Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Non-Smooth and Complementarity-Based Distributed Parameter Systems
Many of the most challenging problems in the applied sciences involve non-differentiable structures as well as partial differential operators, thus leading to non-smooth distributed parameter systems. This edited volume aims to establish a theoretical and numerical foundation and develop new algorithmic paradigms for the treatment of non-smooth phenomena and associated parameter influences. Other goals include the realization and further advancement of these concepts in the context of robust and hierarchical optimization, partial differential games, and nonlinear partial differential complementarity problems, as well as their validation in the context of complex applications. Areas for which applications are considered include optimal control of multiphase fluids and of superconductors, image processing, thermoforming, and the formation of rivers and networks.Chapters are written by leading researchers and present results obtained in the first funding phase of the DFG Special Priority Program on Nonsmooth and Complementarity Based Distributed Parameter Systems: Simulation and Hierarchical Optimization that ran from 2016 to 2019.
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Proceedings of the 22nd Engineering Applications of Neural Networks Conference
This book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.
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Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 held in East Lansing, MI, USA, in March 2019.The 59 revised full papers were carefully reviewed and selected from 76 submissions. The papers are divided into 8 categories, each representing a key area of current interest in the EMO field today. They include theoretical developments, algorithmic developments, issues in many-objective optimization, performance metrics, knowledge extraction and surrogate-based EMO, multi-objective combinatorial problem solving, MCDM and interactive EMO methods, and applications.
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Information Fusion in Distributed Sensor Networks with Byzantines
This book reviews the most powerful attack strategies and potential defense mechanisms, always approaching the interplay between the Fusion Center and the Byzantines from a game-theoretic perspective. For each of the settings considered, the equilibria of the game and the corresponding payoffs are derived, shedding new light on the achievable performance level and the impact that the presence of the Byzantines has on the accuracy of decisions made by the Fusion Center. Accordingly, the book offers a simple yet effective introduction to the emerging field of adversarial information fusion, providing a wealth of intuitive take-home lessons for practitioners interested in applying the most basic notions to the design of practical systems, while at the same time introducing researchers and other readers to the mathematical details behind the theory.
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Metaheuristics and Optimization in Computer and Electrical Engineering
The use of artificial intelligence, especially in the field of optimization is increasing day by day. The purpose of this book is to explore the possibility of using different kinds of optimization algorithms to advance and enhance the tools used for computer and electrical engineering purposes.
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Separable Optimization
In this book, the theory, methods and applications of separable optimization are considered. Some general results are presented, techniques of approximating the separable problem by linear programming problem, and dynamic programming are also studied. Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and convergent iterative algorithms of polynomial complexity are proposed. As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. The problems of numerical approximation of tabulated functions and numerical solution of overdetermined systems of linear algebraic equations and some systems of nonlinear equations are solved by separable convex unconstrained minimization problems. Some properties of the Knapsack polytope are also studied. This second edition includes a substantial amount of new and revised content. Three new chapters, 15-17, are included. Chapters 15-16 are devoted to the further analysis of the Knapsack problem. Chapter 17 is focused on the analysis of a nonlinear transportation problem. Three new Appendices (E-G) are also added to this edition and present technical details that help round out the coverage. Optimization problems and methods for solving the problems considered are interesting not only from the viewpoint of optimization theory, optimization methods and their applications, but also from the viewpoint of other fields of science, especially the artificial intelligence and machine learning fields within computer science. This book is intended for the researcher, practitioner, or engineer who is interested in the detailed treatment of separable programming and wants to take advantage of the latest theoretical and algorithmic results. It may also be used as a textbook for a special topics course or as a supplementary textbook for graduate courses on nonlinear and convex optimization.
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Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering
This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion – state-of-the-art approaches for the application of computational intelligence to engineering problems.The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from four countries – some of which are the original proponents of the methods presented – and 18 research centers around the globe.
