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.

Provides a valuable overview of human-machine interaction in technological systems, with particular emphasis on recent advances in theory, experimental and analytical research, and applications related to man-machine systems. Topics covered include: Automation and Operator - task analysis, decision support, task allocation, management decision support, supervisory control, artificial intelligence, training and teaching, expert knowledge; System Concept and Design - software ergonomics, fault diagnosis, safety, design concepts; Man-machine Interface - interface design, graphics and vision, user adaptive interfaces; Systems Operation - process industry, electric power, aircraft, surface transport, prostheses and manual control. Contains 53 papers and three discussion sessions.

Evaluation of Diagnostic Systems: Methods from Signal Detection Theory addresses the many issues that arise in evaluating the performance of a diagnostic system, across the wide range of settings in which such systems are used. These settings include clinical medicine, industrial quality control, environmental monitoring and investigation, machine and metals inspection, military monitoring, information retrieval, and crime investigation. The book is divided into three parts encompassing 11 chapters that emphasize the interpretation of diagnostic visual images by human observers. The first part of the book describes quantitative methods for measuring the accuracy of a system and the statistical techniques for drawing inferences from performance tests. The subsequent part covers study design and includes a detailed description of the form and conduct of an image-interpretation test. The concluding part examines the case study of a medical imaging system that serves as an example of both simple and complex applications. In this part, three mammographic modalities are used: industrial film radiography, low-dose film radiography, and xeroradiography. The case study focuses on the overall reliability of accuracy indices made by its main components, that is, the variabilities across cases, across readers, and within individual readers. The supplementary texts provide study protocols, a computer program for processing test results, and an extensive list of references that will assist the reader in applying those evaluative methods to diagnostic systems in any setting. This book is of value to scientists and engineers, as well as to applied, quantitative, or experimental psychologists who are engaged in the study of the human processes of discrimination and decision making in either perceptual or cognitive tasks.