Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Handbook of Test Problems in Local and Global Optimization
This collection of challenging and well-designed test problems arising in literature studies also contains a wide spectrum of applications, including pooling/blending operations, heat exchanger network synthesis, homogeneous azeotropic separation, and dynamic optimization and optimal control problems.
R 6,027.17
Stochastic Learning and Optimization
Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework. This new perspective on a popular topic is presented by a well respected expert in the field.
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Distributed Network Systems
Both authors have taught the course of “Distributed Systems” for many years in the respective schools. During the teaching, we feel strongly that “Distributed systems” have evolved from traditional “LAN” based distributed systems towards “Internet based” systems. Although there exist many excellent textbooks on this topic, because of the fast development of distributed systems and network programming/protocols, we have difficulty in finding an appropriate textbook for the course of “distributed systems” with orientation to the requirement of the undergraduate level study for today’s distributed technology. Specifically, from - to-date concepts, algorithms, and models to implementations for both distributed system designs and application programming. Thus the philosophy behind this book is to integrate the concepts, algorithm designs and implementations of distributed systems based on network programming. After using several materials of other textbooks and research books, we found that many texts treat the distributed systems with separation of concepts, algorithm design and network programming and it is very difficult for students to map the concepts of distributed systems to the algorithm design, prototyping and implementations. This book intends to enable readers, especially postgraduates and senior undergraduate level, to study up-to-date concepts, algorithms and network programming skills for building modern distributed systems. It enables students not only to master the concepts of distributed network system but also to readily use the material introduced into implementation practices.
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Handbook of Global Optimization
In 1995 the Handbook of Global Optimization (first volume), edited by R. Horst, and P.M. Pardalos, was published. This second volume of the Handbook of Global Optimization is comprised of chapters dealing with modern approaches to global optimization, including different types of heuristics.Topics covered in the handbook include various metaheuristics, such as simulated annealing, genetic algorithms, neural networks, taboo search, shake-and-bake methods, and deformation methods. In addition, the book contains chapters on new exact stochastic and deterministic approaches to continuous and mixed-integer global optimization, such as stochastic adaptive search, two-phase methods, branch-and-bound methods with new relaxation and branching strategies, algorithms based on local optimization, and dynamical search. Finally, the book contains chapters on experimental analysis of algorithms and software, test problems, and applications.
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Internet and Distributed Computing Systems
This book constitutes the refereed proceedings of the 8th International Conference on Internet and Distributed Computing Systems, IDCS 2015, held in Windsor, UK, in September 2015. The 19 revised full and 6 revised short papers presented were carefully reviewed and selected from 42 submissions. The selected contributions covered cutting-edge aspects of Cloud Computing and Internet of Things, sensor networks, parallel and distributed computing, advanced networking, smart cities and smart buildings, Big Data and social networks.
R 1,559.44
Recent Advances in Evolutionary Multi-objective Optimization
This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
R 4,030.00
Integer Programming and Combinatorial Optimization
This book constitutes the proceedings of the 22nd Conference on Integer Programming and Combinatorial Optimization, IPCO 2021, which took place during May 19-21, 2021. The conference was organized by Georgia Institute of Technology and planned to take place it Atlanta, GA, USA, but changed to an online format due to the COVID-19 pandemic. The 33 papers included in this book were carefully reviewed and selected from 90 submissions. IPCO is under the auspices of the MathematicalOptimization Society, and it is an important forum for presenting the latest results of theory and practice of the various aspects of discrete optimization.
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Intelligent Computing Applications for Sustainable Real-World Systems
This book delves into various solution paradigms such as artificial neural network, support vector machine, wavelet transforms, evolutionary computing, swarm intelligence. During the last decade, novel solution technologies based on human and species intelligence have gained immense popularity due to their flexible and unconventional approach. New analytical tools are also being developed to handle big data processing and smart decision making. The idea behind compiling this work is to familiarize researchers, academicians, industry persons and students with various applications of intelligent techniques for producing sustainable, cost-effective and robust solutions of frequently encountered complex, real-world problems in engineering and science disciplines. The practical problems in smart grids, communication, waste management, elimination of harmful elements from nature, etc., are identified, and smart and optimal solutions are proposed.
