context change as well. This book analyses translations of ‘the rule of law’. It focuses on contemporary donor-driven projects with non-state courts in rural Bangladesh and shows how in these projects, global norms change local courts—but only if they are translated, often in unexpected ways from the perspective of international actors. Based on extensive fieldwork, this book reveals how grassroots-level employees of local non-governmental organizations significantly alter the meaning of global norms—for example when they translate secular notions of the rule of law into the language of Islam and Islamic Law—and only thereby also enhance participatory spaces for marginalized people. Such translations that change both global norms and local courts have been largely neglected by scholars and policy makers alike; they are the central theme of this book.

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