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Author: M. Hadi Amini
Publisher: Springer Nature
ISBN: 3030340945
Category : Technology & Engineering
Languages : en
Pages : 306
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Book Description
This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
Author: M. Hadi Amini
Publisher: Springer Nature
ISBN: 3030340945
Category : Technology & Engineering
Languages : en
Pages : 306
Get Book Here
Book Description
This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
Author: Gustav Lindmark
Publisher: Linköping University Electronic Press
ISBN: 9176852431
Category :
Languages : en
Pages : 51
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Book Description
The control-theoretic notion of controllability captures the ability to guide a systems behavior toward a desired state with a suitable choice of inputs. Controllability of complex networks such as traffic networks, gene regulatory networks, power grids etc. brings many opportunities. It could for instance enable improved efficiency in the functioning of a network or lead to that entirely new applicative possibilities emerge. However, when control theory is applied to complex networks like these, several challenges arise. This thesis consider some of these challenges, in particular we investigate how control inputs should be placed in order to render a given network controllable at a minimum cost, taking as cost function either the number of control inputs or the energy that they must exert. We assume that each control input targets only one node (called a driver node) and is either unconstrained or unilateral. A unilateral control input is one that can assume either positive or negative values but not both. Motivated by the many applications where unilateral controls are common, we reformulate classical controllability results for this particular case into a more computationally-efficient form that enables a large scale analysis. We show that the unilateral controllability problem is to a high degree structural and derive theoretical lower bounds on the minimal number of unilateral control inputs from topological properties of the network, similar to the bounds that exists for the minimal number of unconstrained control inputs. Moreover, an algorithm is developed that constructs a near minimal number of control inputs for a given network. When evaluated on various categories of random networks as well as a number of real-world networks, the algorithm often achieves the theoretical lower bounds. A network can be controllable in theory but not in practice when completely unreasonable amounts of control energy are required to steer it in some direction. For unconstrained control inputs we show that the control energy depends on the time constants of the modes of the network, and that the closer the eigenvalues are to the imaginary axis of the complex plane, the less energy is required for control. We also investigate the problem of placing driver nodes such that the control energy requirements are minimized (assuming that theoretical controllability is not an issue). For the special case with networks having all purely imaginary eigenvalues, several constructive algorithms for driver node placement are developed. In order to understand what determines the control energy in the general case with arbitrary eigenvalues, we define two centrality measures for the nodes based on energy flow considerations: the first centrality reflects the network impact of a node and the second the ability to control it indirectly. It turns out that whether a node is suitable as driver node or not largely depends on these two qualities. By combining the centralities into node rankings we obtain driver node placements that significantly reduce the control energy requirements and thereby improve the “practical degree of controllability”.
Author: M. Hadi Amini
Publisher: Springer
ISBN: 3319744127
Category : Technology & Engineering
Languages : en
Pages : 290
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Book Description
This book focuses on the theory and application of interdependent networks. The contributors consider the influential networks including power and energy networks, transportation networks, and social networks. The first part of the book provides the next generation sustainability framework as well as a comprehensive introduction of smart cities with special emphasis on energy, communication, data analytics and transportation. The second part offers solutions to performance and security challenges of developing interdependent networks in terms of networked control systems, scalable computation platforms, and dynamic social networks. The third part examines the role of electric vehicles in the future of sustainable interdependent networks. The fourth and last part of this volume addresses the promises of control and management techniques for the future power grids.
Author: Daehan Won
Publisher:
ISBN:
Category :
Languages : en
Pages : 177
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Book Description
Networks are all around us, and they may be connections of tangible objects in the Euclidean space such as electric power grids, the Internet, highways systems, etc. Among the wide range of areas in the network analysis, finding critical component in the large scale complex networks is one of the most challenging but fascinating problem in the network analysis. Analytical approaches of finding critical components have been widely studied and extensively used to investigate and provide meaningful characterizations of the intrinsic dynamics and properties of complex structures in networked systems. The objective of this thesis is to build novel mathematical models for finding critical components and connectivity patterns in complex networks that may reveal hidden, yet insightful, information for the investigation of underlying dynamics of the networks. In particular: -I propose mixed integer programming (MIP) models to seek k-Cardinality Tree (KCT) ,which address the finding critical components problem. I proposed seven variations of MIP models that are based on connected component constraints and subtour elimination constraints. Through the investigation of polyhedral structures and test results, the best performance model has been chosen and then we compared it with state of the art algorithm in the literature. -I expand our scope to find critical components in the labeled networks. I design two mathematical programming model to determine k-sized critical component including the most informative edges to classify the networks. As a first step, we develop mixed integer programming (MIP) model for finding critical components in the networked data classification. Due to the computationally intractability on the large scaled data, I built a branch-and-cut algorithm based on the Benders decomposition. -I also build a mixed integer nonlinear programming (MINLP) model based on the support vector machine (SVM) formulation. Rather than solving this MINLP directly, an efficient iterative algorithm combining with multiple kernel learning is proposed. To demonstrate the utility of the proposed models and solution approaches, synthetic networks and brain functional connectivity networks are used as case points in this thesis. Through the extensive experiments on both data sets, proposed approaches achieve impressive scalability and comparable or even better performance rather than the state-of-the-art methods. On human brain networks, the approaches are used to detect informative regions of interests (ROIs) and their connectivity patterns that may be useful in detecting people who are risk of developing neurological diseases.