R 10,117.09
Proceedings of the International Conferences on Digital Technology Driven Engineering 2024
This book gathers the latest advances, innovations, and applications in the field of engineering optimization and architectural design, presented at the 1st International Conference on ADDitively Manufactured OPTimized Structures by means of Machine Learning (ADDOPTML), held in Amman, Jordan, on October 1–4, 2024, jointly with conferences OPTARCH2024 and OPT-ii2024. It covers topics such as machine learning-based design manufacturing process for civil structures, additive manufacturing optimized structural elements, holistic machine learning aided, linear, nonlinear, stochastic, parametric, discrete and dynamic programming—modelling, hybrid methods with metaheuristics, machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, emergent nature-inspired algorithms such as quantum computing and artificial immune systems. Written by leading researchers and engineers, and selected by means of a rigorous international peer-review process, the contributions highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations.
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Introduction to Global Optimization
In this edition, the scope and character of the monograph did not change with respect to the first edition. Taking into account the rapid development of the field, we have, however, considerably enlarged its contents. Chapter 4 includes two additional sections 4.4 and 4.6 on theory and algorithms of D.C. Programming. Chapter 7, on Decomposition Algorithms in Nonconvex Optimization, is completely new. Besides this, we added several exercises and corrected errors and misprints in the first edition. We are grateful for valuable suggestions and comments that we received from several colleagues. R. Horst, P.M. Pardalos and N.V. Thoai March 2000 Preface to the First Edition Many recent advances in science, economics and engineering rely on nu merical techniques for computing globally optimal solutions to corresponding optimization problems. Global optimization problems are extraordinarily di verse and they include economic modeling, fixed charges, finance, networks and transportation, databases and chip design, image processing, nuclear and mechanical design, chemical engineering design and control, molecular biology, and environment al engineering. Due to the existence of multiple local optima that differ from the global solution all these problems cannot be solved by classical nonlinear programming techniques. During the past three decades, however, many new theoretical, algorith mic, and computational contributions have helped to solve globally multi extreme problems arising from important practical applications.
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Control and Estimation of Distributed Parameter Systems
Consisting of 16 refereed original contributions, this volume presents a diversified collection of recent results in control of distributed parameter systems. Topics addressed include - optimal control in fluid mechanics - numerical methods for optimal control of partial differential equations - modeling and control of shells - level set methods - mesh adaptation for parameter estimation problems - shape optimization Advanced graduate students and researchers will find the book an excellent guide to the forefront of control and estimation of distributed parameter systems.
R 3,034.51
High-Performance Simulation-Based Optimization
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
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Distributed Control and Filtering for Industrial Systems
In recent years technological advancements in the design and fabrication of integrated circuits have led to the development of cost effective, low power, thumb-size devices that can be used for sensing/actuating, communication and computing. This trend is enabling a surge of new applications for which pervasive network architectures are being developed. A key feature of these systems is that they are decentralized and communication among different subsystems may be unreliable. From an engineering viewpoint, to ensure correct operation, the theoretical analysis requires a fundamental paradigm shift, as many of the typical assumptions of systems and control theory cease to hold. Distributed Control and Filtering for Industrial Systems provides an introduction to the control and filtering algorithms devised for distributed environments, with a particular emphasis on industrial applications. Topics covered include control architectures for interconnected systems, recent developments in distributed model predictive control for interconnected networked systems, methods for designing distributed linear quadratic controllers for various classes of systems, designing distributed dynamic output-feedback controllers, characterization of distributed consensus control methods, distributed estimation techniques, distributed Kalman filtering methods, experimental setups and simulation of pilot-scale industrial processes.
