Stochastic Processes in Engineering Systems

Stochastic Processes in Engineering Systems
Author: E. Wong
Publisher: Springer Science & Business Media
Total Pages: 372
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461250609

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This book is a revision of Stochastic Processes in Information and Dynamical Systems written by the first author (E.W.) and published in 1971. The book was originally written, and revised, to provide a graduate level text in stochastic processes for students whose primary interest is its applications. It treats both the traditional topic of sta tionary processes in linear time-invariant systems as well as the more modern theory of stochastic systems in which dynamic structure plays a profound role. Our aim is to provide a high-level, yet readily acces sible, treatment of those topics in the theory of continuous-parameter stochastic processes that are important in the analysis of information and dynamical systems. The theory of stochastic processes can easily become abstract. In dealing with it from an applied point of view, we have found it difficult to decide on the appropriate level of rigor. We intend to provide just enough mathematical machinery so that important results can be stated PREFACE vi with precision and clarity; so much ofthe theory of stochastic processes is inherently simple if the suitable framework is provided. The price of providing this framework seems worth paying even though the ul timate goal is in applications and not the mathematics per se.

Stochastic Methods in Engineering

Stochastic Methods in Engineering
Author: I. St Doltsinis
Publisher: WIT Press
Total Pages: 379
Release: 2012
Genre: Mathematics
ISBN: 1845646266

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The increasing industrial demand for reliable quantification and management of uncertainty in product performance forces engineers to employ probabilistic models in analysis and design, a fact that has occasioned considerable research and development activities in the field. Notes on Stochastics eventually address the topic of computational stochastic mechanics. The single volume uniquely presents tutorials on essential probabilistics and statistics, recent finite element methods for stochastic analysis by Taylor series expansion as well as Monte Carlo simulation techniques. Design improvement and robust optimisation represent key issues as does reliability assessment. The subject is developed for solids and structures of elastic and elasto-plastic material, large displacements and material deformation processes; principles are transferable to various disciplines. A chapter is devoted to the statistical comparison of systems exhibiting random scatter. Where appropriate examples illustrate the theory, problems to solve appear instructive; applications are presented with relevance to engineering practice. The book, emanating from a university course, includes research and development in the field of computational stochastic analysis and optimization. It is intended for advanced students in engineering and for professionals who wish to extend their knowledge and skills in computational mechanics to the domain of stochastics. Contents: Introduction, Randomness, Structural analysis by Taylor series expansion, Design optimization, Robustness, Monte Carlo techniques for system response and design improvement, Reliability, Time variant phenomena, Material deformation processes, Analysis and comparison of data sets, Probability distribution of test functions.

Stochastic Methods for Estimation and Problem Solving in Engineering

Stochastic Methods for Estimation and Problem Solving in Engineering
Author: Kadry, Seifedine
Publisher: IGI Global
Total Pages: 291
Release: 2018-03-02
Genre: Technology & Engineering
ISBN: 1522550461

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Utilizing mathematical algorithms is an important aspect of recreating real-world problems in order to make important decisions. By generating a randomized algorithm that produces statistical patterns, it becomes easier to find solutions to countless situations. Stochastic Methods for Estimation and Problem Solving in Engineering provides emerging research on the role of random probability systems in mathematical models used in various fields of research. While highlighting topics, such as random probability distribution, linear systems, and transport profiling, this book explores the use and behavior of uncertain probability methods in business and science. This book is an important resource for engineers, researchers, students, professionals, and practitioners seeking current research on the challenges and opportunities of non-deterministic probability models.

Stochastic Optimization Methods

Stochastic Optimization Methods
Author: Kurt Marti
Publisher: Springer
Total Pages: 389
Release: 2015-02-21
Genre: Business & Economics
ISBN: 3662462141

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This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.

Stochastic Processes and Filtering Theory

Stochastic Processes and Filtering Theory
Author: Andrew H. Jazwinski
Publisher: Courier Corporation
Total Pages: 404
Release: 2013-04-15
Genre: Science
ISBN: 0486318192

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This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.

Stationary Stochastic Processes for Scientists and Engineers

Stationary Stochastic Processes for Scientists and Engineers
Author: Georg Lindgren
Publisher: CRC Press
Total Pages: 316
Release: 2013-10-11
Genre: Mathematics
ISBN: 1466586192

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Suitable for a one-semester course, this text teaches students how to use stochastic processes efficiently. Carefully balancing mathematical rigor and ease of exposition, the book provides students with a sufficient understanding of the theory and a practical appreciation of how it is used in real-life situations. Special emphasis is on the interpretation of various statistical models and concepts as well as the types of questions statistical analysis can answer. To enable hands-on practice, MATLAB code is available online.

Stochastic Tools in Turbulence

Stochastic Tools in Turbulence
Author: John L. Lumley
Publisher: Courier Corporation
Total Pages: 210
Release: 2007-01-01
Genre: Science
ISBN: 0486462706

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This accessible treatment offers the mathematical tools for describing and solving problems related to stochastic vector fields. Advanced undergraduates and graduate students will find its use of generalized functions a relatively simple method of resolving mathematical questions. It will prove a valuable reference for applied mathematicians and professionals in the fields of aerospace, chemical, civil, and nuclear engineering. The author, Professor Emeritus of Engineering at Cornell University, starts with a survey of probability distributions and densities and proceeds to examinations of moments, characteristic functions, and the Gaussian distribution; random functions; and random processes in more dimensions. Extensive appendixes—which include information on Fourier transforms, tensors, generalized functions, and invariant theory—contribute toward making this volume mathematically self-contained.

Stochastic Methods in Engineering

Stochastic Methods in Engineering
Author: I. Doltsinis
Publisher:
Total Pages: 379
Release: 2012
Genre: Science
ISBN: 9781845646271

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The book, emanating from a university course, includes research and development in the field of computational stochastic analysis and optimization. It is intended for advanced students in engineering and for professionals who wish to extend their knowledg

Stochastic Models in Reliability Engineering

Stochastic Models in Reliability Engineering
Author: Lirong Cui
Publisher: CRC Press
Total Pages: 376
Release: 2020-09-01
Genre: Mathematics
ISBN: 1000094618

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This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, all of which have seen extended research activities in recent years. The methods presented in this book are real-world examples that demonstrate improvements in essential reliability and availability for industrial equipment such as medical magnetic resonance imaging, power systems, traction drives for a search and rescue helicopter, and air conditioning systems. The book presents real case studies of redundant multi-state air conditioning systems for chemical laboratories and covers assessments of reliability and fault tolerance and availability calculations. Conventional and contemporary topics in reliability engineering are discussed, including degradation, networks, and dynamic reliability, resilience, and multi-state systems, all of which are relatively new topics to the field. The book is aimed at engineers and scientists, as well as postgraduate students involved in reliability design, analysis, and experiments and applied probability and statistics.