This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
A must-have reference on sustainable organic energy storage systems Organic electrode materials have the potential to overcome the intrinsic limitations of transition metal oxides as cathodes in rechargeable batteries. As promising alternatives to metal-based batteries, organic batteries are renewable, low-cost, and would enable a greener rechargeable world. Rechargeable Organic Batteries is an up-to-date reference and guide to the next generation of sustainable organic electrodes. Focused exclusively on organic electrode materials for rechargeable batteries, this unique volume provides comprehensive coverage of the structures, advantages, properties, reaction mechanisms, and performance of various types of organic cathodes. In-depth chapters examine carbonyl-, organosulfur-, radical-, and organometallic complexes, as well as polymer-based active materials for electrochemical energy storage (EES) technologies. Throughout the book, possible application cases and potential challenges are discussed in detail. Presents advanced characterization methods for verifying redox mechanisms of organic materials Examines recent advances in carbonyl-based small-molecule cathode materials in battery systems including lithium-ion, sodium-ion, and aqueous zinc-ion batteries Introduces organosulfide-inorganic composite cathodes with high electrical conductivity and fast reaction kinetics Outlines research progress on radical electrode materials, polymer-based organic cathode materials, and the development of all-organic batteries Summarizes the synthesis processes, redox mechanisms, and electrochemical performance of different kinds of organic anode materials for metal-ion batteries Featuring a general introduction to organic batteries, including a discussion of their necessity and advantages, Rechargeable Organic Batteries is essential reading for electrochemists, materials scientists, organic chemists, physical chemists, and solid-state chemists working in the field.
This book discusses on the Impact Mechanism of Carbon Tariffs and Carbon Labeling on Agri-trade and Emissions Reduction. Specifically, (1) it has analyzed the effect of carbon tariffs on Agri-trade and emissions reduction based on the hypothesis of carbon factor movement and the game theory, and built a Theoretical Model for carbon labeling to lead low-carbon behavior based on the international practices; (2) it simulated the impact of carbon tariffs on world's macro-economy and Agri-trade in China and worldwide using the Global Trade Analysis Project (GTAP) model; (3) it has made the first attempt to see the differences of willingness to pay for low-carbon products, purchasing behavior and expectations for government subsidies between consumers of different regions at different levels in China, by adopting questionnaire survey and scenario experiment; and (4) it has done an empirical analysis of carbon labels’ effect on low carbon consumption behavior based on Structural Equation Modeling (SEM) and experimental observation data with large samples. Finally, it has proposed policy recommendations based on the findings of the above theoretic and empirical studies.
This book highlights the latest advances in AFM nano-manipulation research in the field of nanotechnology. There are numerous uncertainties in the AFM nano-manipulation environment, such as thermal drift, tip broadening effect, tip positioning errors and manipulation instability. This book proposes a method for estimating tip morphology using a blind modeling algorithm, which is the basis of the analysis of the influence of thermal drift on AFM scanning images, and also explains how the scanning image of AFM is reconstructed with better accuracy. Further, the book describes how the tip positioning errors caused by thermal drift and system nonlinearity can be corrected using the proposed landmark observation method, and also explores the tip path planning method in a complex environment. Lastly, it presents an AFM-based nano-manipulation platform to illustrate the effectiveness of the proposed method using theoretical research, such as tip positioning and virtual nano-hand.
Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry outlines important theories and algorithms of DMRG-based approaches and explores their use in computational chemistry. Beginning with an introduction to DMRG and DMRG-based approaches, the book goes on to discuss the key theories and applications of DMRG, from DMRG for semi-empirical and ab-initio quantum chemistry, to DMRG in embedded environments, frequency spaces and quantum dynamics. Drawing on the experience of its expert authors, sections detail recent ideas and key developments, providing an up-to-date view of current developments in the field for students and researchers in quantum chemistry. - Provides an expertly-curated, consolidated overview of research in the field - Includes exercises that support learning and link theory to practice - Outlines key theories and algorithms for computational chemistry applications
This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
This timely book offers a comprehensive study of the mechanism that gives effect to foreign bank resolution actions. In particular, it focuses on how the legal framework for the recognition of foreign bank resolution actions should be structured and proposes detailed legal principles on which effective frameworks should be based.
