Modern Machine Learning Techniques And Their Applications In Cartoon Animation Research

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Various systems science and engineering disciplines are covered and challenging new research issues in these disciplines are revealed. They will be extremely valuable for the readers to search for some new research directions and problems. Chapters are contributed by world-renowned systems engineers Chapters include discussions and conclusions Readers can grasp each event holistically without having professional expertise in the field

An in-depth review of hybrid control techniques for smart prosthetic hand technology by two of the world’s pioneering experts in the field Long considered the stuff of science fiction, a prosthetic hand capable of fully replicating all of that appendage’s various functions is closer to becoming reality than ever before. This book provides a comprehensive report on exciting recent developments in hybrid control techniques—one of the most crucial hurdles to be overcome in creating smart prosthetic hands. Coauthored by two of the world’s foremost pioneering experts in the field, Fusion of Hard and Soft Control Strategies for Robotic Hand treats robotic hands for multiple applications. Itbegins withan overview of advances in main control techniques that have been made over the past decade before addressing the military context for affordable robotic hand technology with tactile and/or proprioceptive feedback for hand amputees. Kinematics, homogeneous transformations, inverse and differential kinematics, trajectory planning, and dynamic models of two-link thumb and three-link index finger are discussed in detail. The remainder of the book is devoted to the most promising soft computing techniques, particle swarm optimization techniques, and strategies combining hard and soft controls. In addition, the book: Includes a report on exciting new developments in prosthetic/robotic hand technology, with an emphasis on the fusion of hard and soft control strategies Covers both prosthetic and non-prosthetic hand designs for everything from routine human operations, robotic surgery, and repair and maintenance, to hazardous materials handling, space applications, explosives disposal, and more Provides a comprehensive overview of five-fingered robotic hand technology kinematics, dynamics, and control Features detailed coverage of important recent developments in neuroprosthetics Fusion of Hard and Soft Control Strategies for Robotic Hand is a must-read for researchers in control engineering, robotic engineering, biomedical sciences and engineering, and rehabilitation engineering.

A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.

E-CARGO and Role-Based Collaboration A model for collaboratively solving complex problems E-CARGO and Role-Based Collaboration offers a unique guide that explains the nature of collaboration, explores an easy-to-follow process of collaboration, and defines a model to solve complex problems in collaboration and complex systems. Written by a noted expert on the topic, the book initiates the study of an effective collaborative system from a novel perspective. The role-based collaboration (RBC) methodology investigates the most important aspects of a variety of collaborative systems including societal-technical systems. The models and algorithms can also be applied across system engineering, production, and management. The RBC methodology provides insights into complex systems through the use of its core model E-CARGO. The E-CARGO model provides the fundamental components, principles, relationships, and structures for specifying the state, process, and evolution of complex systems. This important book: Contains a set of concepts, models, and algorithms for the analysis, design, implementation, maintenance, and assessment of a complex system Presents computational methods that use roles as a primary underlying mechanism to facilitate collaborative activities including role assignment Explores the RBC methodology that concentrates on the aspects that can be handled by individuals to establish a well-formed team Offers an authoritative book written by a noted expert on the topic Written for researchers and practitioners dealing with complex problems in collaboration systems and technologies, E-CARGO and Role-Based Collaboration contains a model to solve real world problems with the help of computer-based systems.