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Probabilistic Reliability Analysis of Power Systems: A Student’s Introduction
by José L. Rueda Torres Francisco M. Gonzalez-Longatt Mart A. van der Meijden Bart W. Tuinema Alexandru I. StefanovThis textbook provides an introduction to probabilistic reliability analysis of power systems. It discusses a range of probabilistic methods used in reliability modelling of power system components, small systems and large systems. It also presents the benefits of probabilistic methods for modelling renewable energy sources. The textbook describes real-life studies, discussing practical examples and providing interesting problems, teaching students the methods in a thorough and hands-on way.The textbook has chapters dedicated to reliability models for components (reliability functions, component life cycle, two-state Markov model, stress-strength model), small systems (reliability networks, Markov models, fault/event tree analysis) and large systems (generation adequacy, state enumeration, Monte-Carlo simulation). Moreover, it contains chapters about probabilistic optimal power flow, the reliability of underground cables and cyber-physical power systems.After reading this book, engineering students will be able to apply various methods to model the reliability of power system components, smaller and larger systems. The textbook will be accessible to power engineering students, as well as students from mathematics, computer science, physics, mechanical engineering, policy & management, and will allow them to apply reliability analysis methods to their own areas of expertise.
Probabilistic Reliability Models
by Igor A. UshakovPractical Approaches to Reliability Theory in Cutting-Edge ApplicationsProbabilistic Reliability Models helps readers understand and properly use statistical methodsand optimal resource allocation to solve engineering problems.The author supplies engineers with a deeper understanding of mathematical models while alsoequipping mathematically oriented readers with a fundamental knowledge of the engineeringrelatedapplications at the center of model building. The book showcases the use of probabilitytheory and mathematical statistics to solve common, real-world reliability problems. Followingan introduction to the topic, subsequent chapters explore key systems and models including:* Unrecoverable objects and recoverable systems* Methods of direct enumeration* Markov models and heuristic models* Performance effectiveness* Time redundancy* System survivability* Aging units and their related systems* Multistate systemsDetailed case studies illustrate the relevance of the discussed methods to real-world technicalprojects including software failure avalanches, gas pipelines with underground storage, andintercontinental ballistic missile (ICBM) control systems. Numerical examples and detailedexplanations accompany each topic, and exercises throughout allow readers to test theircomprehension of the presented material.Probabilistic Reliability Models is an excellent book for statistics, engineering, and operationsresearch courses on applied probability at the upper-undergraduate and graduate levels. Thebook is also a valuable reference for professionals and researchers working in industry whowould like a mathematical review of reliability models and the relevant applications.
Probabilistic Risk and Hazard Assessment: Proceedings of the conference, Newcastle, NSW, Australia, 22-23 September 1993
by R.E.MELCHERS; M.G.STEWARTHighlights the multi-disciplinary nature of probabilistic risk and hazard assessment procedures. Topics covered include: Hazard scenario analyses (e.g. HAZOP, FMEA); probabilistic risk assessments; consequence modelling; structural reliability; human error; uncertainty analyses; and risk assessment. Topics are related to the design, construction & operation of chemical & process plants; nuclear facilities; bridges; buildings; offshore structures; dams.
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
by Sebastian Thrun Wolfram Burgard Dieter Fox<p>An introduction to the techniques and algorithms of the newest field in robotics. <p>Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.</p>
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
by Sebastian Thrun Wolfram Burgard Dieter FoxAn introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Probabilistic-Statistical Approaches to the Prediction of Aircraft Navigation Systems Condition (Springer Aerospace Technology)
by Eliseev B. P. Kozlov A. I. Romancheva N. I. Shatrakov Y. G. Zatuchny D. A. Zavalishin O. I.This book highlights the development of new methods for assessing and forecasting the state of various complex ageing systems in service; analyzing the influence of destabilizing factors on the accuracy of aircraft flight navigation support; and making recommendations on the ideal aircraft route, taking into consideration the available information on the reliability of the navigation and communication equipment.
