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The performance, safety and stability of machines depends largely on their design, manufacturing and interaction with environment. Machine foundations should be designed in such a way that the dynamic forces transmitted to the soil through the foundation, eliminating all potentially harmful forces. This handbook is designed primarily for the practising engineers engaged in design of machine foundations. It covers basic fundamentals for understanding and evaluating dynamic response of machine foundation systems with emphasis is on detailed dynamic analysis for response evaulation. Use of commercially available Finite Element packages, for analysis and design of the foundation, is recommended. Theory is supported by results from practice in the form of examples.
Imperfect designing of machine foundations based on empirical formulations has led to the problem of troublesome vibrations in the existing foundations. Recent developments in the field of structural and soil dynamics have helped establish basic design principles for various types of machine foundations. In order to achieve efficiency and economy in the design, it is imperative that the designer have an in depth knowledge of various aspects of analysis, design and construction of machine foundations
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
In his introduction to this book, George R. Harrison, Dean Emeritus of M.I.T.'s School of Science, writes as follows: "Basic to man's behavior is his ability to determine, modify, and adapt to his environment. This he has been able to do in proportion to his skill at making measurements, and fundamental to all other measuring operations is his ability to determine locations in the material world. Thus the science of mechanical measurements is a fundamental one. It is this science, and the art which accompanies and informs it, with which this book is concerned." This is the third book produced by the , Inc., of Bridgeport, Connecticut. Like all of its products, the book is marked by a clean precision of design and execution. The firm has built a worldwide reputation since 1924, both as a manufacturer of special tooling to extremely close accuracies and of machine tools that make possible a very high degree of precision. Wayne R. Moore has assembled in the 350 pages of Foundations of Mechanical Accuracythe company's intimate knowledge of and experience with mechanical accuracy, and how to achieve it. He has illustrated his text with over 500 original photographs and drawings. This book tells how to attain precision in manufacturing to millionths of an inch and how to control such precision by appropriate measuring techniques. The book is divided into four main sections: geometry, standards of length, dividing the circle, and roundness. A fifth section covers "Universal Measuring Machine Techniques and Applications." The book is printed in two colors throughout, and interspersed with full-page, full-color plates.
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
This is the third volume of a handbook which covers the whole field of soil mechanics, discussing deterministic and stochastic theories and methods, and showing how they can be used in conjunction with one another. The first volume discusses soil physics, while the second deals with the determination of physical characteristics of the soil. Australian Mining wrote of the Handbook ``a valuable addition to the extensive literature on the topic and will be found to be more useful than most.''The main objective of the third volume is to present solutions to the problems of engineering practice. It deals with the most important theoretical and practical problems of soil mechanics, discussing the following in detail: stability of earthworks, load-bearing capacity and settlement of shallow foundations, design of pile foundations, soil mechanics in road construction, improving the physical properties of soils, the characteristics of soil dynamics, foundations for machines and soil behaviour as affected by earthquakes. The book not only presents up-to-date deterministic methods, but also discusses solutions of probability theory in the fields of design and safety.The book is divided into six chapters covering the stability of slopes, landslides, load-bearing capacity and settlement of shallow foundations and pile foundations, soil mechanics in road construction, and the improvement of the physical characteristics of soil with special emphasis on machine foundations and earthquakes, giving detailed treatment of each subject. For example, the first chapter deals not only with the stability of slopes, but also discusses the natural and artificial effects, slope protection, filter design, stresses in embankments, and the time factor. In this way, the book gives a clear and comprehensive picture of the special fields of soil mechanics and its subjects. It is therefore emminently suitable for postgraduate engineers, and engineers working in the fields of geotechnics, earthworks, foundations, road construction, engineering geology and statistics, and the design of structures.
The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces— user input involving new media (speech, multi-touch, gestures, writing) embedded in multimodal-multisensor interfaces. These interfaces support smart phones, wearables, in-vehicle and robotic applications, and many other areas that are now highly competitive commercially. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This first volume of the handbook presents relevant theory and neuroscience foundations for guiding the development of high-performance systems. Additional chapters discuss approaches to user modeling and interface designs that support user choice, that synergistically combine modalities with sensors, and that blend multimodal input and output. This volume also highlights an in-depth look at the most common multimodal-multisensor combinations—for example, touch and pen input, haptic and non-speech audio output, and speech-centric systems that co-process either gestures, pen input, gaze, or visible lip movements. A common theme throughout these chapters is supporting mobility and individual differences among users. These handbook chapters provide walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this emerging field. In the final section of this volume, experts exchange views on a timely and controversial challenge topic, and how they believe multimodal-multisensor interfaces should be designed in the future to most effectively advance human performance.