This book grew up from the lectures given by the author in the last decade at the Ruhr University Bochum, Germany. Since vibrations and waves are constituents of various disciplines (physics, mechanics, electrical engineering etc.) and cannot be handled in a single textbook, I have restricted myself mainly to vibrations and waves of mechanical nature. The material of this book can be recommended for a one year course in higher dynamics for graduate students of mechanical and civil engineering. For this circle of readers, the emphasis is made on the constructive methods of solution and not on the rigorous mathematical proofs of convergence. As compensation, various numerical simulations of the exact and approximate solutions are provided which demonstrate vividly the validity of the used methods. To help students become more proficient, each chapter ends with exercises, of which some can be solved effectively by using Mathematica.
This second edition includes the solutions to all exercises as well as some new materials concerning amplitude and slope modulations of nonlinear dispersive waves.
“Artfully envisions a breathtakingly better world.” —Los Angeles Times
“Elaborate, smart and persuasive.” —The Boston Globe
“A pleasure to read.” —The Wall Street Journal
One of CBS News’s Best Fall Books of 2005 • Among St Louis Post-Dispatch’s Best Nonfiction Books of 2005 • One of Amazon.com’s Best Science Books of 2005
A radical and optimistic view of the future course of human development from the bestselling author of How to Create a Mind and The Age of Spiritual Machines who Bill Gates calls “the best person I know at predicting the future of artificial intelligence”
For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.
From the Trade Paperback edition.
A Huffington Post Definitive Tech Book of 2013
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.
Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.
From the Hardcover edition.
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
The Mechanical Engineering Reference Manual is the most comprehensive textbook for the Mechanical PE exam. This book's time-tested organization and clear explanations start with the basics to help you quickly get up to speed on common mechanical engineering concepts. Together, the 76 chapters provide an in-depth review of NCEES Mechanical PE exam topics. The extensive index contains thousands of terms, most indexed in a variety of ways, in anticipation of how you'll search for them.
Features of the Mechanical Engineering Reference Manual: • over 120 appendices containing essential support material • over 375 clarifying example problems • thousands of equations, figures, and tables • industry-standard terminology and nomenclature • equal support of U.S. customary and SI units
After you pass your exam, the Mechanical Engineering Reference Manual will continue to serve as an invaluable reference throughout your mechanical engineering career.
Topics Covered: • Dynamics and Vibrations: Kinematics; Kinetics; Power Transmission Systems; Vibrating Systems • Materials: Engineering Materials Properties and Testing; Thermal Treatment of Metals • Fluids: Fluid Properties; Fluid Statics; Fluid Flow Parameters; Fluid Dynamics; Hydraulic Machines • Power Cycles: Vapor, Combustion, and Nuclear Power Cycles; Refrigeration and Gas Compression Cycles • HVAC: Psychrometrics; Fans, Ductwork, and Ventilation; Heating and Cooling Loads; Air Conditioning Systems • Heat Transfer: Natural Convection; Evaporation; Condensation; Forced Convection; Radiation • Machine Design: Basic and Advanced Machine Design; Pressure Vessels • Thermodynamics: Inorganic Chemistry; Fuels and Combustion; Properties of Substances • Control Systems: Modeling and Analysis of Engineering Systems • Plant Engineering: Manufacturing Processes; Instrumentation and Measurements; Materials Handling and Processing; Fire Protection Systems; Environmental Pollutants and Remediation; Hazardous Material Storage and Disposal • Fundamentals: Math Review; Probability; Statics; Engineering Economic Analysis • Law and Ethics: Engineering Law; Ethics
What's New in This Edition: • 36 chapters with new material, and 46 chapters with revisions to existing material • 300 new equations, and 128 updated equations • 27 new tables, and 31 updated tables • 7 new examples, and 34 updated examples • 10 new appendices, and 27 updated appendices • 35 new figures, and 28 updated figures • 1,094 new index entries, and 108 updated index entries
Illustrating turbomachinery, vibration severity levels, condition monitoring, and rotor vibration cause identification, Rotating Machinery Vibration
Provides a primer on vibration fundamentals
Highlights calculation of rotor unbalance response and rotor self-excited vibration
Demonstrates calculation of rotor balancing weights
Furnishes PC codes for lateral rotor vibration analyses
Treats bearing, seal, impeller, and blade effects on rotor vibration
Describes modes, excitation, and stability of computer models
Includes extensive PC data coefficient files on bearing dynamics
Providing comprehensive descriptions of vibration symptoms for rotor unbalance, dynamic instability, rotor-stator rubs, misalignment, loose parts, cracked shafts, and rub-induced thermal bows, Rotating Machinery Vibration is an essential reference for mechanical, chemical, design, manufacturing, materials, aerospace, and reliability engineers; and specialists in vibration, rotating machinery, and turbomachinery; and an ideal text for upper-level undergraduate and graduate students in these disciplines.