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Distributed, Ambient and Pervasive Interactions
This book constitutes the refereed proceedings of the 5th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, held in Vancouver, BC, Canada, in July 2017. The total of 1228 papers presented at the 15 colocated HCII 2017 conferenceswas carefully reviewed and selected from 4340 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. This volume contains papers addressing the following major topics: designing and evaluating distributed, ambient and pervasive interactions; natural interaction; smart cities; art and cultural heritage in smart environments; smart environments for quality of life; smart environments for learning and creativity; and ambient games and humour.
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Advances in Neural Networks
This carefullyedited book is putting emphasis on computational and artificial intelligentmethods for learning and their relative applications in robotics, embeddedsystems, and ICT interfaces for psychological and neurological diseases. Thebook is a follow-up of the scientific workshop on Neural Networks (WIRN 2015)held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. Theworkshop, at its 27th edition became a traditional scientific event thatbrought together scientists from many countries, and several scientificdisciplines. Each chapter is an extended version of the original contributionpresented at the workshop, and together with the reviewers’ peer revisions italso benefits from the live discussion during the presentation.The content of book is organized in the following sections. 1. Introduction,2. Machine Learning,3. Artificial Neural Networks: Algorithms and models,4. Intelligent Cyberphysical and Embedded System,5. Computational Intelligence Methods for Biomedical ICT in NeurologicalDiseases,6. Neural Networks-Based Approaches to Industrial Processes,7. Reconfigurable Modular Adaptive Smart Robotic Systems forOptoelectronicsIndustry: The White'R InstantiationThis book is unique in proposing a holistic and multidisciplinaryapproach to implement autonomous, and complex Human Computer Interfaces.
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Distributed and Parallel Computing
There are many applications that require parallel and distributed processing to allow complicated engineering, business and research problems to be solved in a reasonable time. Parallel and distributed processing is able to improve company profit, lower costs of design, production, and deployment of new technologies, and create better business environments. The major lesson learned by car and aircraft engineers, drug manufacturers, genome researchers and other specialist is that a computer system is a very powerful tool that is able to help them solving even more complicated problems. That has led computing specialists to new computer system architecture and exploiting parallel computers, clusters of clusters, and distributed systems in the form of grids. There are also institutions that do not have so complicated problems but would like to improve profit, lower costs of design and production by using parallel and distributed processing on clusters. In general to achieve these goals, parallel and distributed processing must become the computing mainstream. This implies a need for new architectures of parallel and distributed systems, new system management facilities, and new application algorithms. This also implies a need for better understanding of grids and clusters, and in particular their operating systems, scheduling algorithms, load balancing, heterogeneity, transparency, application deployment, which is of the most critical importance for their development and taking them by industry and business.
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Computer Networks and Distributed Systems
This book constitutes the refereed proceedings of the International Symposium on Computer Networks and Distributed Systems, CNDS 2013, held in Tehran, Iran, in December 2013. The 14 full papers presented were carefully reviewed and selected from numerous submissions. They are organized in topical sections such as cognitive and multimedia networks; wireless sensor networks; security; clouds and grids.
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Distributed Computing and Internet Technology
Kansas City) and Ashok Srivastava (NASA Ames Research Center, USA). The tutorials were on security issues in distributed systems and enterprise resource planning. Wetake this opportunity to thank various peopleand organizations who c- tributed to make this conference a grand success. Our heartfelt thanks to the track chairs whose full cooperation and technical assessments were crucial to achieve this high level of quality even in the ?rst edition of the conference. The full participation of the program committee members and the reviewers made it possible to complete the peer-review process in time. We are extremely thankful to them. We would like to thank our advisors and general chair for having c- ?dence in us. Our special thanks go to A. Samanta and P. K. Mishra of KIIT for their support and encouragement in organizing this conference. We gratefully acknowledge the ?nancial and logistic support extended by KIIT in hosting this conference. TheAllIndiaCouncilofTechnicalEducation(AICTE),Government ofIndia,NewDelhiprovided agenerousgrant-in-aid fororganizingICDCIT. We thankfully acknowledge AICTE's support.We are thankful to Springer for its cooperation in publishing the proceedings in such a short time. We are also thankful to our parent departments, Department of Computer Science and - gineering, IIT Kanpur and Department of Computer and Information Sciences, University of Hyderabad, for providing infrastructural support to carry out the editorial work. We thank Krithi Ramamritham and Pradeep Khosla for agr- ing to deliver the lectures in the plenary sessions.
