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Books in Engineering general

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    • Machine Learning in the Analysis of Solid Deformation, Fatigue and Fracture

      • 1st Edition
      • January 1, 2026
      • Guozheng Kang + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 6 1 5 3
      • eBook
        9 7 8 0 4 4 3 4 4 6 1 6 0
      Machine Learning in the Analysis of Solid Deformation, Fatigue and Fracture fills a clear gap in literature by applying machine learning to deformation, fatigue, and fracture analysis in solid mechanics. The book’s focus on complex mechanisms and coupling phenomena, discussed with practical examples, makes it a valuable resource for advanced researchers. Practical examples and case studies enable readers to understand both the underlying engineering problems and the application of machine learning methods to enhance fatigue life prediction analysis for solid materials and structures.

    • Engineering Materials for 3D Printing

      • 1st Edition
      • January 1, 2026
      • Rupinder Singh + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 6 2 5 6
      • eBook
        9 7 8 0 4 4 3 3 3 6 2 6 3
      3D printing processes have promising advantages over conventional manufacturing processes from a functional ability viewpoint. In the past 30 years, a variety of additive manufacturing-based 3D printing processes have been reported for the fabrication of functional prototypes, using materials such as thermoplastic, resins, metal and their alloys. While there is a significant amount of information on various 3D printing processes, and materials that may be used in 3D printing-based manufacturing practices for engineering applications, there is far less information on 3D printable materials for structural and non-structural engineering applications. Engineering Materials for 3D Printing fills this gap. The book includes three sections and provides an insight on the development and characterization of polymer-based 3D printed materials by different processes such as extrusion, sol-gel, and pulverization along with meta-structure properties for various structural applications

    • Industrial Fault Diagnosis and Remaining Useful Life Prediction

      • 1st Edition
      • February 1, 2026
      • Hongpeng Yin + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 2 9 1 9
      • eBook
        9 7 8 0 4 4 3 4 4 2 9 2 6
      Industrial Fault Diagnosis and Remaining Useful Life Prediction: Cross-Domain, Zero-Sample, and Degradation Modeling Methods introduces zero-sample learning methods that enable fault diagnosis and Predict Remaining Useful Life (RUL) without the need for labelled fault data. This is particularly valuable in industrial settings where labelled data is scarce or unavailable. Offers step-by-step guidance on implementing zero-shot learning models using real industrial data, reducing the learning curve for practitioners; includes real-world industrial case studies to demonstrate the application of zero-sample learning techniques in various industries, such as manufacturing, energy, and transportation. Such case studies provide readers with actionable insights and practical solutions. The book covers advanced methodologies for predicting the remaining useful life of industrial equipment, supporting readers in optimizing maintenance schedules, reducing downtime and extending the lifespan of critical assets. Covers state-of-the-art algorithms, including deep learning, transfer learning and domain adaptation, tailored for zero-sample scenarios. These tools empower readers to develop robust fault diagnosis and RUL prediction systems, enhancing predictive maintenance capabilities and ensuring the reliability of industrial systems

    • BioMEMS Devices

      • 1st Edition
      • February 1, 2026
      • Azrul Azlan Hamzah + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 4 0 1 5 7
      • eBook
        9 7 8 0 4 4 3 2 4 0 1 4 0
      BioMEMS Devices covers the fundamentals of bioMEMS and discusses how MEMS are implemented into biomedical devices. The book discusses the most up-to-date technologies, including the various types of electrochemical, optical, and mechanical MEMS sensors. Other topics include bio cell and particle separation and filtration using microfluidic platform such as dielectrophoresis (DEP) and microfilters. MEMS devices for drug mixing, pumping and transdermal drug delivery using microneedles and micropumps are also discussed, along with recent advances in point-of-care diagnostics and MEMS in medical implantsThe book concludes with a discussion on integration of bioMEMS system with IoT, medical practitioners’ adaptation of the devices, market penetration, emerging technologies, and future outlooks.

    • The Control of Multiple Unmanned Aerial Vehicles

      • 1st Edition
      • January 1, 2026
      • Mingyang Xie + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 0 4 3 3 7
      • eBook
        9 7 8 0 4 4 3 4 0 4 3 4 4
      The Control of Multiple Unmanned Aerial Vehicles provides readers with a unified framework to address the challenges of multi-UAV target encirclement tracking control. This is achieved by comprehensively integrating the concepts of dynamic modelling, state and disturbance estimation, guidance law design, distributed control, obstacle avoidance and reinforcement learning-based formation tracking control with multi-UAV encirclement tracking systems. The book provides practical insight and solutions to the challenges of multi-UAV use, including limited sensing, communication constraint, wind disturbances and obstacle and collision avoidance, enabling readers to effectively apply the concepts to their work or research. Cutting-edge control techniques and advanced theories are explored, including robust distributed control, adaptive neural network approximation, reinforcement learning and multi-agent systems, presenting readers with state-of-the-art methodologies from which to develop practical applications of UAV formation. Each guidance and control method presented in the book is accompanied by a thorough comprehensive analysis to ensure that each is theoretically grounded. The book ensures transparency by providing open-access simulation codes, allowing readers to easily access and reproduce presented results.