Combining leading research from both academia and industry onto a single platform, Energy Harvesting Technologies serves as an important reference for researchers and engineers involved with power sources, sensor networks and smart materials.
Following the routine of a normal day, from coffee and breakfast to shopping, household chores, sports, a drink, supper, and a bath, we see how the seemingly mundane can provide insight into the most profound scientific questions. Some of the topics included are the art and science of dunking; how to boil an egg; how to tally a supermarket bill; the science behind hand tools; catching a ball or throwing a boomerang; the secrets of haute cuisine, bath (or beer) foam; and the physics of sex. Fisher writes with great authority and a light touch, giving us an entertaining and accessible look at the science behind our daily activities.
Two physically different dampers (magnetorheological and controlled-friction) are analysed from the perspectives of mechatronics and control. Ride comfort, road holding, road damage and human-body modelling (nonlinear visceral response in particular) are studied.
A multidisciplinary approach is adopted throughout the book. Sound mathematical modelling is balanced by a large and detailed section on experimental implementation, where a variety of automotive applications are described offering a well-rounded view of the application of such systems. The implementation of control algorithms with regard to real-life engineering constraints is emphasised.
The applications described include semi-active suspensions for a saloon car, seat suspensions for vehicles not equipped with a primary suspension, and control of heavy-vehicle dynamic-tyre loads to reduce road damage and improve handling.
Engineers and practitioners working in noise and vibration; automotive engineers working in vehicle design, research and development; biomechanical engineers, physicists and life-scientists interested in human-body responses to vibration; and graduate students in vehicle studies, mechanics of vibration, dynamics and control will find this book of material assistance in their work.
From the Trade Paperback edition.
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
Integrated throughout the text are real-world applications that emphasize the relevance of thermodynamics principles to some of the most critical problems and issues of today, including a wealth of coverage of topics related to energy and the environment, biomedical/bioengineering, and emerging technologies.
In an age of custom-fabricated, do-it-yourself product design and creation, the collective potential of a million garage tinkerers and enthusiasts is about to be unleashed, driving a resurgence of American manufacturing. A generation of “Makers” using the Web’s innovation model will help drive the next big wave in the global economy, as the new technologies of digital design and rapid prototyping gives everyone the power to invent--creating “the long tail of things”.
• Covers all aspects of the dynamics of surface processes
• Provides understanding of this unique field utilizing a multitude of accurate experiments and advanced microscopic theory that allows quantum-level comparisons
• Presents the concepts and tools relevant beyond surface science for catalysis, nanotechnology, biology, medicine, and materials
Two editions later, this bestselling handbook is still the most trusted and widely embraced guide in the field. The third edition was reorganized, updated, and expanded to be more convenient, intuitive, and to reflect the latest developments in the area. Dedicated chapters now discuss the basics of alignment modeling, each of the five basic alignment methods, and electro-optic methods. Significant new material reflects recent findings on detecting misalignment, machinery movement from offline to running conditions, multiple element drive trains, and specific information on virtually every type of rotating machinery in existence. Entirely new chapters explore bore and parallel alignment.
Providing detailed guidance based on years of hands-on experience, the Shaft Alignment Handbook, Third Edition is a practical tool to help avoid costly shutdowns, dangerous failures, and early replacements.
Beginning with current training needs, Mechanical Design reinforces the fundamentals of the design of mechanical components. It employs an analytical approach to the subjects based on algorithms from traditional calculus without extensive reference to more current methodologies. This gives students of the ability to use simple models and calculations that are reliably effective and helpful at times when more complicated algorithms or well-known commercial programs need to be used.