Probabilistic-Statistical Methods for Risk Assessment in Civil Aviation (Springer Aerospace Technology)
by Valery Dmitryevich Sharov Vadim Vadimovich Vorobyov Dmitry Alexandrovich ZatuchnyThis book analyses the models for major risks related to flight safety in the aviation sector and presents risk estimation methods through examples of several known aviation enterprises. The book provides a comprehensive content for professionals engaged in the development of flight safety regulatory framework as well as in the design and operation of ground-based or on-board flight support radio electronic systems. The book is also useful for senior students and postgraduates in aviation specialties, especially those related to air traffic management.
Probabilistic Transmission System Planning
by Wenyuan LiThe book is composed of 12 chapters and three appendices, and can be divided into four parts. The first part includes Chapters 2 to 7, which discuss the concepts, models, methods and data in probabilistic transmission planning. The second part, Chapters 8 to 11, addresses four essential issues in probabilistic transmission planning applications using actual utility systems as examples. Chapter 12, as the third part, focuses on a special issue, i.e. how to deal with uncertainty of data in probabilistic transmission planning. The fourth part consists of three appendices, which provide the basic knowledge in mathematics for probabilistic planning.
Probabilità, Statistica e Simulazione: Programmi applicativi scritti in R (UNITEXT #131)
by Alberto Rotondi Paolo Pedroni Antonio PievatoloIl libro contiene in forma compatta il programma svolto negli insegnamenti introduttivi di Statistica e tratta alcuni argomenti indispensabili per l'attività di ricerca, come le tecniche di simulazione Monte Carlo, i metodi di inferenza statistica, di best fit e di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Il testo è rivolto agli studenti universitari dei corsi ad indirizzo scientifico e a tutti quei ricercatori che devono risolvere problemi concreti che coinvolgono l’analisi dei dati e le tecniche di simulazione. In questa edizione, completamente rivista e corretta, sono stati aggiunti alcuni importanti argomenti sul test d’ipotesi (a cui è stato dedicato un capitolo interamente nuovo) e sul trattamento degli errori sistematici. Per la prima volta è stato adottato il software R, con una ricca libreria di programmi originali accessibile al lettore.
Probability and Random Processes for Electrical and Computer Engineers
by John A. GubnerThe theory of probability is a powerful tool that helps electrical and computer engineers to explain, model, analyze, and design the technology they develop. The text begins at the advanced undergraduate level, assuming only a modest knowledge of probability, and progresses through more complex topics mastered at graduate level. The first five chapters cover the basics of probability and both discrete and continuous random variables. The later chapters have a more specialized coverage, including random vectors, Gaussian random vectors, random processes, Markov Chains, and convergence. Describing tools and results that are used extensively in the field, this is more than a textbook; it is also a reference for researchers working in communications, signal processing, and computer network traffic analysis. With over 300 worked examples, some 800 homework problems, and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students. Further resources for this title, including solutions (for Instructors only), are available online at www. cambridge. org/9780521864701.
Probability and Random Variables for Electrical Engineering: Probability: Measurement of Uncertainty (Studies in Systems, Decision and Control #390)
by Muammer Catak Tofigh Allahviranloo Witold PedryczThis book delivers a concise and carefully structured introduction to probability and random variables. It aims to build a linkage between the theoretical conceptual topics and the practical applications, especially in the undergraduate engineering area. The book motivates the student to gain full understanding of the fundamentals of probability theory and help acquire working problem-solving skills and apply the theory to engineering applications. Each chapter includes solved examples at varying levels (both introductory and advanced) in addition to problems that demonstrate the relevance of the probability and random variables in engineering. As authors, we focused on to find out the optimum ways in order to introduce the topics in probability and random variables area.