Author: Sean P. Meyn
Publisher:
ISBN: 9787040254815
Category : Computer networks
Languages : en
Pages : 562
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Book Description
Author: Hamid R. Arabnia
Publisher: Springer Nature
ISBN: 3030710513
Category : Technology & Engineering
Languages : en
Pages : 903
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Book Description
The book presents the proceedings of four conferences: The 24th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'20), The 6th International Conference on Health Informatics and Medical Systems (HIMS'20), The 21st International Conference on Bioinformatics & Computational Biology (BIOCOMP'20), and The 6th International Conference on Biomedical Engineering and Sciences (BIOENG'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020, and are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Authors include academics, researchers, professionals, and students. Presents the proceedings of four conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks on Image Processing, Computer Vision, & Pattern Recognition, Health Informatics & Medical Systems, Bioinformatics, Computational Biology & Biomedical Engineering; Features papers from IPCV'20, HIMS'20, BIOCOMP'20, and BIOENG'20.
Author: Pawel Sniatala
Publisher: Springer Nature
ISBN: 3030331326
Category : Technology & Engineering
Languages : en
Pages : 202
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Book Description
This book provides a comprehensive analysis of Brooks-Iyengar Distributed Sensing Algorithm, which brings together the power of Byzantine Agreement and sensor fusion in building a fault-tolerant distributed sensor network. The authors analyze its long-term impacts, advances, and future prospects. The book starts by discussing the Brooks-Iyengar algorithm, which has made significant impact since its initial publication in 1996. The authors show how the technique has been applied in many domains such as software reliability, distributed systems and OS development, etc. The book exemplifies how the algorithm has enhanced new real-time features by adding fault-tolerant capabilities for many applications. The authors posit that the Brooks-Iyengar Algorithm will to continue to be used where fault-tolerant solutions are needed in redundancy system scenarios. This book celebrates S.S. Iyengar's accomplishments that led to his 2019 Institute of Electrical and Electronics Engineers' (IEEE) Cybermatics Congress "Test of Time Award" for his work on creating Brooks-Iyengar Algorithm and its impact in advancing modern computing.
Author: M. Hadi Amini
Publisher: John Wiley & Sons
ISBN: 1119748305
Category : Science
Languages : en
Pages : 324
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Book Description
Learn to deploy novel algorithms to improve and secure smart city infrastructure In Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making, accomplished researchers Drs. M. Hadi Amini and Miadreza Shafie-Khah deliver a crucial exploration of new directions in the science and engineering of deploying novel and efficient computing algorithms to enhance the efficient operation of the networks and communication systems underlying smart city infrastructure. The book covers special issues on the deployment of these algorithms with an eye to helping readers improve the operation of smart cities. The editors present concise and accessible material from a collection of internationally renowned authors in areas as diverse as computer science, electrical engineering, operation research, civil engineering, and the social sciences. They also include discussions of the use of artificial intelligence to secure the operations of cyberphysical smart city infrastructure and provide several examples of the applications of novel theoretical algorithms. Readers will also enjoy: Thorough introductions to fundamental algorithms for computing and learning, large-scale optimizations, control theory for large-scale systems Explorations of machine learning and intelligent decision making in cyberphysical smart cities, including smart energy systems and intelligent transportation networks In-depth treatments of intelligent decision making in cyberphysical smart city infrastructure and optimization in networked smart cities Perfect for senior undergraduate and graduate students of electrical and computer engineering, computer science, civil engineering, telecommunications, information technology, and business, Cyberphysical Smart Cities Infrastructures is an indispensable reference for anyone seeking to solve real-world problems in smart cities.
Author: Pradeep Singh
Publisher: Springer Nature
ISBN: 9819900476
Category : Technology & Engineering
Languages : en
Pages : 885
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Book Description
This book comprises the proceedings of the 4th International Conference on Machine Intelligence and Signal Processing (MISP2022). The contents of this book focus on research advancements in machine intelligence, signal processing, and applications. The book covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. It also includes the progress in signal processing to process the normal and abnormal categories of real-world signals such as signals generated from IoT devices, smart systems, speech, videos and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), electromyogram (EMG), etc. This book proves to be a valuable resource for those in academia and industry.
Author: Ajith Abraham
Publisher: Springer Nature
ISBN: 3030730506
Category : Technology & Engineering
Languages : en
Pages : 817
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Book Description
This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 58 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2020) and 20 papers from the 12th World Congress on Nature and Biologically Inspired Computing (NaBIC 2020), which was held online, from December 14 to 16, 2020. A premier conference in the field of artificial intelligence, HIS - NaBIC 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 25 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of science and engineering.