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Distributed Autonomous Robotic Systems
This book presents the state of the art in distributed autonomous systems composed of multiple robots, robotic modules, or robotic agents. Swarms in nature can not only adapt to their environments, but can also construct suitable habitats to their own advantage. Distributed autonomous robotic systems can do many things that its individuals cannot do alone. As the global pandemic was still ongoing, the 15th International Symposium on Distributed Autonomous Robotic Systems (DARS2021) was held on June 1–4, 2021, as an online meeting. The scope of DARS201 was to create a bridge between biologists and engineers interested in the distributed intelligence of living things and to establish a new academic field by integrating knowledge from both disciplines. Topics of DARS2021 were swarm intelligence, swarm robotics, multi-agent system, modular robotics, decentralized control, distributed system, etc. The papers in this book provide a very good overview of the state of the art in distributed autonomous robotic systems (DARS). They reflect current research themes in DARS with important contributions. We hope that this book helps to sustain the interest in DARS and triggers new research.
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Recent Advances in Computational Optimization
This book presents recent advances in computational optimization. Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.The book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2020. The book includes important real problems like modeling of physical processes, workforce planning, parameter settings for controlling different processes, transportation problems, wireless sensor networks, machine scheduling, air pollution modeling, solving multiple integrals and systems of differential equations which describe real processes, solving engineering problems. It shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics and other domains can be formulated as optimization problems.
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Bioinspired Heuristics for Optimization
This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.
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Role of 6G Wireless Networks in AI and Blockchain-Based Applications
Artificial intelligence (AI), the internet of things (IoT), and blockchain provide services to 6G in the form of radio resource management, mobility management, energy management, and network management. Moreover, 6G strengthens AI and blockchain-based applications. Further study on the benefits and potential opportunities of 6G for AI and blockchain is required to utilize the technology successfully. Role of 6G Wireless Networks in AI and Blockchain-Based Applications considers the role of the 6G wireless network deployed on AI and blockchain technology-based applications in fields such as the healthcare industry, agriculture, e-business, and transportation. The book specifically focuses on remote healthcare monitoring, online shopping preference, V2V communication, UAV, holographic application, and augmented and virtual reality as advanced services of 6G networks. Covering topics such as machine learning, smart cities, and virtual reality, this reference work is ideal for computer scientists, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.
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Applied Optimization and Swarm Intelligence
This book gravitates on the prominent theories and recent developments of swarm intelligence methods, and their application in both synthetic and real-world optimization problems. The special interest will be placed in those algorithmic variants where biological processes observed in nature have underpinned the core operators underlying their search mechanisms. In other words, the book centers its attention on swarm intelligence and nature-inspired methods for efficient optimization and problem solving. The content of this book unleashes a great opportunity for researchers, lecturers and practitioners interested in swarm intelligence, optimization problems and artificial intelligence.
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Handbook of Moth-Flame Optimization Algorithm
Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters.Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides an in-depth analysis of this algorithm and the existing methods in the literature to cope with such challenges.Key Features:Reviews the literature of the Moth-Flame Optimization algorithmProvides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithmProposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problemsDemonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithmIntroduces several applications areas of the Moth-Flame Optimization algorithmThis handbook will interest researchers in evolutionary computation and meta-heuristics and those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas.
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Distributed, Ambient and Pervasive Interactions
This conference proceedings LNCS 12782 constitutes the refereed proceedings of the 9 th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. The conference was held virtually due to the COVID-19 pandemic.The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of DAPI 2021, Distributed, Ambient and Pervasive Interactions, are organized in topical sections named: Smart Cities; IoT, Sensors and Smart Environments; Learning and Culture in Intelligent Environments; Designing Intelligent Environments.
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Brain Storm Optimization Algorithms
Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A “good enough” optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence.This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.
