Knowledge-Driven Board-Level Functional Fault Diagnosis PDF Download
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Author: Fangming Ye
Publisher: Springer
ISBN: 3319402102
Category : Technology & Engineering
Languages : en
Pages : 154
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Book Description
This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system design. • Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing;• Demonstrates techniques based on industrial data and feedback from an actual manufacturing line;• Discusses practical problems, including diagnosis accuracy, diagnosis time cost, evaluation of diagnosis system, handling of missing syndromes in diagnosis, and need for fast diagnosis-system development.
Author: Fangming Ye
Publisher: Springer
ISBN: 3319402102
Category : Technology & Engineering
Languages : en
Pages : 154
Get Book Here
Book Description
This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system design. • Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing;• Demonstrates techniques based on industrial data and feedback from an actual manufacturing line;• Discusses practical problems, including diagnosis accuracy, diagnosis time cost, evaluation of diagnosis system, handling of missing syndromes in diagnosis, and need for fast diagnosis-system development.
Author: Zhi-Hua Zhou
Publisher: Springer Science & Business Media
ISBN: 3540717005
Category : Computers
Languages : en
Pages : 2367
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Book Description
This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.
Author: Tshilidzi Marwala
Publisher: World Scientific
ISBN: 9813271248
Category : Computers
Languages : en
Pages : 329
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Book Description
This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.
Author: Shai Shalev-Shwartz
Publisher: Foundations & Trends
ISBN: 9781601985460
Category : Computers
Languages : en
Pages : 88
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Book Description
Online Learning and Online Convex Optimization is a modern overview of online learning. Its aim is to provide the reader with a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms.
Author:
Publisher: ScholarlyEditions
ISBN: 146496470X
Category : Computers
Languages : en
Pages : 1750
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Book Description
Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Artificial Intelligence, Robotics and Machine Learning. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence, Robotics and Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Author: Paolo Remagnino
Publisher: Springer Science & Business Media
ISBN: 3642182771
Category : Computers
Languages : en
Pages : 223
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Book Description
This book is a continuation of our previous volumes on Innovations in Defence Support Systems. This book includes a sample of recent advances in intelligent monitoring. The contributions include: · Data fusion in modern surveillance · Distributed intelligent surveillance systems modeling for performance evaluation · Incremental learning on trajectory clustering · Pedestrian speed profiles from video sequence · System-wide tracking of individuals · A scalable approach based on normality components for intelligent surveillance · Distributed camera overlap estimation · Multi-robot team for environmental monitoring The book is directed to the security experts, engineers, scientists, students and professors who are interested in intelligent monitoring.
Author: Dominic Palmer-Brown
Publisher: Springer Science & Business Media
ISBN: 3642039693
Category : Computers
Languages : en
Pages : 508
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Book Description
A cursory glance at the table of contents of EANN 2009 reveals the am- ing range of neural network and related applications. A random but revealing sample includes: reducing urban concentration, entropy topography in epil- tic electroencephalography, phytoplanktonic species recognition, revealing the structure of childhood abdominal pain data, robot control, discriminating angry and happy facial expressions, ?ood forecasting, and assessing credit worthiness. The diverse nature of applications demonstrates the vitality of neural comp- ing and related soft computing approaches, and their relevance to many key contemporary technological challenges. It also illustrates the value of EANN in bringing together a broad spectrum of delegates from across the world to learn from each other’s related methods. Variations and extensions of many methods are well represented in the proceedings, ranging from support vector machines, fuzzy reasoning, and Bayesian methods to snap-drift and spiking neurons. This year EANN accepted approximately 40% of submitted papers for fu- length presentation at the conference. All members of the Program Committee were asked to participate in the reviewing process. The standard of submissions was high, according to the reviewers, who did an excellent job. The Program and Organizing Committees thank them. Approximately 20% of submitted - pers will be chosen, the best according to the reviews, to be extended and - viewedagainfor inclusionin a specialissueofthe journalNeural Computing and Applications. We hope that these proceedings will help to stimulate further research and development of new applications and modes of neural computing.
Author: Li Ning
Publisher: Springer Nature
ISBN: 9811600104
Category : Computers
Languages : en
Pages : 453
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Book Description
This book constitutes the refereed proceedings of the 11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020, held in Shenzhen, China, in December 2020. The 37 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers deal with research results and development activities in all aspects of parallel architectures, algorithms and programming techniques.
Author: Moonis Ali
Publisher: Springer
ISBN: 3319006517
Category : Technology & Engineering
Languages : en
Pages : 219
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Book Description
Since its origination in the mid-twentieth century, the area of Artificial Intelligence (AI) has undergone a number of developments. While the early interest in AI was mainly triggered by the desire to develop artifacts that show the same intelligent behavior as humans, nowadays scientists have realized that research in AI involves a multitude of separate challenges, besides the traditional goal to replicate human intelligence. In particular, recent history has pointed out that a variety of ‘intelligent’ computational techniques, part of which are inspired by human intelligence, may be successfully applied to solve all kinds of practical problems. This sub-area of AI, which has its main emphasis on applications of intelligent systems to solve real-life problems, is currently known under the term Applied Intelligence. The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE) is to promote and disseminate recent research developments in Applied Intelligence. The current book contains 30 chapters authored by participants of the 26th edition of IEA/AIE, which was held in Amsterdam, the Netherlands. The material of each chapter is self-contained and was reviewed by at least two anonymous referees, to assure a high quality. Readers can select any individual chapter based on their research interests without the need of reading other chapters. We are confident that this book provides useful reference values to researchers and students in the field of Applied Intelligence, enabling them to find opportunities and recognize challenges in the field.
Author: Ron Sun
Publisher: Springer
ISBN: 354044565X
Category : Computers
Languages : en
Pages : 400
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Book Description
Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.