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Distributed Graph Analytics
This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects.The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research.This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.
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Future Wireless Networks and Information Systems
This volume contains revised and extended research articles written by prominent researchers participating in the ICF4C 2011 conference. 2011 International Conference on Future Communication, Computing, Control and Management (ICF4C 2011) has been held on December 16-17, 2011, Phuket, Thailand. Topics covered include intelligent computing, network management, wireless networks, telecommunication, power engineering, control engineering, Signal and Image Processing, Machine Learning, Control Systems and Applications, The book will offer the states of arts of tremendous advances in Computing, Communication, Control, and Management and also serve as an excellent reference work for researchers and graduate students working on Computing, Communication, Control, and Management Research.
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Advances in Neural Networks – ISNN 2020
This volume LNCS 12557 constitutes the refereed proceedings of the 17th International Symposium on Neural Networks, ISNN 2020, held in Cairo, Egypt, in December 2020.The 24 papers presented in the two volumes were carefully reviewed and selected from 39 submissions. The papers were organized in topical sections named: optimization algorithms; neurodynamics, complex systems, and chaos; supervised/unsupervised/reinforcement learning/deep learning; models, methods and algorithms; and signal, image and video processing.
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Evolutionary Algorithms for Mobile Ad Hoc Networks
Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: Instructs on how to identify, model, and optimize solutions to problems that arise in daily researchPresents complete and up-to-date surveys on topics like network and mobility simulatorsProvides sample problems along with solutions/descriptions used to solve each, with performance comparisonsCovers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.
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Distributed Computer and Communication Networks: Control, Computation, Communications
This book constitutes the refereed proceedings of the 23rd International Conference on Distributed and Computer and Communication Networks, DCCN 2020, held in Moscow, Russia, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 43 papers were carefully reviewed and selected from 167 submissions.The papers are organized in the following topical sections: computer and communication networks and technologies; analytical modeling of distributed systems, and distributed systems applications.
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Neural Networks for Optimization and Signal Processing
A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
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Grid and Distributed Computing
As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole. Of particular interest are hybrid solutions that c- bine ideas taken from multiple disciplines in order to achieve something more signi- cant than the sum of the individual parts. Through such hybrid philosophy, a new principle can be discovered, which has the propensity to propagate throughout mul- faceted disciplines. FGIT 2009 was the first mega-conference that attempted to follow the above idea of hybridization in FGIT in a form of multiple events related to particular disciplines of IT, conducted by separate scientific committees, but coordinated in order to expose the most important contributions. It included the following international conferences: Advanced Software Engineering and Its Applications (ASEA), Bio-Science and Bio-Technology (BSBT), Control and Automation (CA), Database Theory and Application (DTA), D- aster Recovery and Business Continuity (DRBC; published independently), Future G- eration Communication and Networking (FGCN) that was combined with Advanced Communication and Networking (ACN), Grid and Distributed Computing (GDC), M- timedia, Computer Graphics and Broadcasting (MulGraB), Security Technology (SecTech), Signal Processing, Image Processing and Pattern Recognition (SIP), and- and e-Service, Science and Technology (UNESST).
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Parallel and Distributed Computing and Networks
This book constitutes the refereed proceedings of the International conference on Parallel and Distributed Computing and Networks, PDCN 2011, held in Chongqing, China, in December 2010. The 19 revised full papers presented were carefully reviewed and selected from numerous submissions. The conference provided a forum for participants from industry, academic, and non-profit organizations to exchange innovative ideas on Parallel and Distributed Computing and Networks related technologies. The papers address current issues in distributed, parallel, ubiquitous, and cloud computing with special focus on systems security, healthcare, and sports economics.
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Intelligent Sensor Networks
Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperationMaintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.