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Elements of Distributed Algorithms
Distributed Computing is rapidly becoming the principal computing paradigm in diverse areas of computing, communication, and control. Processor clusters, local and wide area networks, and the information highway evolved a new kind of problems which can be solved with distributed algorithms.In this textbook a variety of distributed algorithms are presented independently of particular programming languages or hardware, using the graphically suggestive technique of Petri nets which is both easy to comprehend intuitively and formally rigorous. By means of temporal logic the author provides surprisingly simple yet powerful correctness proofs for the algorithms.The scope of the book ranges from distributed control and synchronization of two sites up to algorithms on any kind of networks. Numerous examples show that description and analysis of distributed algorithms in this framework are intuitive and technically transparent.
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Decision Tree and Ensemble Learning Based on Ant Colony Optimization
This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation.Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process.The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers.This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.
R 4,250.88
Distributed Autonomous Robotic Systems
Distributed robotics is a rapidly growing and maturing interdisciplinary research area lying at the intersection of computer science, network science, control theory, and electrical and mechanical engineering. The goal of the Symposium on Distributed Autonomous Robotic Systems (DARS) is to exchange and stimulate research ideas to realize advanced distributed robotic systems.This volume of proceedings includes 31 original contributions presented at the 2012 International Symposium on Distributed Autonomous Robotic Systems (DARS 2012) held in November 2012 at the Johns Hopkins University in Baltimore, MD USA. The selected papers in this volume are authored by leading researchers from Asia, Europa, and the Americas, thereby providing a broad coverage and perspective of the state-of-the-art technologies, algorithms, system architectures, and applications in distributed robotic systems. The book is organized into five parts, representative of critical long-term and emerging research thrusts in the multi-robot community: Coordination for Perception, Coverage, and Tracking; Task Allocation and Coordination Strategies; Modular Robots and Novel Mechanisms and Sensors; Formation Control and Planning for Robot Teams; and Learning, Adaptation, and Cognition for Robot Teams.
R 6,228.00
Distributed Computer and Communication Networks
This book constitutes the refereed proceedings of the 22nd International Conference on Distributed and Computer and Communication Networks, DCCN 2019, held in Moscow, Russia, in September 2019.The 44 full papers and 2 short papers were carefully reviewed and selected from 174 submissions. The papers cover the following topics: Computer and Communication Networks, Analytical Modeling of Distributed Systems, and Distributed Systems Applications.
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Advances in Applied Mathematics and Global Optimization
The articles that comprise this distinguished annual volume for the Advances in Mechanics and Mathematics series have been written in honor of Gilbert Strang, a world renowned mathematician and exceptional person. Written by leading experts in complementarity, duality, global optimization, and quantum computations, this collection reveals the beauty of these mathematical disciplines and investigates recent developments in global optimization, nonconvex and nonsmooth analysis, nonlinear programming, theoretical and engineering mechanics, large scale computation, quantum algorithms and computation, and information theory.
R 6,041.70
Distributed Generation
Throughout the world there is concern over the impact of energy use on the environment (particularly CO2 emissions) and also over the security of fossil fuel supplies. Consequently, governments and energy planners are actively encouraging alternative and cleaner forms of energy production such as renewables (e.g. wind, solar, biomass) and combined heat and power (CHP). The economics and locations of sustainable energy sources have meant that many of these new generators are connected into distribution networks. It is recognized that the information flow and control of distribution networks is inadequate for these future low-carbon electricity supply systems. The future distribution network will change its operation from passive to active, and the distributed generators will be controlled to support the operation of the power system. In many countries this transformation of electricity supply is managed through energy markets and privately owned, regulated transmission and distribution systems. This book discusses the connection of generation to distribution networks and then moves on to consider how sustainable generation can be fully integrated into the operation of the power system. Both technical and economic aspects are addressed. It is written for later-year undergraduate and postgraduate students studying courses on energy. The book has four tutorial chapters (with examples and questions) to provide fundamental material for those without a strong electrical engineering background.
