This book presents the peer-reviewed proceedings of the Sixth International Conference on Intelligent Computing and Applications (ICICA 2020), held at Government College of Engineering, Keonjhar, Odisha, India, during December 22–24, 2020. The book includes the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks.
The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. This book, which constitutes the eighth volume of the Transactions on Rough Sets series, contains a wide spectrum of contributions to the theory and applications of rough sets. The 17 papers presented explore several research streams and introduce a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science.
The field of SMART technologies is an interdependent discipline. It involves the latest burning issues ranging from machine learning, cloud computing, optimisations, modelling techniques, Internet of Things, data analytics, and Smart Grids among others, that are all new fields. It is an applied and multi-disciplinary subject with a focus on Specific, Measurable, Achievable, Realistic & Timely system operations combined with Machine intelligence & Real-Time computing. It is not possible for any one person to comprehensively cover all aspects relevant to SMART Computing in a limited-extent work. Therefore, these conference proceedings address various issues through the deliberations by distinguished Professors and researchers. The SMARTCOM 2020 proceedings contain tracks dedicated to different areas of smart technologies such as Smart System and Future Internet, Machine Intelligence and Data Science, Real-Time and VLSI Systems, Communication and Automation Systems. The proceedings can be used as an advanced reference for research and for courses in smart technologies taught at graduate level.
Hierarchical Modeling of Energy Systems presents a detailed methodology for hierarchical modeling of large-scale complex systems with a focus on energy systems and their expansion planning and control. General methodological principles of hierarchical modeling are analyzed, and based on this analysis, a generalized technology for the hierarchical approach is presented. The mathematical foundations of decomposition and bi-level programming, as well as the possibility of using information technologies are also considered. The theoretical propositions are demonstrated by numerous hierarchical modeling examples aimed at planning the development of the energy sector and expansion of energy systems, analyzing, and optimizing these systems, and controlling their operation. In addition, codes and sample simulations are included throughout. This is an invaluable guide for researchers, engineers, and other specialists involved in the development, control and management of energy systems, while the summary of fundamental principles and concepts in energy modeling makes this an accessible learning tool for graduate students on any course involving energy systems or energy modeling. - Summarizes hierarchical modeling principles and methods - Critically evaluates all energy systems including electric power systems, heat supply systems, gas, and coal supply systems, integrated and cogeneration systems, its interrelations and more - Examines expansion planning, development and operation, control and management of energy systems - Provides a detailed mathematical descriptions of models, computation algorithms, and optimization problems