structural health monitoring a machine learning perspective

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Structural Health Monitoring

Author : Charles R. Farrar
ISBN : 9781118443217
Genre : Technology & Engineering
File Size : 89. 72 MB
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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.

Structural Health Monitoring

Author : Charles R. Farrar
ISBN : 9781118443200
Genre : Technology & Engineering
File Size : 82. 61 MB
Format : PDF, ePub
Download : 686
Read : 1096

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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.

Computational Techniques For Structural Health Monitoring

Author : Srinivasan Gopalakrishnan
ISBN : 0857292846
Genre : Computers
File Size : 89. 20 MB
Format : PDF
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The increased level of activity on structural health monitoring (SHM) in various universities and research labs has resulted in the development of new methodologies for both identifying the existing damage in structures and predicting the onset of damage that may occur during service. Designers often have to consult a variety of textbooks, journal papers and reports, because many of these methodologies require advanced knowledge of mechanics, dynamics, wave propagation, and material science. Computational Techniques for Structural Health Monitoring gives a one-volume, in-depth introduction to the different computational methodologies available for rapid detection of flaws in structures. Techniques, algorithms and results are presented in a way that allows their direct application. A number of case studies are included to highlight further the practical aspects of the selected topics. Computational Techniques for Structural Health Monitoring also provides the reader with numerical simulation tools that are essential to the development of novel algorithms for the interpretation of experimental measurements, and for the identification of damage and its characterization. Upon reading Computational Techniques for Structural Health Monitoring, graduate students will be able to begin research-level work in the area of structural health monitoring. The level of detail in the description of formulation and implementation also allows engineers to apply the concepts directly in their research.

Intelligent Sensor Networks

Author : Fei Hu
ISBN : 9781439892824
Genre : Technology & Engineering
File Size : 79. 75 MB
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Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

Machine Learning

Author : Stephen Marsland
ISBN : 1420067192
Genre : Computers
File Size : 31. 70 MB
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Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

Introduction To Uav Systems

Author : Paul Fahlstrom
ISBN : 9781118396810
Genre : Technology & Engineering
File Size : 21. 33 MB
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Unmanned aerial vehicles (UAVs) have been widely adopted in the military world over the last decade and the success of these military applications is increasingly driving efforts to establish unmanned aircraft in non-military roles. Introduction to UAV Systems, 4th edition provides a comprehensive introduction to all of the elements of a complete Unmanned Aircraft System (UAS). It addresses the air vehicle, mission planning and control, several types of mission payloads, data links and how they interact with mission performance, and launch and recovery concepts. This book provides enough information to encourage a student to learn more; to provide a specialist with a basic appreciation of the technical issues that drive other parts of the system and interact with their specialty; or to help a program manager understand system-level tradeoffs and know what questions to ask. Key features: Comprehensive overview of all elements of a UAS and of how they interact. Introduces the underlying concepts of key subsystems. Emphasizes system-integration issues and how they relate to subsystem design choices. Practical discussion of issues informed by lessons learned in UAV programs. Introduction to UAV Systems, 4th edition is written both for newcomers to the subject and for experienced members of the UAV community who desire a comprehensive overview at the system level. As well as being a primary text for an introductory course on UAS or a supplementary text in a course that goes into more depth in one of the individual technologies involved in a UAS, this book is a useful overview for practicing engineers, researchers, managers, and consultants interested in UAV systems.

Nondestructive Evaluation

Author : Peter J. Shull
ISBN : 0203911067
Genre : Technology & Engineering
File Size : 74. 23 MB
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Describing NDE issues associated with real-world applications, this comprehensive book details conventional and forthcoming NDE technologies. It instructs on current practices, common techniques and equipment applications, and the potentials and limitations of current NDE methods. Each chapter details a different method, providing an overview, an explanation of the fundamental physical laws governing the method, the inspection techniques and typical equipment used in it's application, final system integration of transducers, supporting instrumentation, commonly practiced procedures necessary for viable NDE inspection, examples of how the method can be applied, and end-of-chapter problems.

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