Emphasizing logical and analytical thinking, students start by analyzing the physical problem with the most appropriate schematic and end with a constructional definition of the component in need of planning. Typical Machine Construction course subjects/modules occupy the greater part of this book (mechanical system component planning), but two preliminary sections enhance its appeal: the methodological set-up of the project (traditional or more recent developments), and the project criteria that take into account environmental concerns. To comply with the requirements of the new teaching regulations, the principal materials tests and simple stress states are outlined prior to the study of fatigue, which refers to fine-tuning methods developed at Catania’s Faculty of Engineering. Two useful appendices group tables of the general properties of metallic materials, and there are various applications whose theoretical methods and tools are applied to the planning of real mechanical systems.
As long as conflict exists in the world, protection technologies will always be in demand. Armour: Materials, Theory, and Design describes the existing and emerging protection technologies that are currently driving the latest advances in armour systems. This book explains the theory, applications, and material science aspects of modern armour design as they are used in relation to vehicles, ships, personnel, and buildings, and explores the science and technology used to provide protection against blasts and ballistic attacks. It covers materials technologies used in protection; addresses the system effects of adding blast-wave shaping to vehicles, as well as the effect on the human body; and outlines ballistic testing techniques.
Takes a Look at How Armour Works
The book discusses ceramics for armour applications; transparent armour; and metals for armour applications (including aluminium alloys, magnesium alloys, titanium alloys and steels); as well as composite armour systems; explosive reactive armour systems with reference to defensive aid suites for vehicles; and wound ballistics. In addition, the author lists more than 100 references for advanced study and further reading.
Armour: Materials, Theory, and Designintroduces a variety of armour technologies, outlines modern threats and dangers applicable to protection technology, and aids readers in implementing protective structures that can be used in battle, conflict, military zones, and other related environments.
What’s New in This EditionRevised, expanded, and updated information in every chapter Advances in feedforward control algorithms, DSP hardware, and applications Practical application examples of active control of noise propagating in ducts The use of a sound intensity cost function, model reference control, sensing radiation modes, modal filtering, and a comparison of the effectiveness of various sensing strategies New material on feedback control of sound transmission into enclosed spaces New material on model uncertainty, experimental determination of the system model, optimization of the truncated model, collocated actuators and sensors, biologically inspired control, and a discussion of centralised versus de-centralised control A completely revised chapter on control system implementation New material on parametric array loudspeakers, turbulence filtering, and virtual sensing More material on smart structures, electrorheological fluids, and magnetorheological fluids
Integrating the related disciplines of active noise control and active vibration control, this comprehensive two-volume set explains how to design and implement successful active control systems in practice. It also details the pitfalls one must avoid to ensure a reliable and stable system.
Topics addressed are theoretical kinematics, computational kinematics, mechanism design, experimental mechanics, mechanics of robots, dynamics of machinery, dynamics of multi-body systems, control issues of mechanical systems, mechanisms for biomechanics, novel designs, mechanical transmissions, linkages and manipulators, micro-mechanisms, teaching methods, history of mechanism science and industrial and non-industrial applications.
This volume will also serve as an interesting reference for the European activity in the fields of Mechanism and Machine Science as well as a source of inspirations for future works and developments.
Drawn from the immensely popular Vibration and Shock Handbook, each expertly crafted chapter of this book includes convenient summary windows, tables, graphs, and lists to provide ready access to the important concepts and results. Working systematically from general principles to specific applications, coverage spans from theory and experimental techniques in vibration damping to isolation, passive control, active control, and structural dynamic modification. The book also discusses specific issues in designing for and controlling vibrations and noise such as regenerative chatter in machine tools, fluid-induced vibration, hearing and psychological effects, instrumentation for monitoring, and statistical energy analysis. This carefully edited work strikes a balance between practical considerations, design issues, and experimental techniques.
Complemented by design examples and case studies, Vibration Damping, Control, and Design builds a deep understanding of the concepts and demonstrates how to apply these principles to real systems.