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Optimization, Learning Algorithms and Applications
This book constitutes the proceedings of the Second International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022, held in Bragança, Portugal, in October 2022.The 53 full papers and 3 short papers were thoroughly reviewed and selected from 145 submissions. They are organized in the topical sections on Machine and Deep Learning; Optimization; Artificial Intelligence; Optimization in Control Systems Design; Measurements with the Internet of Things; Trends in Engineering Education; Advances and Optimization in Cyber-Physical Systems; and Computer vision based on learning algorithms.
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Distributed Computing and Networking
This book constitutes the refereed proceedings of the 14th International Conference on Distributed Computing and Networking, ICDCN 2013, held in Mumbai, India, during January 3-6, 2013. The 27 revised full papers, 5 short papers presented together with 7 poster papers were carefully reviewed and selected from 149 submissions. The papers cover topics such as distributed algorithms and concurrent data structures; integration of heterogeneous wireless and wired networks; distributed operating systems; internetworking protocols and internet applications; distributed database systems; mobile and pervasive computing, context-aware distributed systems; embedded distributed systems; next generation and converged network architectures; experiments and performance evaluation of distributed systems; overlay and peer-to-peer networks and services; fault-tolerance, reliability, and availability; home networking and services; multiprocessor and multi-core architectures and algorithms; resource management and quality of service; self-organization, self-stabilization, and autonomic computing; network security and privacy; high performance computing, grid computing, and cloud computing; energy-efficient networking and smart grids; security, cryptography, and game theory in distributed systems; sensor, PAN and ad-hoc networks; and traffic engineering, pricing, network management.
R 1,586.80
Network Games, Control, and Optimization
This contributed volume offers a collection of papers presented at the 2018 Network Games, Control, and Optimization conference (NETGCOOP), held at the New York University Tandon School of Engineering in New York City, November 14-16, 2018. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other related fields.
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Distributed, Ambient, and Pervasive Interactions
This book constitutes the refereed proceedings of the Second International Conference on Distributed, Ambient, and Pervasive Interactions, DAPI 2014, held as part of the 16th International Conference on Human-Computer Interaction, HCII 2014, held in Heraklion, Crete, Greece in June 2014, jointly with 13 other thematically conferences. The total of 1476 papers and 220 posters presented at the HCII 2014 conferences were carefully reviewed and selected from 4766 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The 58 papers included in this volume are organized in topical sections on design frameworks and models for intelligent interactive environments; natural interaction; cognitive, perceptual and emotional issues in ambient intelligence; user experience in intelligent environments; developing distributed, pervasive and intelligent environments; smart cities.
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Mobile Ad Hoc Networks
In recent years, a lot of work has been done in an effort to incorporate Swarm Intelligence (SI) techniques in building an adaptive routing protocol for Mobile Ad Hoc Networks (MANETs). Since centralized approach for routing in MANETs generally lacks in scalability and fault-tolerance, SI techniques provide a natural solution through a distributed approach for the adaptive routing for MANETs. In SI techniques, the captivating features of insects or mammals are correlated with the real world problems to find solutions. Recently, several applications of bio-inspired and nature-inspired algorithms in telecommunications and computer networks have achieved remarkable success. The main aims/objectives of this book, "Mobile Ad Hoc Networks: Bio-Inspired Quality of Service Aware Routing Protocols", are twofold; firstly it clearly distinguishes between principles of traditional routing protocols and SI based routing protocols, while explaining in detail the analogy between MANETs and SI principles. Secondly, it presents the readers with important Quality of Service (QoS) parameters and explains how SI based routing protocols achieves QoS demands of the applications. This book also gives quantitative and qualitative analysis of some of the SI based routing protocols for MANETs.