    • Artificial Intelligence Methods in Railway Infrastructure Systems

      • 1st Edition
      • March 1, 2026
      • Diogo Ribeiro + 5 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 7 7 9 6
      • eBook
        9 7 8 0 4 4 3 3 3 7 8 0 2
      With the rapid recent advances in the field of railway systems and infrastructure construction, and the evolution of AI tools that have enormous potential for application to railway design, maintenance and operations, industry professionals and researchers need an up-to-date resource on these developments. Artificial Intelligence Methods in Railway Infrastructure Systems: Application of Data Centric Engineering addresses this need. The book encapsulates the latest breakthroughs and contributions in these pivotal areas, providing readers with comprehensive insights into the cutting-edge methodologies and approaches shaping the field of railway infrastructure management. For engineers and researchers, the book provides a focused explanation of AI methodologies such as machine learning, computer vision and predictive analytics and their implementation to railway infrastructure development, tools that are new to this field. It combines theory with practical examples of the application of data centric engineering in structural health monitoring of monitoring of railway systems, thus enabling early anomaly detection and empowering infrastructure managers to address potential issues before they escalate. Given the expansive scope of research driving technological advancements in railway infrastructure management, this book serves as a reference for readers seeking to explore novel AI-based methodologies and harness their potential in the field. Readers will benefit from insights into how AI innovations can streamline their operations and enhance network safety across multiple dimensions. By providing a comprehensive overview of the subject matter, this book guides anticipatory strategies and shape future trends in railway infrastructure management.

    • Network-Constrained Data-Driven Control of High-Speed Rail Systems

      • 1st Edition
      • January 1, 2026
      • Deqing Huang + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 8 9 9 4 5
      • eBook
        9 7 8 0 4 4 3 4 8 9 9 5 2
      Network-Constrained Data-Driven Control of High-Speed Rail Systems addresses the critical challenges in high-speed railway (HSR) operational control systems, focusing on enhancing safety, efficiency, and automation in an era of rapid network expansion. Railway systems face limitations in automatic driving capabilities and decentralized control, relying heavily on manual adjustments and outdated communication infrastructure like GSM-R (limited to 9.6 kbit/s speeds and 400 ms delays). Network-Constrained Data-Driven Control of High-Speed Rail Systems introduces a transformative framework for data-driven adaptive control and multi-train cooperative control under dynamic network constraints. It integrates next-generation 5G-R communication to enable real-time train-to-train (T2T) coordination, reducing dependency on fixed infrastructure and addressing vulnerabilities like faded channels and interference. By combining rigorous theoretical analysis with simulations, the book proposes solutions to improve operational precision, resilience against disruptions, and transportation capacity. This resource is helpful for researchers, engineers, and graduate students in high speed railway control systems, offering innovative strategies to advance autonomous operations and meet the demands of high-density, high-speed rail networks

    • Event-Driven State Estimation for Stochastic Networked Systems

      • 1st Edition
      • December 1, 2025
      • Cong Huang + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 0 1 4 3
      • eBook
        9 7 8 0 4 4 3 4 5 0 1 5 0
      Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stochastic systems within limited communication networks, bringing together existing and emerging concepts. It provides a series of the latest results in, including but not limited to, recursive state estimation, fusion estimation, and state and fault estimation. The book provides practitioner readers with practical tools for the analysis and design of stochastic systems under limited communication networks, capturing recent advances in theory, technological aspects and real-world applications of advanced event-based state estimation methodologies. Realistic research problems are addressed in each chapter, with numerical and simulation results provided to reflect engineering practice, while demonstrating the main focus of the developed estimation approaches. The book is an advanced-level resource presented in an accessible manner, appealing to senior students as a core reference and to researchers and practitioners alike

    • Sliding Mode Control of Fractional-order Systems

      • 1st Edition
      • March 28, 2025
      • Hamid Reza Karimi + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 1 6 9 6 8
      • eBook
        9 7 8 0 4 4 3 3 1 6 9 7 5
      In the fields of dynamical systems and control theory, a fractional-order system is a dynamical system that can be modeled by a fractional differential equation containing derivatives of non-integer order. In control systems, sliding mode control (SMC) is a nonlinear control method that alters the dynamics of a nonlinear system by applying a discontinuous control signal (or more rigorously, a set-valued control signal) that forces the system to "slide" along a cross-section of the system's normal behavior. Sliding Mode Control of Fractional-order Systems discusses the design of several types of fractional-order systems. Sliding mode control strategy allows the exploration of the problems of projection synchronization control, finite-time stability, asymptotic stability, and formation control of fractional-order systems, which make up the shortages in the analysis and design of fractional-order systems. The book focuses on several types of fractional-order control systems, combined with the sliding-mode control (SMC) and event-triggered control, the problems of projection synchronization control, finite-time stability, asymptotic stability, and formation control for those systems are explored, which makes up the shortages in the analysis and design of fractional-order systems.

    • Deep Learning for Image Recognition

      • 1st Edition
      • November 3, 2025
      • Peng Long + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 3 9 5 0 6
      • eBook
        9 7 8 0 4 4 3 4 3 9 5 1 3
      Deep Learning for Image Recognition provides a detailed explanation of the fundamental theories underpinning image recognition and code for recognition tasks in specific application scenarios. Readers can manipulate the existing code, thereby deepening their understanding. Chapters include project work enabling readers to apply the skills and knowledge gained from that section of the book. Projects are based on the accessible Pytorch framework, which is straightforward to learn and can be replicated and modified. Readers are presented with current research findings and up to date techniques in image recognition and deep learning.