Mechanism of charge transport in organic solids has been an issue of intensive interests and debates for over 50 years, not only because of the applications in printing electronics, but also because of the great challenges in understanding the electronic processes in complex systems. With the fast developments of both electronic structure theory and the computational technology, the dream of predicting the charge mobility is now gradually becoming a reality. This volume describes recent progresses in Prof. Shuai’s group in developing computational tools to assess the intrinsic carrier mobility for organic and carbon materials at the first-principles level. According to the electron-phonon coupling strength, the charge transport mechanism is classified into three different categories, namely, the localized hopping model, the extended band model, and the polaron model. For each of them, a corresponding theoretical approach is developed and implemented into typical examples.
This book is closely related to the energy conservation problem of rail transport systems, focusing on reducing the energy consumption of train operation. The system process of train operation is analyzed and the relationship between train operation and energy consumption is introduced. The fundamental theories, modelling and application of technologies for energy-efficient train driving are presented, discussing timely topics such as energy-efficient train control and timetabling, integrated timetabling and regenerative braking, and maximizing regenerated energy usage with energy storage systems. In addition, the modelling and application of a traction power simulation platform is introduced, to calculate the detailed energy flow over a railway network. The book is enriched with a set of practical examples to illustrate the performance of the proposed methods in improving energy efficiency of both urban and long-distance trains. Overall, the book provides a timely guide to professionals in the railway industry, and to researchers and graduate students in transport, electrical and control engineering.
The book focuses on the transient modelling, stability analysis and control of power electronic systems, since these systems face severe safe operation problems the during transient period. It discusses both theoretical analysis and practical applications, highlighting the transient characteristics of converters with different control strategies, and proposes transient modelling and model reduction methods. Furthermore, it classifies the transient stability problems of the system to help the readers gain an understanding of the basic theoretical methods for analysing the power electronic system, at the same time providing sufficient detail to enable engineers to design such systems. Comprehensively describing theoretical analyses, ranging from system modelling and stability analysis to transient control, the book is a valuable resource for researchers, engineers and graduate students in fields of transient modelling, stability analysis and control of power electronic systems.
This book applies a novel theory of ‘unbalanced responsiveness’ to the issue of economic inequality in China to better understand the relationship between authoritarian regimes and their citizens. The book highlights how the Chinese Communist Party (CCP) has responded to dissatisfaction over inequality, with both propaganda and policy, revealing how the responsiveness in these two arenas is unbalanced. Arguing that while CCP propaganda claims to reduce inequality, its welfare programs have been stratified, unfair, and regressive, aggravating instead of alleviating inequalities. By utilizing data from multiple national surveys, the book reveals that the discrepancy between propaganda and policy ultimately generates further dissatisfaction and strong demands for redistribution. The findings of this study indicate how unmitigated and prolonged economic inequality could be a real threat to the sustained rule of the CCP regime. Providing a new theory, applicable to authoritarian and especially communist regimes, demonstrated through the lens of China, this book will be a valuable resource to students and scholars of Chinese studies, political science, and public policy.
This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks. The use of ML techniques for security tasks has been steadily increasing in research and also in practice over the last 10 years. Covering efforts to devise more effective defenses, the book explores security solutions that leverage machine learning (ML) techniques that have recently grown in feasibility thanks to significant advances in ML combined with big data collection and analysis capabilities. Since the use of ML entails understanding which techniques can be best used for specific tasks to ensure comprehensive security, the book provides an overview of the current state of the art of ML techniques for security and a detailed taxonomy of security tasks and corresponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. While many recent papers have proposed approaches for specific tasks, such as software security analysis and anomaly detection, these approaches differ in many aspects, such as with respect to the types of features in the model and the dataset used for training the models. In a way that no other available work does, this book provides readers with a comprehensive view of the complex area of ML for security, explains its challenges, and highlights areas for future research. This book is relevant to graduate students in computer science and engineering as well as information systems studies, and will also be useful to researchers and practitioners who work in the area of ML techniques for security tasks.
This book mainly discusses the background of e-commerce, the basic knowledge of e-commerce, the basic models of e-commerce, the basic principles of e-commerce and the cases of e-commerce. This book has formed a theoretical system of e-commerce with a clear integration boundary. The introduction of the systematic theory is guided by the background of e-commerce, centered on the model of e-commerce, paved with the principles of e-commerce and integrated with the cutting-edge cases. This book defines the basic concepts, models and principle of e-commerce in the form of mathematical analysis and analyzes the basic theory of e-commerce from the perspective of mathematical model. This enables readers to form an abstract understanding of the connotation and extension of e-commerce. It establishes a knowledge system with the background of social ecology, engineering ecology and innovative ecology, taking the models of e-commerce as the core, the principles of e-commerce as the process, the architecture of e-commerce as the platform and the operation and management of e-commerce as the means to integrate the knowledge into application. This book uses case study to comprehensively analyze and apply the knowledge system involved in e-commerce, combining theoretical research with engineering research. Through this book, readers can systematically master all kinds of theories involved in e-commerce. This book aims at different professional and diverse reader groups. It can be used as the basic books for students of various e-commerce-related specialties.
