Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications
Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

Author: Andrew Pownuk

Publisher: Springer

Published: 2018-05-03

Total Pages: 210

ISBN-13: 3319910264

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How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc. In all these developments, the authors’ objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty. The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty.

Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications
Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

Author: Andrew M. Pownuk

Publisher:

Published: 2018

Total Pages: 202

ISBN-13: 9783319910277

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How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc. In all these developments, the authors' objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty. The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty.

Measurement Uncertainty
Measurement Uncertainty

Author: Ronald H. Dieck

Publisher: ISA

Published: 2007

Total Pages: 292

ISBN-13: 9781556179150

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Literally an entire course between two covers, Measurement Uncertainty: Methods and Applications, Fourth Edition, presents engineering students with a comprehensive tutorial of measurement uncertainty methods in a logically categorized and readily utilized format. The new uncertainty technologies embodied in both U.S. and international standards have been incorporated into this text with a view toward understanding the strengths and weaknesses of both. The book is designed to also serve as a practical desk reference in situations that commonly confront an experimenter. The text presents the basics of the measurement uncertainty model, non-symmetrical systematic standard uncertainties, random standard uncertainties, the use of correlation, curve-fitting problems, and probability plotting, combining results from different test methods, calibration errors, and uncertainty propagation for both independent and dependent error sources. The author draws on years of experience in industry to direct special attention to the problem of developing confidence in uncertainty analysis results and using measurement uncertainty to select instrumentation systems.

Fuzzy Randomness
Fuzzy Randomness

Author: Bernd Möller

Publisher: Springer Science & Business Media

Published: 2004-02-19

Total Pages: 356

ISBN-13: 9783540402947

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The subject of the book is the comprehensive consideration of uncertainty in the numerical analysis, the safety assessment, and the design of structures. Stochastic as well as non-stochastic uncertainty is treated on the basis of the superordinated uncertainty model fuzzy randomness. This new uncertainty model contains the special cases of real valued random variables and fuzzy variables and permits to take account of both uncertainty characteristics simultaneously. The book introduces to the problem of uncertainty and provides a current survey of relevant uncertainty models and their application in civil engineering. The necessary, special mathematical basics of the fuzzy set theory and the theory of fuzzy random variables are explained in an engineering manner and illustrated by way of examples. Basic ideas and methods for appropriately quantifying uncertain structural parameters are presented and demonstrated by means of characteristic examples. For processing uncertainty in structural analysis, safety assessment, and structural design completely new algorithms are introduced and described in detail as fuzzy structural analysis, fuzzy probabilistic safety assessment, and fuzzy cluster design. The application of the new methods is demonstrated for selected examples from civil engineering, their essential advantages are emphasized. For the first time this represents a coherent, overall concept for considering uncertainty in civil engineering. The book in particular addresses to civil engineers and requires a university degree as well as basic knowledge in stochastics. But also for mechanical engineers, colleagues from applied mathematics, and other people who are interested in uncertainty problems the book represents a suitable introduction to the problem of uncertainty modeling and provides general solutions and algorithms, which may also be applied to problems from other fields beyond engineering.

Soft Methods for Integrated Uncertainty Modelling
Soft Methods for Integrated Uncertainty Modelling

Author: Jonathan Lawry

Publisher: Springer Science & Business Media

Published: 2007-10-08

Total Pages: 413

ISBN-13: 3540347771

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The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned ‘Soft Computing’ as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertaintymodelling methodologies which are complementary to conventional statistics and probability theory.

Handbook of Smart Energy Systems
Handbook of Smart Energy Systems

Author: Michel Fathi

Publisher: Springer Nature

Published: 2023-08-04

Total Pages: 3382

ISBN-13: 3030979407

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This handbook analyzes and develops methods and models to optimize solutions for energy access (for industry and the general world population alike) in terms of reliability and sustainability. With a focus on improving the performance of energy systems, it brings together state-of-the-art research on reliability enhancement, intelligent development, simulation and optimization, as well as sustainable development of energy systems. It helps energy stakeholders and professionals learn the methodologies needed to improve the reliability of energy supply-and-demand systems, achieve more efficient long-term operations, deal with uncertainties in energy systems, and reduce energy emissions. Highlighting novel models and their applications from leading experts in this important area, this book will appeal to researchers, students, and engineers in the various domains of smart energy systems and encourage them to pursue research and development in this exciting and highly relevant field.

Recent Advancements in Multi-View Data Analytics
Recent Advancements in Multi-View Data Analytics

Author: Witold Pedrycz

Publisher: Springer Nature

Published: 2022-05-20

Total Pages: 346

ISBN-13: 3030952398

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This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others. The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

Uncertainty, Calibration and Probability
Uncertainty, Calibration and Probability

Author: C.F Dietrich

Publisher: Routledge

Published: 2017-07-12

Total Pages: 564

ISBN-13: 1351406272

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All measurements are subject to error because no quantity can be known exactly; hence, any measurement has a probability of lying within a certain range. The more precise the measurement, the smaller the range of uncertainty. Uncertainty, Calibration and Probability is a comprehensive treatment of the statistics and methods of estimating these calibration uncertainties. The book features the general theory of uncertainty involving the combination (convolution) of non-Gaussian, student t, and Gaussian distributions; the use of rectangular distributions to represent systematic uncertainties; and measurable and nonmeasurable uncertainties that require estimation. The author also discusses sources of measurement errors and curve fitting with numerous examples of uncertainty case studies. Many useful tables and computational formulae are included as well. All formulations are discussed and demonstrated with the minimum of mathematical knowledge assumed. This second edition offers additional examples in each chapter, and detailed additions and alterations made to the text. New chapters consist of the general theory of uncertainty and applications to industry and a new section discusses the use of orthogonal polynomials in curve fitting. Focusing on practical problems of measurement, Uncertainty, Calibration and Probability is an invaluable reference tool for R&D laboratories in the engineering/manufacturing industries and for undergraduate and graduate students in physics, engineering, and metrology.

Convex Models of Uncertainty in Applied Mechanics
Convex Models of Uncertainty in Applied Mechanics

Author: Y. Ben-Haim

Publisher: Elsevier

Published: 2013-10-22

Total Pages: 240

ISBN-13: 1483290972

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Recognition of the need to introduce the ideas of uncertainty in a wide variety of scientific fields today reflects in part some of the profound changes in science and engineering over the last decades. Nobody questions the ever-present need for a solid foundation in applied mechanics. Neither does anyone question nowadays the fundamental necessity to recognize that uncertainty exists, to learn to evaluate it rationally, and to incorporate it into design.This volume provides a timely and stimulating overview of the analysis of uncertainty in applied mechanics. It is not just one more rendition of the traditional treatment of the subject, nor is it intended to supplement existing structural engineering books. Its aim is to fill a gap in the existing professional literature by concentrating on the non-probabilistic model of uncertainty. It provides an alternative avenue for the analysis of uncertainty when only a limited amount of information is available. The first chapter briefly reviews probabilistic methods and discusses the sensitivity of the probability of failure to uncertain knowledge of the system. Chapter two discusses the mathematical background of convex modelling. In the remainder of the book, convex modelling is applied to various linear and nonlinear problems. Uncertain phenomena are represented throughout the book by convex sets, and this approach is referred to as convex modelling.This book is intended to inspire researchers in their goal towards further growth and development in this field.