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AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
This work addresses the research and development of an innovative optimization kernel applied to analog integrated circuit (IC) design. Particularly, this works describes the modifications inside the AIDA Framework, an electronic design automation framework fully developed by at the Integrated Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by enhancing AIDA-C, which is the circuit optimizer component of AIDA, with a new multi-objective multi-constraint optimization module that constructs a base for multiple algorithm implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA. Moreover, the implemented structure allows the easy hybridization between kernels transforming the previous simple NSGAII optimization module into a more evolved and versatile module supporting multiple single and multi-kernel algorithms. The three multi-objective optimization approaches were validated with CEC2009 benchmarks to constrained multi-objective optimization and tested with real analog IC design problems. The achieved results were compared in terms of performance, using statistical results obtained from multiple independent runs. Finally, some hybrid approaches were also experimented, giving a foretaste to a wide range of opportunities to explore in future work.
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Sequential Approximate Multiobjective Optimization Using Computational Intelligence
Many kinds of practical problems such as engineering design, industrial m- agement and ?nancial investment have multiple objectives con?icting with eachother. Thoseproblemscanbeformulatedasmultiobjectiveoptimization. In multiobjective optimization, there does not necessarily a unique solution which minimizes (or maximizes) all objective functions. We usually face to the situation in which if we want to improve some of objectives, we have to give up other objectives. Finally, we pay much attention on how much to improve some of objectives and instead how much to give up others. This is called “trade-o?. ” Note that making trade-o? is a problem of value ju- ment of decision makers. One of main themes of multiobjective optimization is how to incorporate value judgment of decision makers into decision s- port systems. There are two major issues in value judgment (1) multiplicity of value judgment and (2) dynamics of value judgment. The multiplicity of value judgment is treated as trade-o? analysis in multiobjective optimi- tion. On the other hand, dynamics of value judgment is di?cult to treat. However, it is natural that decision makers change their value judgment even in decision making process, because they obtain new information during the process. Therefore, decision support systems are to be robust against the change of value judgment of decision makers. To this aim, interactive p- grammingmethodswhichsearchasolutionwhileelicitingpartialinformation on value judgment of decision makers have been developed. Those methods are required to perform ?exibly for decision makers’ attitude.
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Learning and Intelligent Optimization
This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8–12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.
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Intelligent Estimation
Intelligent Estimation: A Soft Computing Paradigm presents a unified treatment of a new soft computing framework using AI-based approaches for system identification, parameter estimation, and filtering. This Neuro-Fuzzy-GA–based methodology, succinctly referred to as Intelligent Estimation (IE), integrates these estimation and filtering tasks within a single paradigm.Offering a thorough understanding of soft computing-based estimation concepts and theory, the book discusses a modeling-control-system approach with numerous practical applications in solving mathematical modeling problems for industrial and aerospace engineering systems. It delves into theory, concepts and various ramifications of neural networks, fuzzy logic, and genetic algorithms for modeling, system identification, filtering, and estimation.This book is intended for upper-level undergraduate and graduate engineering students studying soft computing, intelligent systems, and advanced control systems in industry applications.Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.
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Handbook of Optimization in Complex Networks
Complex Social Networks is a newly emerging (hot) topic with applications in a variety of domains, such as communication networks, engineering networks, social networks, and biological networks. In the last decade, there has been an explosive growth of research on complex real-world networks, a theme that is becoming pervasive in many disciplines, ranging from mathematics and computer science to the social and biological sciences. Optimization of complex communication networks requires a deep understanding of the interplay between the dynamics of the physical network and the information dynamics within the network.Although there are a few books addressing social networks or complex networks, none of them has specially focused on the optimization perspective of studying these networks. This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology.
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Distributed Graph Algorithms for Computer Networks
This book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of each algorithm, with extensive use of supporting examples, and discusses their concrete network applications; examines key graph-theoretical algorithm concepts, such as dominating sets, and parameters for mobility and energy levels of nodes in wireless ad hoc networks, and provides a contemporary survey of each topic; presents a simple simulator, developed to run distributed algorithms; provides practical exercises at the end of each chapter.