Probability and Statistical Models in Operations Research, Computer and Management Sciences: Including Applications to Reliability Models (Springer Series in Reliability Engineering)
by Syouji Nakamura Katsushige Sawaki Toshio NakagawaThis book explores the convergence of stochastic modeling, reliability tools, and the quest for solutions in an era of globalized challenges. The tools have become only more important in the unforeseen emergencies such as the COVID-19 pandemic and the conflict in Ukraine. This comprehensive book is an invaluable resource for graduate students seeking practical knowledge on probability and statistics in real-world applications. The book is divided into four parts: reliability, computer science, management science, and operations research. Each part includes surveys, recent results, and tools used. Moreover, it offers an essential reference point for researchers, engineers, and managers operating in laboratories, industries, businesses, and government agencies. Through the exchange of academic achievements, ideas, and discussions, this book serves as a catalyst for progress and innovation.
Probability and Statistics Applications for Environmental Science
by Stacey J Shaefer Louis TheodoreSimple, clear, and to the point, Probability and Statistics Applications for Environmental Science delineates the fundamentals of statistics, imparting a basic understanding of the theory and mechanics of the calculations. User-friendliness, uncomplicated explanations, and coverage of example applications in the environmental field set this book ap
Probability and Statistics for Engineers and Scientists (Ninth Edition)
by Ronald E. Walpole Sharon L. Myers Raymond H. Myers Keying E. YeThis classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
Probability and Statistics for STEM: A Course in One Semester (Synthesis Lectures on Mathematics & Statistics)
by Emmanuel N. Barron John G. Del GrecoThis new edition presents the essential topics in probability and statistics from a rigorous standpoint. Any discipline involving randomness, including medicine, engineering, and any area of scientific research, must have a way of analyzing or even predicting the outcomes of an experiment. The authors focus on the tools for doing so in a thorough, yet introductory way. After providing an overview of the basics of probability, the authors cover essential topics such as confidence intervals, hypothesis testing, and linear regression. These subjects are presented in a one semester format, suitable for engineers, scientists, and STEM students with a solid understanding of calculus. There are problems and exercises included in each chapter allowing readers to practice the applications of the concepts.
Probability and Stochastic Modeling
by Vladimir I. RotarA First Course in Probability with an Emphasis on Stochastic ModelingProbability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability t
Probability and Stochastic Processes for Physicists (UNITEXT for Physics)
by Nicola Cufaro PetroniThis book seeks to bridge the gap between the parlance, the models, and even the notations used by physicists and those used by mathematicians when it comes to the topic of probability and stochastic processes. The opening four chapters elucidate the basic concepts of probability, including probability spaces and measures, random variables, and limit theorems. Here, the focus is mainly on models and ideas rather than the mathematical tools. The discussion of limit theorems serves as a gateway to extensive coverage of the theory of stochastic processes, including, for example, stationarity and ergodicity, Poisson and Wiener processes and their trajectories, other Markov processes, jump-diffusion processes, stochastic calculus, and stochastic differential equations. All these conceptual tools then converge in a dynamical theory of Brownian motion that compares the Einstein–Smoluchowski and Ornstein–Uhlenbeck approaches, highlighting the most important ideas that finally led to a connection between the Schrödinger equation and diffusion processes along the lines of Nelson’s stochastic mechanics. A series of appendices cover particular details and calculations, and offer concise treatments of particular thought-provoking topics.
Probability Applications in Mechanical Design (Mechanical Engineering)
by Franklin E. Fisher Joy R. FisherThe authors of this text seek to clarify mechanical fatigue and design problems by applying probability and computer analysis, and further extending the uses of probability to determine mechanical reliability and achieve optimization. The work solves examples using commercially available software. It is formatted with examples and problems for use
Probability-Based Multi-objective Optimization for Material Selection
by Ying Cui Yi Wang Jie Yu Maosheng Zheng Haipeng TengThis book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection systematically, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies, such as response surface methodology, orthogonal experimental design, and uniform experimental design, are all performed; the conditions of the material performance utility with desirable value and robust assessment are included; the discretization treatment of complicated integral in the evaluation is presented. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. This book can be used as a textbook for postgraduate and advanced undergraduate students in material relevant majors, and a reference book for scientists and engineers digging in the related fields.