Doctors Savaresi and Tanelli show how these different control approaches are complementary, each having specific peculiarities in terms of either performance or of the structural properties of the closed-loop system. They also consider other problems closely related to the design of braking control systems, namely:
• longitudinal vehicle speed estimation and its relationship with braking control system design;
• tire–road friction estimation;
• direct estimation of tire–road contact forces via in-tire sensors, with the aim of providing a comprehensive treatment of active vehicle braking control from a wider perspective linked to both advanced academic research and industrial reality.
The high degree of cooperation with the automotive industry in the research which generated much of the work presented here, coupled with survey sections in which problems and methodologies are introduced in a historical and tutorial framework, makes this book accessible from three standpoints: a methodological one for academic research; an application-oriented one for automotive engineers and practitioners; and a source of study and tuition for graduate students interested in vehicle control systems.
Furthermore, the following topics are included:
- installing seismic stations, networks, and arrays
- working with portable instruments
- calibrating instruments
- dealing with response information
- and teaching about seismic instruments.
This book is primarily intended for seismologists and technicians working with seismological instruments. The aim is that it should be used as a practical tool with only the amount of theory needed to understand the basic principles, and the answers to most practical problems should be found here.
This book is written in such a way that it is understandable for a broad range of professionals working with seismological instruments and seismic data, whether students, engineers, or seismologists. Both practical and theoretical topics are covered, with the main emphasis on practical aspects. The book is based on the authors' (seismologist and physicist) experience working in this field for more than 25 years.
SEISAN and SEISLOG software systems that are available from http://extras.springer.com and http://www.geo.uib.no/seismo/software/software.html.
In the second edition of the book, chapters on roll dynamics, rollover prevention and hybrid electric vehicles have been added, and the chapter on electronic stability control has been enhanced.
The use of feedback control systems on automobiles is growing rapidly. This book is intended to serve as a useful resource to researchers who work on the development of such control systems, both in the automotive industry and at universities. The book can also serve as a textbook for a graduate level course on Vehicle Dynamics and Control.
The book is addressed to graduate level, PhD students and young researchers interested in structural dynamics, vibrations and acoustics. It is also suitable for industrial researchers in mechanical, aeronautical and civil engineering with a professional interest in structural dynamics, vibrations and acoustics or involved in questions regarding noise characterization and reduction in building, car, plane, space, train, industries by means of computer simulations.
A key objective of these volumes, which present a vector treatment of the principles of mechanics, is to help the student gain confidence in transforming problems into appropriate mathematical language that may be manipulated to give useful physical conclusions or specific numerical results. In the first volume, the elements of vector calculus and the matrix algebra are reviewed in appendices. Unusual mathematical topics, such as singularity functions and some elements of tensor analysis, are introduced within the text. A logical and systematic building of well-known kinematic concepts, theorems, and formulas, illustrated by examples and problems, is presented offering insights into both fundamentals and applications. Problems amplify the material and pave the way for advanced study of topics in mechanical design analysis, advanced kinematics of mechanisms and analytical dynamics, mechanical vibrations and controls, and continuum mechanics of solids and fluids.
Volume I of Principles of Engineering Mechanics provides the basis for a stimulating and rewarding one-term course for advanced undergraduate and first-year graduate students specializing in mechanics, engineering science, engineering physics, applied mathematics, materials science, and mechanical, aerospace, and civil engineering. Professionals working in related fields of applied mathematics will find it a practical review and a quick reference for questions involving basic kinematics.
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.What You Will LearnInstall and adopt TensorFlow in your Python environment to solve mathematical problemsGet to know the basic machine and deep learning conceptsTrain and test neural networks to fit your data modelMake predictions using regression algorithmsAnalyze your data with a clustering procedureDevelop algorithms for clustering and data classificationUse GPU computing to analyze big dataIn Detail
Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.
This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.
By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.Style and approach
Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.
This book presents the most recent research advances in Mechanism Science with different applications. Amongst the topics treated are papers on Theoretical kinematics, Computational kinematics, Mechanism design, Mechanical transmissions, Linkages and manipulators, Mechanisms for biomechanics, Micro-mechanisms, Experimental mechanics, Mechanics of robots, Dynamics of multi-body systems, Dynamics of machinery, Control issues of mechanical systems, Novel designs, History of mechanism science etc.