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Modeling, Control and Optimization of Complex Systems
Modeling, Control And Optimization Of Complex Systems is a collection of contributions from leading international researchers in the fields of dynamic systems, control theory, and modeling. These papers were presented at the Symposium on Modeling and Optimization of Complex Systems in honor of Larry Yu-Chi Ho in June 2001. They include exciting research topics such as: -modeling of complex systems, -power control in ad hoc wireless networks, -adaptive control using multiple models, -constrained control, -linear quadratic control, -discrete events, -Markov decision processes and reinforcement learning, -optimal control for discrete event and hybrid systems, -optimal representation and visualization of multivariate data and functions in low-dimensional spaces.
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Optimization, Learning Algorithms and Applications
This two-volume set, CCIS 2280 and CCIS 2281, constitutes the proceedings of the 4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024, held in Tenerife, Spain, in July 2024.The 41 papers presented here were carefully reviewed and selected from 105 submissions. They have been organized in the two volumes under the following topical sections:-Part I: Learning Algorithms in Engineering Education; Machine Learning; Deep Learning; Optimization in the SDG context. Part II: Optimization in Control Systems Design; Optimization.
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Distributed Applications Engineering
Client/server and distributed technologies have made great strides since their emergence in the late 1980s to become very popular in the IT industry today. This book illustrates techniques not only for designing GUI client/server applications, but also for managing complex application environments containing both legacy and new applications. Topics covered in this book include - The what, when and how of the three tier client/server model - Coupling and dependency: key design factors in distributed systems - Distributed application design alternatives for the enterprise - The Federated application structure for integrating the applications of the enterprise - A real-life case study of a major financial institution- Systems Architects and senior technical staffProject Managers and Software Engineers involved with or interested in client/server computing, and final year undergraduate and postgraduate students will find this book useful.
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Aggregation in Large-Scale Optimization
When analyzing systems with a large number of parameters, the dimen sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The aggregated variables are either readily defined and processed, or the aggregated system may be considered as an approximate model for the orig inal system. In the latter case, the operation of the original system can be exhaustively analyzed within the framework of the aggregated model, and one faces the problems of defining the rules for introducing macrovariables, specifying loss of information and accuracy, recovering original variables from aggregates, etc. We consider also in detail the so-called iterative aggregation approach. It constructs an iterative process, at· every step of which a macroproblem is solved that is simpler than the original problem because of its lower dimension. Aggregation weights are then updated, and the procedure passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization.
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Optimization in the Agri-Food Supply Chain
This book discusses the optimization of supply chains in the agri-food and animal industries, and focuses on the integration of technology and sustainability practices. It explores the use of emerging technologies like IoT, Blockchain and AI in supply chain management, and also addresses the need for resilient supply chains and strategies for risk management. Optimization in the Agri-Food Supply Chain provides an overview of various studies conducted in the field, including topics such as the impact of climate change, sustainable initiatives, inventory management activities and the dynamics of specific supply chain systems. It also discusses the use of underutilized crops, optimization techniques, forecasting methods, circular production and the role of open innovation in the food supply chain. Overall, the book aims to contribute to the knowledge on supply chain optimization and also provide insights and recommendations for enhancing efficiency and sustainability in the agri-food and animal industries.
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Distributed Generation
Distributed power generation is a technology that could help to enable efficient, renewable energy production both in the developed and developing world. It includes all use of small electric power generators, whether located on the utility system, at the site of a utility customer, or at an isolated site not connected to the power grid. Induction generator (IG) is the most commonly used and cheapest technology, compatible with renewable energy resources. Permanent magnet (PM) generators have traditionally been avoided due to high fabrication costs; however, compared with IGs they are more reliable and productive. Distributed Generation thoroughly examines the principles, possibilities and limitations of creating energy with both IGs and PM generators. It takes an electrical engineering approach in the analysis and testing of these generators, and includes diagrams and extensive case study examples to better demonstrate how the integration of energy sources can be accomplished. The book also provides the practical tools needed to model and implement new techniques for generating energy through isolated or grid-connected systems. Besides a chapter introducing the technical, economic and environmental impacts of distributed generation, this book includes: an examination of various phase-balancing schemes for a three-phase IG operating on a single-phase power system;a coupled circuit 2-D finite element analysis of a grid-connected IG, with Steinmetz connection;a study of self-excited induction generator (SEIG) schemes for autonomous power systems, and the voltage and frequency control of SEIG with a slip-ring machine (SESRIG);a report on a PM synchronous generator with inset rotor for achieving a reduced voltage regulation when supplying an autonomous power system, and an analysis of its performance using a two-axis model and finite element method;experimental work on various IG and SEIG schemes. This book is a must-read for engineers, consultants, regulators, and environmentalists involved in energy production and delivery, helping them to evaluate renewable energy sources and to integrate these into an efficient energy delivery system. It is also a superior reference for undergraduates and postgraduates. Designers, operators, and planners will appreciate its unique contribution to the literature in this field.