This book systematically presents the concept, history, implementation, theory system and basic methods of pulsar and space flight, illustrating the characteristics of pulsars. It also describes the classification of spacecraft navigation systems and the autonomous navigation technologies, as well as X-ray pulsar-based navigation systems (XPNAV) and discusses future navigation satellite systems in detail.
Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots’ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.
In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.
Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.
This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.
This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks. The use of ML techniques for security tasks has been steadily increasing in research and also in practice over the last 10 years. Covering efforts to devise more effective defenses, the book explores security solutions that leverage machine learning (ML) techniques that have recently grown in feasibility thanks to significant advances in ML combined with big data collection and analysis capabilities. Since the use of ML entails understanding which techniques can be best used for specific tasks to ensure comprehensive security, the book provides an overview of the current state of the art of ML techniques for security and a detailed taxonomy of security tasks and corresponding ML techniques that can be used for each task. It also covers challenges for the use of ML for security tasks and outlines research directions. While many recent papers have proposed approaches for specific tasks, such as software security analysis and anomaly detection, these approaches differ in many aspects, such as with respect to the types of features in the model and the dataset used for training the models. In a way that no other available work does, this book provides readers with a comprehensive view of the complex area of ML for security, explains its challenges, and highlights areas for future research. This book is relevant to graduate students in computer science and engineering as well as information systems studies, and will also be useful to researchers and practitioners who work in the area of ML techniques for security tasks.
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
In this book, we present our systematic investigations into consensus in multi-agent systems. We show the design and analysis of various types of consensus protocols from a multi-agent perspective with a focus on min-consensus and its variants. We also discuss second-order and high-order min-consensus. A very interesting topic regarding the link between consensus and path planning is also included. We show that a biased min-consensus protocol can lead to the path planning phenomenon, which means that the complexity of shortest path planning can emerge from a perturbed version of min-consensus protocol, which as a case study may encourage researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. We also illustrate the design and analysis of consensus protocols for nonlinear multi-agent systems derived from an optimal control formulation, which do not require solving a Hamilton-Jacobi-Bellman (HJB) equation. The book was written in a self-contained format. For each consensus protocol, the performance is verified through simulative examples and analyzed via mathematical derivations, using tools like graph theory and modern control theory. The book’s goal is to provide not only theoretical contributions but also explore underlying intuitions from a methodological perspective.
In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.
Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.
This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.
Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.
This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots’ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.
This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.
This book comprehensively covers the topics of origin and distribution, evolution and types, regional and global importance, biodiversity conservation, plant-soil interfaces, ecosystem functions and services, social-ecological systems, climate change adaptations, land degradation and restoration, grazing management and pastoral production, and sustainable future of the grasslands on the Qinghai-Tibetan Plateau (QTP), which is a globally unique eco-region called the "Roof of the World" because of its high elevation, “Third Pole on Earth" because of its alpine environment and the "Water Tower in Asia" because of its headwater location. The grassland ecosystem covers above 60% of QTP, which is about 2.5 million km2, 1/4 of Chinese total territorial lands. The grassland ecosystem of the QTP (the Third Pole) is an important part of the palaearctic region, which features alpine cover and low oxygen. The Third Pole's grasslands not only provide important ecosystem functions such as biodiversity conservation, carbon storage, water resource regulation, climate control, and natural disaster mitigation at a global scale, but also provide critical ecosystem services such as pastoral production, cultural inheritance, and tourism and recreation at local and regional scales. The purposes of this monograph are to address the following questions: (1) What are the special features of the Third Pole's grasslands? (2) How have climate changes and human activities changed the structures and functions of the Third Pole's grasslands? (3) How can we cope with land degradation and climate change through innovative restoration and protective actions for Third Pole's grasslands? And (4) How can we promote the sustainable development of social-ecological systems of the Third Pole's grasslands through best management practices including grazing? The goal of this book is to attract the attention of international audiences to realize the importance of the Third Pole’s grasslands, and to call for the actions of global communities to effectively protect and sustainably use the Third Pole's grasslands. This book can be served as textbooks, teaching materials and documentaries for different audiences. The target audiences include students, teachers, researchers, policy makers, planners, government officials, and NGOs in agricultural, environmental and natural resources sectors.
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors' papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
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