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Optimization
Optimization is a foundational topic in mathematics, underpinning nearly all of our modern industrial and technological world. Assuming only basic knowledge of linear algebra and calculus, this book provides a rapid, yet thorough, overview of applied mathematical optimization for advanced undergraduates, beginning graduate students, or practitioners in engineering and science. The text opens with an 'Optimization Bootcamp', introducing methods at a beginning level, before progressing to deep-dives into advanced topics and research-ready methods. The focus throughout is on modern applications of machine learning, inverse problems, and control. Rich pedagogy includes Python code with simple working examples and advanced case studies. Every section is accompanied by YouTube lectures to encourage interaction with the material. Using intuitive explanations, this book makes the material as simple and interesting as possible, while still having the depth, breadth and precision required to empower use in research and real-world applications.
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Network Optimization Methods in Passivity-Based Cooperative Control
This book establishes an important mathematical connection between cooperative control problems and network optimization problems. It shows that many cooperative control problems can in fact be understood, under certain passivity assumptions, using a pair of static network optimization problems. Merging notions from passivity theory and network optimization, it describes a novel network optimization approach that can be applied to the synthesis of controllers for diffusively-coupled networks of passive (or passivity-short) dynamical systems. It also introduces a data-based, model-free approach for the synthesis of network controllers for multi-agent systems with passivity-short agents. Further, the book describes a method for monitoring link faults in multi-agent systems using passivity theory and graph connectivity. It reports on some practical case studies describing the effectivity of the developed approaches in vehicle networks. All in all, this book offers an extensive source of information and novel methods in the emerging field of multi-agent cooperative control, paving the way to future developments of autonomous systems for various application domains
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Distributed Algorithms for Message-Passing Systems
Distributed computing is at the heart of many applications. It arises as soon as one has to solve a problem in terms of entities -- such as processes, peers, processors, nodes, or agents -- that individually have only a partial knowledge of the many input parameters associated with the problem. In particular each entity cooperating towards the common goal cannot have an instantaneous knowledge of the current state of the other entities. Whereas parallel computing is mainly concerned with 'efficiency', and real-time computing is mainly concerned with 'on-time computing', distributed computing is mainly concerned with 'mastering uncertainty' created by issues such as the multiplicity of control flows, asynchronous communication, unstable behaviors, mobility, and dynamicity. While some distributed algorithms consist of a few lines only, their behavior can be difficult to understand and their properties hard to state and prove. The aim of this book is to present in a comprehensive way the basic notions, concepts, and algorithms of distributed computing when the distributed entities cooperate by sending and receiving messages on top of an asynchronous network. The book is composed of seventeen chapters structured into six parts: distributed graph algorithms, in particular what makes them different from sequential or parallel algorithms; logical time and global states, the core of the book; mutual exclusion and resource allocation; high-level communication abstractions; distributed detection of properties; and distributed shared memory. The author establishes clear objectives per chapter and the content is supported throughout with illustrative examples, summaries, exercises, and annotated bibliographies. This book constitutes an introduction to distributed computing and is suitable for advanced undergraduate students or graduate students in computer science and computer engineering, graduate students in mathematics interested in distributed computing, and practitioners and engineers involved in the design and implementation of distributed applications. The reader should have a basic knowledge of algorithms and operating systems.
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Advances in Distributed Agent-Based Retrieval Tools
This volume contains revised and extended versions of papers presented at the 4th edition of the international workshop on Distributed and Agent-based Retrieval Tools (DART'10) held in June 2010, in conjunction with the Symposium on Human Language Technology for the Information Society, in Geneva, Switzerland. Practitioners and researchers working on pervasive and intelligent access to web services and distributed information retrieval met to share their results and insights in intriguing and challenging topics such as: (i) social media and collaboration, (ii) new challenges in search technology, (iii) sentiment analysis and opinion mining, (iv) distributed information retrieval, (v) pervasive intelligence. Every chapter, before discussing in depth the specific topic, presents a comprehensive review of related work and state of the art, in the hope of this volume to be of use in the years to come, to both researchers and students.
