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Fault Diagnosis of Hybrid Dynamic and Complex Systems
Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to p...
Predictive Maintenance in Dynamic Systems
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applic...
Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior
In recent years, there has been growing interest in industrial systems, especially in robotic manipulators and mobile robot systems. As the cost of robots goes down and become more compact, the number of industrial applications of robotic systems increases. Moreover, there is need to design industrial systems with intelligence, autonomous decision making capabilities, and self-diagnosing properties. Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior analyzes current trends in industrial systems design, such as intelligent, industrial, and mobile robotics, complex electromechanical systems, fault diagnosis and avoidance of critical conditions, optimization, and adaptive behavior. This book discusses examples from major areas of research for engineers and researchers, providing an extensive background on robotics and industrial systems with intelligence, autonomy, and adaptive behavior giving emphasis to industrial systems design.
Learning from Data Streams in Evolving Environments
This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.
Learning from Data Streams in Dynamic Environments
This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.
Social Engineering in Cybersecurity
In today’s digitally interconnected world, the threat landscape has evolved to include not just sophisticated technical exploits but also the art of human manipulation. Social engineering attacks have emerged as a formidable and often underestimated threat to information security. The primary aim of this textbook is to provide a comprehensive and in-depth exploration of social engineering attacks. The book seeks to equip cybersecurity professionals, IT practitioners, students, and anyone concerned with information security with the knowledge and tools needed to recognize, prevent, and mitigate the risks posed by social engineering. The scope of this textbook is broad and multifaceted. It c...
Discrete Event Systems 2004 (WODES'04)
Approx. 484 pages
Artificial Neural Networks and Machine Learning - ICANN 2011
This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Artificial Intelligence Techniques for a Scalable Energy Transition
This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).
Deep Learning Applications
This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