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Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy
The proliferation of renewable energy enhances the sustainability of power systems, but the inherent variability also poses great challenges to the planning and operation of large power grids. The corresponding electric power deficiencies can be compensated by fast ramping generators and energy storage devices. However, frequent ramp up/down power adjustments can increase the operation and the maintenance cost of generators. Moreover, storage devices are regarded as costly alternatives. Demand response (DR) and transactive energy can address this problem owing to its attractive and versatile capability for balancing the supply-demand, improving energy efficiency, and enhancing system resilience. Distributed resources are the typical participants of DR and transactive energy programs, which greatly contribute to keep the supply and demand in a balance. Thermostatically controlled loads (TCLs) (i.e., air conditioners, water heaters, and refrigerators) represent an example of distributed resources, the ratio of which to the total power consumption in developed countries is up to 30%–40%. Providing tremendous potentials in adjustable power consumption, TCLs have attracted major interests in DR and transactive energy opportunities. It has highlighted the advantages of TCLs in responding to uncertainties in power systems. This book provides an insight of TCLs as typical distributed resources in smart grids for demand response and transactive energy to address the imbalance between supply and demand problems in power systems. The key points on analysis of uncertainty parameters, aggregated control models, battery modelling, multi-time scale control, transactive control and robust restoration of TCLs are all included. These are the research points of smart grids and deserve much attention. We believe this book will offer the related researcher a better understanding on the integration of distributed resources into smart grid for demand response and transactive energy. And it will be helpful to address the problems in practical projects.
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Distributed Computing and Internet Technology
This book constitutes the proceedings of the 13th International Conference on Distributed Computing and Internet Technology, ICDCIT 2017, held in Bhubaneswar, India, in January 2017. The 17 full papers and 3 poster papers presented together with 6 abstracts of invited talks were carefully reviewed and selected from 208 submissions (118 abstract and 90 full paper submissions). The ICDCIT conference focusses on mobile computing; analytics; distributed computing; virtual machines; access control; and security and privacy.
R 2,167.13
Nature-Inspired Algorithms and Applied Optimization
This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.
R 6,197.74
Distributed Computing in Sensor Systems
The book constitutes the refereed proceedings of the 4th International Conference on Distributed Computing in Sensor Systems, DCOSS 2008, held on Santorini Island, Greece, in June 2008.The 29 revised full papers and 12 revised short papers presented were carefully reviewed and selected from 116 submissions. The papers propose a multitude of novel algorithmic design and analysis techniques, systematic approaches and application development methodologies for distributed sensor networking. The papers cover aspects including energy management, communication, coverage and tracking, time synchronization and scheduling, key establishment and authentication, compression, medium access control, code update, and mobility.
R 1,600.73
Wireless Next Generation Networks
This SpringerBrief proposes a trust model motivated by virtue epistemology, addressing the need for a more efficient and flexible trust model for wireless next generation networks. This theory of trust simplifies the computation and communication overhead of strictly cognitive-computational models of trust. Both the advantages and the challenges of virtue-based trust models are discussed. This brief offers new research and a general theory of rationality that enables users to interpret trust and reason as complementary mechanisms that guide our rational conduct at two different epistemic levels. The presented model of human social interaction is designed for the highly dynamic and unstructured environment of the mobile Internet and wireless next generation networks. Wireless Next Generation Networks: A Virtue-Based Trust Model targets network engineers, cognitive scientists, AI researchers, philosophers, and social scientists. Advanced-level students studying computer science, electrical engineering and social science will also find the interdisciplinary perspective useful.
R 1,530.35