Probability-Based Multi-objective Optimization for Material Selection
by Maosheng Zheng Jie Yu Haipeng Teng Ying Cui Yi WangThe second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields.
Probability-Based Structural Fire Load
by Leo Razdolsky P. E. S. E.In the structural design of airframes and buildings, probability-based procedures are used to mitigate the risk of failure as well as produce cost-effective designs. This book introduces the subject of probabilistic analysis to structural and fire protection engineers and can also be used as a reference to guide those applying this technology. In addition to providing an understanding of how fire affects structures and how to optimize the performance of structural framing systems, Probability-Based Structural Fire Load provides guidance for design professionals and is a resource for educators. The goal of this book is to bridge the gap between prescriptive and probability-based performance design methods and to simplify very complex and comprehensive computer analyses to the point that stochastic structural fire loads have a simple, approximate analytical expression that can be used in structural analysis and design on a day-to-day basis. Numerous practical examples are presented in step-by-step computational form.
Probability Concepts and Theory for Engineers
by Harry SchwarzlanderA thorough introduction to the fundamentals of probability theoryThis book offers a detailed explanation of the basic models and mathematical principles used in applying probability theory to practical problems. It gives the reader a solid foundation for formulating and solving many kinds of probability problems for deriving additional results that may be needed in order to address more challenging questions, as well as for proceeding with the study of a wide variety of more advanced topics.Great care is devoted to a clear and detailed development of the 'conceptual model' which serves as the bridge between any real-world situation and its analysis by means of the mathematics of probability. Throughout the book, this conceptual model is not lost sight of. Random variables in one and several dimensions are treated in detail, including singular random variables, transformations, characteristic functions, and sequences. Also included are special topics not covered in many probability texts, such as fuzziness, entropy, spherically symmetric random variables, and copulas.Some special features of the book are:a unique step-by-step presentation organized into 86 topical Sections, which are grouped into six Parts over 200 diagrams augment and illustrate the text, which help speed the reader's comprehension of the material short answer review questions following each Section, with an answer table provided, strengthen the reader's detailed grasp of the material contained in the Section problems associated with each Section provide practice in applying the principles discussed, and in some cases extend the scope of that material an online separate solutions manual is available for course tutors. The various features of this textbook make it possible for engineering students to become well versed in the 'machinery' of probability theory. They also make the book a useful resource for self-study by practicing engineers and researchers who need a more thorough grasp of particular topics.
Probability Foundations for Engineers
by Joel A. NachlasThis textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industrial engineering and operations management students. The book offers theory in an accessible manner and includes numerous practical examples based on engineering applications. Probability Foundations for Engineers, Second Edition continues to focus specifically on probability rather than probability and statistics. It offers a conversational presentation rather than a theorem or proof and includes examples based on engineering applications as it highlights Excel computations. This new edition presents a review of set theory and updates all descriptions, such as events versus outcomes, so that they are more understandable. Additional new material includes distributions such as beta and lognormal, a section on counting principles for defining probabilities, a section on mixture distributions and a pair of distribution summary tables. Intended for undergraduate engineering students, this new edition textbook offers a foundational knowledge of probability. It is also useful to engineers already in the field who want to learn more about probability concepts. An updated solutions manual is available for qualified textbook adoptions.
Probability Foundations for Engineers
by Joel A. NachlasSuitable for a first course in probability theory, this textbook covers theory in an accessible manner and includes numerous practical examples based on engineering applications. The book begins with a summary of set theory and then introduces probability and its axioms. It covers conditional probability, independence, and approximations. An important aspect of the text is the fact that examples are not presented in terms of "balls in urns". Many examples do relate to gambling with coins, dice and cards but most are based on observable physical phenomena familiar to engineering students.
Probability in Electrical Engineering and Computer Science: An Application-Driven Course
by Jean WalrandThis revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book.