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Structural Health Monitoring - A Machine Learning Perspective Original English version

Structural Health Monitoring - A Machine Learning Perspective Original English version
Structural Health Monitoring - A Machine Learning Perspective Original English version
US$113.20
  • Stock: 100
  • Model: 10044913079600
  • Weight: 1.11kg
  • Dimensions: 24.90cm x 17.50cm x 3.30cm

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Structural Health Monitoring: A Machine Learning Perspective

作者: Farrar, Charles R.
出版社: Wiley (2012年12月26日)
页数: 654页
ISBN: 9781119994336
条形码: 9781119994336
商品重量: 1.11g
商品尺寸: 24.89cm * 17.53cm * 3.30cm

内容简介
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.

  • Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
  • Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
  • Benefits from extensive use of the authors' detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.


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作者简介
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.


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Standard Size
China (cm) 160-165/84-86 165-170 / 88-90 167-172 / 92-96 168-173 / 98-102 170-176 / 106-110
International XS S M L XL
USA 2 4-6 8-10 12-14 16-18
Europe 34 34-36 38-40 42 44
Bra - Under bust
Standard Size
China
(cm)
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USA 30 32 34 36 38 40 42 44 46 48 50 52 54 56
UK 30 32 34 36 38 40 42 44 46 48 50 52 54 56
Europe   70 75 80 85 90                
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Standard Size
China A B C D E                  
USA AA A B C D DD DDD/E F FF G GG H HH J
UK AA A B C D DD E F FF G GG H HH J
Europe AA A B C D E F              
France AA A B C D E F              
Italy   B B/none C D DD E F            
Women's Underwear
Standard Size
China S M L XL XXL XXXL
International XS S M L XL XXL
USA 2 4 6 8 10 12
UK 6 8 10 12 14 16
Europe 32 34 36 38 40 42
France 34 36 38 40 42 44
Italy 38 40 42 44 46 48
Women's Shoes
Standard Size
Length (cm) 22.8 23.1 23.5 24.1 24.5 25.1 25.7 26 26.7 27.3 27.9 28.6 29.2
China 35.5 36 37 38 39 40 41.5 42 43 44.5 46 47 48
USA 5 5.5 6 7 7.5 8.5 9.5 10 10.5 12 13 14 15.5
UK 2.5 3 3.5 4.5 5 6 7 7.5 8 9.5 10.5 11.5 13
Europe 35 35.5 36 37.5 38 39 41 42 43 44 45 46.5 48.5
Men's Clothing (Coats & Jacketst,T-Shirts,Suits)
Standard Size
China (cm) 165 / 88-90 170 / 96-98 175 / 108-110 180 / 118-122 185 / 126-130
International S M L XL XXL
Men's Clothing (Shirts)
Standard Size
China (cm) 36 - 37 38 - 39 40 - 42 43 - 44 45 - 47
International S M L XL XXL
Men's Clothing (Pants)
Size (cm) 42 44 46 48 50
Waist (cm) 68 - 72 cm 71 - 76 cm 75 - 80 cm 79 - 84 cm 83 - 88 cm
Outseam (cm) 99 cm 101.5 cm 104 cm 106.5 cm 109 cm
Men's Underwear
Standard Size
China(cm) 72-76 76-81 81-87 87-93 93-98
International S M L XL XXL
USA(inch) 28-30 30-32 32-34 34-38 38-42
Men's Shoes
Standard Size
Length(cm) 24.5 25.1 25.7 26 26.7 27 27.6 28.3 28.6 28.9
China 39.5 41 42 43 44 44.5 46 47 47.5 48
USA 6 7 8 8.5 9.5 10 11 12 12.5 13
UK 5.5 6.5 7.5 8 9 9.5 10.5 11.5 12 12.5
Europe 38 39 41 42 43.5 44 45 46 46.5 47
长度Length
Imperial英制 Meric公制
1 inch[in] 英寸 ---- 2.54 cm 厘米
1 foot[ft] 英尺 12 in 英寸 0.03048 m 米
1 yard[yd] 码 3 ft 英尺 0.9144 m 米
1 mile[mi] 英里 1760 yd 码 1.6093 km 千米
1 int nautical mile[inm] 海里 2025.4 yd 码 1.853 km 千米
面积Area
Imperial英制 Meric公制
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1 sq foot[ft2] 平方英尺 144 in2 平方英寸 0.0929 m2 平方米
1 sq yard[yd2] 平方码 9 ft2 平方英尺 0.8361 m2 平方米
1 acre 英亩 4840 yd2 平方码 4046.9 m2 平方米
1 sql mile[mile2] 平方英里 640 acre 英亩 2.59 km2 平方千米
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Imperial英制 Meric公制
1 fluid ounce 液量蛊司 1.048 UK fl oz 英制液量蛊司 29.574 ml 毫升
1 pint(16 fl oz 液量品脱)品脱 0.8327 UK pt 英制品脱 0.4731 l 升
1 gallon 加仑 0.8327 UK gal 英制加仑 3.7854 l 升
重量Weight
Imperial英制 Meric公制
1 ounce[oz]蛊司 437.5 grain 格令 28.35 g 克
1 pound[lb]磅 16 oz 蛊司 0.4536 kg 千克
1 stone 石 14 lb 磅 6.3503 kg 千克
1 hundredweight[cwt] 英担 112 lb 磅 50.802 kg 千克
1 long ton(UK) 长顿 20 cwt 英担 1.061 t 顿