This volume offers an international selection of the most important new results and developments, in 73 papers, grouped in seven different parts, representing a well-balanced overview, and spanning the general theory of machines and mechanisms, through analysis and synthesis of planar and spatial mechanisms, dynamics of machines and mechanisms, linkages and cams, computational mechanics, rotor dynamics, biomechanics, mechatronics, vibration and noise in machines, optimization of mechanisms and machines, control and monitoring systems of machines, accuracy and reliability of machines and mechanisms, robots and manipulators to the mechanisms of textile machines.
•Chassis Vibration and Noise Control
•Transmission Vibration and Noise Control
•Engine Vibration and Noise Control
•Body Vibration and Noise Control
•Vehicle Vibration and Noise Control
•Analysis and Evaluation of In-Car Vibration & Noise
•Wind Noise Control Technology
•Vibration and Noise Testing Technology
Above all researchers, professional engineers and graduates in fields of automotive engineering, mechanical engineering and electronic engineering will benefit from this book.
SAE-China is a national academic organization composed of enterprises and professionals who focus on research, design and education in the fields of automotive and related industries. FISITA is the umbrella organization for the national automotive societies in 37 countries around the world. It was founded in Paris in 1948 with the purpose of bringing engineers from around the world together in a spirit of cooperation to share ideas and advance the technological development of the automobile.
Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.comDemystifies data mining concepts with easy to understand languageShows how to get up and running fast with 20 commonly used powerful techniques for predictive analysisExplains the process of using open source RapidMiner toolsDiscusses a simple 5 step process for implementing algorithms that can be used for performing predictive analyticsIncludes practical use cases and examples
"Anyone who wants to understand what's coming must read this fascinating book." -- Martin Ford, New York Times bestselling author of Rise of the Robots
In the year 2014, Google fired a shot heard all the way to Detroit. Google's newest driverless car had no steering wheel and no brakes. The message was clear: cars of the future will be born fully autonomous, with no human driver needed. In the coming decade, self-driving cars will hit the streets, rearranging established industries and reshaping cities, giving us new choices in where we live and how we work and play.
In this book, Hod Lipson and Melba Kurman offer readers insight into the risks and benefits of driverless cars and a lucid and engaging explanation of the enabling technology. Recent advances in software and robotics are toppling long-standing technological barriers that for decades have confined self-driving cars to the realm of fantasy. A new kind of artificial intelligence software called deep learning gives cars rapid and accurate visual perception. Human drivers can relax and take their eyes off the road.
When human drivers let intelligent software take the wheel, driverless cars will offer billions of people all over the world a safer, cleaner, and more convenient mode of transportation. Although the technology is nearly ready, car companies and policy makers may not be. The authors make a compelling case for why government, industry, and consumers need to work together to make the development of driverless cars our society's next "Apollo moment."
Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or mediaMethods of clustering to detect groups of similar items in a large datasetSearch engine features -- crawlers, indexers, query engines, and the PageRank algorithmOptimization algorithms that search millions of possible solutions to a problem and choose the best oneBayesian filtering, used in spam filters for classifying documents based on word types and other featuresUsing decision trees not only to make predictions, but to model the way decisions are madePredicting numerical values rather than classifications to build price modelsSupport vector machines to match people in online dating sitesNon-negative matrix factorization to find the independent features in a datasetEvolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a gameEach chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.
"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google
"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect
In the beginning was Josh Levine, an idealistic programming genius who dreamed of wresting control of the market from the big exchanges that, again and again, gave the giant institutions an advantage over the little guy. Levine created a computerized trading hub named Island where small traders swapped stocks, and over time his invention morphed into a global electronic stock market that sent trillions in capital through a vast jungle of fiber-optic cables.
By then, the market that Levine had sought to fix had turned upside down, birthing secretive exchanges called dark pools and a new species of trading machines that could think, and that seemed, ominously, to be slipping the control of their human masters.
Dark Pools is the fascinating story of how global markets have been hijacked by trading robots--many so self-directed that humans can't predict what they'll do next.
Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included.
Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth.
Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters.
Contents include:PointersRecursionAnalysis of algorithmsData structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs)Sorting and searchingNumerical methodsData compressionData encryptionGraph algorithmsGeometric algorithms