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.

Theory of vibrations. Wave propagation in an elastic medium. Dynamic soil properties. Unbalanced forces for design of machine foundations. Foundations for reciprocating machines. Foundations for impact machines. Foundations for high-speed rotary machines. Foundations for miscellaneous types of machines. Vibration absorption and isolation. Dynamic response of embedded block foundations. Machine foundations on piles. Case histories. Construction of machine foundations. Computer program for the design of a block foundation. Computer program for the design of a hammer foundation. Brief description of some available computer programs. Computation of moment of inertia. Conversion factors.

This text brings together traditional and new concepts and procedures for analyzing and designing dynamically loaded structures.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

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.