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1. |
Record Nr. |
UNINA9910453180103321 |
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Autore |
Shelhamer Mark |
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Titolo |
Nonlinear dynamics in physiology [[electronic resource] ] : a state-space approach / / Mark Shelhamer |
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Pubbl/distr/stampa |
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Singapore ; ; Hackensack, NJ, : World Scientific, c2007 |
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ISBN |
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1-281-92446-6 |
9786611924461 |
981-277-279-0 |
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Descrizione fisica |
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1 online resource (367 p.) |
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Disciplina |
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515.252 |
571.01/5118 |
571.015118 |
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Soggetti |
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Physiology - Mathematical models |
Nonlinear systems |
State-space methods |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Preface; Contents; 1. The mathematical analysis of physiological systems: goals and approaches; 1.1 The goals of mathematical analysis in physiology; 1.2 Outline of dynamic systems; 1.3 Types of dynamic systems - random, deterministic, linear, nonlinear; 1.4 Types of dynamic behaviors - random, fixed point, periodic, quasi-periodic, chaotic; 1.5 Follow the ""noise""; 1.6 Chaos and physiology; General Bibliography; References for Chapter 1; 2. Fundamental signal processing and analysis concepts and measures; 2.1 Sampled data and continuous distributions; 2.2 Basic statistics |
2.3 Correlation coefficient2.4 Linear regression, least-squares, squared-error; 2.5 Random processes, white noise, correlated noise; 2.6 Autocorrelation; 2.7 Concluding remarks; References for Chapter 2; 3. Analysis approaches based on linear systems; 3.1 Definition and properties of linear systems; 3.2 Autocorrelation, cross-correlation, stationarity; 3.3 Fourier transforms and spectral analysis; 3.4 Examples of autocorrelations and frequency spectra; 3.5 Transfer functions of |
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linear systems, Gaussian statistics; References for Chapter 3; 4. State-space reconstruction |
4.1 State variables, state space4.2 Time-delay reconstruction; 4.3 A digression on topology; 4.4 How to do the reconstruction correctly; 4.5 Example: detection of fast-phase eye movements; 4.6 Historical notes, examples from the literature; 4.7 Points for further consideration; References for Chapter 4; 5. Dimensions; 5.1 Euclidean dimension and topological dimension; 5.2 Dimension as a scaling process - coastline length, Mandelbrot, fractals, Cantor, Koch; 5.3 Box-counting dimension and correlation dimension; 5.4 Correlation dimension - how to measure it correctly |
5.5 Error bars on dimension estimates5.6 Interpretation of the dimension; 5.7 Tracking dimension overtime; 5.8 Examples; 5.9 Points for further consideration; References for Chapter 5; 6. Surrogate data; 6.1 The need for surrogates; 6.2 Statistical hypothesis testing; 6.3 Statistical randomization and its implementation; 6.4 Random surrogates; 6.5 Phase-randomization surrogate; 6.6 AAFT surrogate; 6.7 Pseudo-periodic surrogate; 6.8 First differences and surrogates; 6.9 Multivariate surrogates; 6.10 Surrogates tailored to specific physiological hypotheses; 6.11 Examples of different surrogates |
6.12 Physiological examplesReferences for Chapter 6; 7. Nonlinear forecasting; 7.1 Predictability of prototypical systems; 7.2 Methodology; 7.3 Variations; 7.4 Surrogates, global linear forecasting; 7.5 Time-reversal and amplitude-reversal for detection of nonlinearity; 7.6 Chaos versus colored noise; 7.7 Forecasting of neural spike trains and other discrete events; 7.8 Examples; References for Chapter 7; 8. Recurrence analysis; 8.1 Concept and methodology; 8.2 Recurrence plots of simple systems; 8.3 Recurrence quantification analysis (RQA); 8.4 Extensions; 8.5 Examples |
References for Chapter 8 |
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Sommario/riassunto |
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This book provides a compilation of mathematical-computational tools that are used to analyze experimental data. The techniques presented are those that have been most widely and successfully applied to the analysis of physiological systems, and address issues such as randomness, determinism, dimension, and nonlinearity. In addition to bringing together the most useful methods, sufficient mathematical background is provided to enable non-specialists to understand and apply the computational techniques. Thus, the material will be useful to life-science investigators on several levels, from phys |
