Download Free Combining Interval Probabilistic And Other Types Of Uncertainty In Engineering Applications Book in PDF and EPUB Free Download. You can read online Combining Interval Probabilistic And Other Types Of Uncertainty In Engineering Applications and write the review.

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.
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.
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.
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.
Vehicle reliability problems continue to be the news because of major vehicle recalls from several manufacturers. This book includes 40 SAE technical papers, published from 2007 through 2010, that describe the latest research on automotive electronics reliability technology. This book will help engineers and researchers focus on the design strategies being used to minimize electronics reliability problems, and how to test and verify those strategies. After an overview of durability, risk assessment, and failure mechanisms, this book focuses on state-of-the-art techniques for reliability-based design, and reliability testing and verification. Topics include: powertrain control monitoring distributed automotive embedded systems model-based design x-by-wire systems battery durability design verification fault tree analysis The book also includes editor Ronald K. Jurgen’s introduction ,“Striving for Maximum Reliability in a Highly Complex Electronic Environment”, and a concluding section on the future of electronics reliability, including networking technology, domain control units, the use of AUTOSAR, and embedded software.
Examines numerical and semi-analytical methods for differential equations that can be used for solving practical ODEs and PDEs This student-friendly book deals with various approaches for solving differential equations numerically or semi-analytically depending on the type of equations and offers simple example problems to help readers along. Featuring both traditional and recent methods, Advanced Numerical and Semi Analytical Methods for Differential Equations begins with a review of basic numerical methods. It then looks at Laplace, Fourier, and weighted residual methods for solving differential equations. A new challenging method of Boundary Characteristics Orthogonal Polynomials (BCOPs) is introduced next. The book then discusses Finite Difference Method (FDM), Finite Element Method (FEM), Finite Volume Method (FVM), and Boundary Element Method (BEM). Following that, analytical/semi analytic methods like Akbari Ganji's Method (AGM) and Exp-function are used to solve nonlinear differential equations. Nonlinear differential equations using semi-analytical methods are also addressed, namely Adomian Decomposition Method (ADM), Homotopy Perturbation Method (HPM), Variational Iteration Method (VIM), and Homotopy Analysis Method (HAM). Other topics covered include: emerging areas of research related to the solution of differential equations based on differential quadrature and wavelet approach; combined and hybrid methods for solving differential equations; as well as an overview of fractal differential equations. Further, uncertainty in term of intervals and fuzzy numbers have also been included, along with the interval finite element method. This book: Discusses various methods for solving linear and nonlinear ODEs and PDEs Covers basic numerical techniques for solving differential equations along with various discretization methods Investigates nonlinear differential equations using semi-analytical methods Examines differential equations in an uncertain environment Includes a new scenario in which uncertainty (in term of intervals and fuzzy numbers) has been included in differential equations Contains solved example problems, as well as some unsolved problems for self-validation of the topics covered Advanced Numerical and Semi Analytical Methods for Differential Equations is an excellent text for graduate as well as post graduate students and researchers studying various methods for solving differential equations, numerically and semi-analytically.
The stability of natural rock slopes is influenced by a wide spectrum of factors, such as mechanical properties of bedrocks and spatial distribution of discontinuities. Their specific values are typically incomplete, due mainly to the lack of effective and comprehensive methods to accurately characterize these factors, especially those inside of the slopes. The neutrosophic number is a useful tool to solve problems in indeterminate environment.
his volume of the series Lecture Notes in Medical Informatics contains the T proceedings of the Workshop on System Engineering in Medicine, which was held in Maastricht, The Netherlands, 16-18 March 1989. This workshop was sponsored by the EC under the framework of the Medical and Health Research Programme. The aim of the workshop was to assess whether there was sufficient support in the Medical Informatics community in the EC to establish a concerted action. This proceedings contain papers of the presentations given at the workshop. These presentations were centred around three themes: • Methods and Tools • Applications in the domains of chronic care and critical care • Evaluation of decision support systems The papers were prepared after the workshop and therefore we were able to include the relevant parts of the discussions which were related to the presentations. As a result of the discussions during the workshop, a proposal was prepared for the establishment of a concerted action, specifically addressing the development of guidelines for the evaluation of medical decision aids. This proposal was granted early 1990 under the same Medical and Health Research programme of the EC. Over 40 institutes are participating in this concerted action. It have been the outstanding presentations and the open discussions at the workshop that have been the starting point of this concerted action. The papers in this proceedings formed a starting point for the discussions in the meetings of the concerted action.
This volume includes chapters presenting applications of different metaheuristics in reliability engineering, including ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization. It also presents chapters devoted to cellular automata and support vector machines, and applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe aspects of imprecise reliability and applications of fuzzy and vague set theory.