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Distributed Computing
This book constitutes the proceedings of the 29th International Symposium on Distributed Computing, DISC 2015, held in Tokyo, Japan, in October 2015. The 42 full papers presented in this volume were carefully reviewed and selected from 143 submissions. The papers feature original contributions to theory, design, implementation, modeling, analysis, or application of distributed systems and networks. A number of 14 two-page brief announcements are included in the back matter of the proceedings.
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Anticipatory Optimization for Dynamic Decision Making
The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process.However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems.This book has serves two major purposes:‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. It fully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making.‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community.
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Computational Intelligence in Telecommunications Networks
Telecommunications has evolved and grown at an explosive rate in recent years and will undoubtedly continue to do so. As its functions, applications, and technology grow, it becomes increasingly complex and difficult, if not impossible, to meet the demands of a global network using conventional computing technologies. Computational intelligence (CI) is the technology of the future-and the future is now. Computational Intelligence in Telecommunications Networks offers an in-depth look at the rapid progress of CI technology and shows its importance in solving the crucial problems of future telecommunications networks. It covers a broad range of topics, from Call Admission Control, congestion control, and QoS-routing for ATM networks, to network design and management, optical, mobile, and active networks, and Intelligent Mobile Agents.Today's telecommunications professionals need a working knowledge of CI to exploit its potential to overcome emerging challenges. The CI community must become acquainted with those challenges to take advantage of the enormous opportunities the telecommunications field offers. This text meets both those needs, clearly, concisely, and with a depth certain to inspire further theoretical and practical advances.
R 11,174.39
Distributed Decision Making and Control
Distributed Decision Making and Control is a mathematical treatment of relevant problems in distributed control, decision and multiagent systems, The research reported was prompted by the recent rapid development in large-scale networked and embedded systems and communications. One of the main reasons for the growing complexity in such systems is the dynamics introduced by computation and communication delays. Reliability, predictability, and efficient utilization of processing power and network resources are central issues and the new theory and design methods presented here are needed to analyze and optimize the complex interactions that arise between controllers, plants and networks. The text also helps to meet requirements arising from industrial practice for a more systematic approach to the design of distributed control structures and corresponding information interfaces Theory for coordination of many different control units is closely related to economics and game theory network uses being dictated by congestion-based pricing of a given pathway. The text extends existing methods which represent pricing mechanisms as Lagrange multipliers to distributed optimization in a dynamic setting. In Distributed Decision Making and Control, the main theme is distributed decision making and control with contributions to a general theory and methodology for control of complex engineering systems in engineering, economics and logistics. This includes scalable methods and tools for modeling, analysis and control synthesis, as well as reliable implementations using networked embedded systems. Academic researchers and graduate students in control science, system theory, and mathematical economics and logistics will find mcu to interest them in this collection, first presented orally by the contributors during a sequence of workshops organized in Spring 2010 by the Lund Center for Control of Complex Engineering Systems, a Linnaeus Center at Lund University, Sweden.>
R 4,030.00
Distributed Fuzzy Control of Multivariable Systems
It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its capabilities in solving high dimensional control problems on the basis of decomposition, hierarchy, decentralization and multilayers. On the other hand, the fuzzy linguistic approach is almost at the same age and has shown its advantages in solving approximately formulated control problems on the basis of linguistic reasoning and logical inference. However, if both aspects of complexity are considered together, the corresponding control problem becomes non-trivial and does not have an easy solution. Modem control theory and practice have reacted accordingly to the above mentioned new cballenges of tbe day by utilizing the latest achievements in computer technology and artificial intelligence distributed computation and intelligent operation. In this respect, a new field has emerged in the last decade, called " Distributed intelligent control systems" . However, the majority of the familiar works in this field are still either on an empirical or on a conceptual level and this is a significant drawback.
R 3,021.89