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2. |
Record Nr. |
UNINA9910143554203321 |
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Titolo |
Damage prognosis for aerospace, civil and mechanical systems [[electronic resource] /] / edited by Daniel J. Inman ... [et al.] |
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Pubbl/distr/stampa |
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Chichester, England ; ; Hoboken, NJ, : Wiley, c2005 |
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ISBN |
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1-280-28789-6 |
9786610287895 |
0-470-30056-6 |
0-470-86909-7 |
0-470-86908-9 |
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Descrizione fisica |
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1 online resource (471 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Structural analysis (Engineering) |
Materials - Deterioration |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Damage Prognosis; Contents; List of Contributors; Preface; 1 An Introduction to Damage Prognosis; 1.1 Introduction; 1.2 The Damage-Prognosis Solution Process; 1.3 Motivation for Damage-Prognosis Solutions; 1.4 Disciplines Needed to Address Damage Prognosis; 1.5 Summary; References; Part I Damage Models; 2 An Overview of Modeling Damage Evolution in Materials; 2.1 Introduction; 2.2 Overview of General Modeling Issues; 2.3 Characterization of Material Behavior: Damage Initiation and Evolution; 2.4 Material Modeling: General Considerations and Preliminary Concepts |
2.5 Classical Damage-Modeling Approaches2.6 Phenomenological Constitutive Modeling; 2.7 Micromechanical Modeling of Materials; 2.8 Summary; References; 3 In Situ Observation of Damage Evolution and Fracture Toughness Measurement by SEM; 3.1 Overview of Fracture Mechanics Related to Damage Prognosis; 3.2 In Situ Observation of Damage Evolution and Fracture Toughness Measurement; 3.3 Concluding remarks; Acknowledgements; References; 4 Predictive |
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Modeling of Crack Propagation Using the Boundary Element Method; 4.1 Introduction; 4.2 Damage and Fracture Mechanics Theories |
4.3 Boundary Element Fracture Mechanics4.4 Predictive Modeling of Crack Propagation; 4.5 Numerical Results; 4.6 Conclusions; Acknowledgments; References; 5 On Friction Induced Nonideal Vibrations: A Source of Fatigue; 5.1 Preliminary Remarks; 5.2 Nonlinear Dynamics of Ideal and Nonideal Stick-Slip Vibrations; 5.3 Switching Control for Ideal and Nonideal Stick-Slip Vibrations; 5.4 Some Concluding Remarks; Acknowledgments; References; 6 Incorporating and Updating of Damping in Finite Element Modeling; 6.1 Introduction; 6.2 Theoretical Fundamentals; 6.3 Application; 6.4 Conclusion; References |
Part II Monitoring Algorithms7 Model-Based Inverse Problems in Structural Dynamics; 7.1 Introduction; 7.2 Theory of Discrete Vibrating Systems; 7.3 Response Sensitivity; 7.4 Finite-Element Model Updating; 7.5 Review of Classical Optimization Techniques; 7.6 Heuristic Optimization Methods; 7.7 Multicriteria Optimization; 7.8 General Optimization Scheme for Inverse Problems in Engineering; 7.9 Applications; Acknowledgments; References; 8 Structural Health Monitoring Algorithms for Smart Structures; 8.1 Initial Considerations about SHM |
8.2 Optimal Placement of Sensors and Actuators for Smart Structures8.3 Proposed Methodology; 8.4 Artificial Neural Network as a SHM Algorithm; 8.5 Genetic Algorithms as a SHM Algorithm; 8.6 Conclusion; References; 9 Uncertainty Quantification and the Verification and Validation of Computational Models; 9.1 Introduction; 9.2 Verification Activities; 9.3 Validation Activities; 9.4 Uncertainty Quantification; 9.5 Assessment of Prediction Accuracy; 9.6 Conclusion; References; 10 Reliability Methods; 10.1 Introduction; 10.2 Reliability Assessment; 10.3 Approximation of the Probability of Failure |
10.4 Decision Making |
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Sommario/riassunto |
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Damage prognosis is a natural extension of damage detection and structural health monitoring and is forming a growing part of many businesses. This comprehensive volume presents a series of fundamental topics that define the new area of damage prognosis. Bringing together essential information in each of the basic technologies necessary to perform damage prognosis, it also reflects the highly interdisciplinary nature of the industry through the extensive referencing of each of the component disciplines. Taken from lectures given at the Pan American Advanced Studies Institute in Damage Pro |
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