Written by global leaders and pioneers in the field, this book is a must-have read for researchers,  practicing engineers and university faculty working in SHM.
Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.
Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors&; detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.
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Charles R Farrar, Los Alamos National Laboratory, New Mexico, USA is currently the director of The Engineering Institute at LANL. His research interests focus on developing integrated hardware and software solutions to structural health monitoring problems and the development of damage prognosis technology. The results of this research have been documented in 50 refereed journal articles, 14 book chapters, more than 100 conference papers, 31 Los Alamos Reports and numerous keynote lectures at international conferences. In 2000 he founded the Los Alamos Dynamics Summer School. His has recently received the inaugural Los Alamos Fellows Prize for Technical Leadership and the inaugural Lifetime Achievement Award in Structural Health Monitoring. He is currently working with engineering faculty at University of California, San Diego to develop the Los Alamos/UCSD Engineering Institute and Education Initiative with a research focus on Damage Prognosis. He is associate editor for the Int. Journal of Structural Health Monitoring and Earthquake Engineering and Structural Dynamics.
Keith Worden, University of Sheffield, UK is Head of the Dynamics Research Group in the Department of Mechanical Engineering at the University of Sheffield. His research interests lie in the applications of advanced signal processing and machine learning methods to structural dynamics. He has authored over 400 research publications including two co-authored books on nonlinear structural dynamics and nonlinear system identification, two book chapters and over 130 refereed journal papers. He serves on the editorial boards of 2 international journals: Journal of Sound and Vibration and Mechanical Systems and Signal Processing. He was awarded "2004 Person of the Year" (jointly with W.J. Staszewski) awarded by Structural Health Monitoring journal for outstanding contribution in the field.
Electrical Installation Designs is the only book on electrical installation practice that uses typical projects to illustrate how to produce designs that comply with current standards. 
This Fourth Edition has been revised and updated to take account of the 2011 Amendment to the Seventeenth Edition of the Wiring Regulations BS 7671: 2008. It offers a  practical guide on how to design and complete a range of electrical installation projects in a  way to ensure  compliance with these new Wiring Regulations. 
Examining projects including domestic, commercial, industrial, agricultural, and leisure complexes, the authors explain the requirements of earthing and bonding, isolation and switching, overcurrent protection and installing cables.  With careful attention on electrical safety requirements, they supply guidance on inspection, testing and certification.
Key features of this new edition: 
The book is a valuable resource for electricians and electrical contractors.  It enables them to adapt standard formats for electrical installations to suit specific jobs.  Designers, consultants, trainers and students may also appreciate the authors&; expert guidance on applying the Wiring Regulations in practice.
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Condizione: New. Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. Num Pages: 654 pages, Illustrations. BIC Classification: TNC. Category: (P) Professional & Vocational. Dimension: 251 x 173 x 35. Weight in Grams: 1112. . 2012. 1st Edition. Hardcover. . . . . Codice articolo V9781119994336
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