Multi-Objective Optimization Using Local Fractional Differential Operator

In this effort, we aim to generalize the concept of Univex functions by utilizing a local fractional differential-difference operator, based on different types of local fractional calculus (fractal calculus). This study leads to a new class of these functions in some optimal problems by illustrating conditions on the generalized functions. We call it the class of local fractional Univex functions. Strong, weak, converse, and strict converse duality theorems are given. Multi-objective optimal problem involves the new process is solved (local optimal problem). The main tool employed in the analysis is based on the local fractional derivative operators

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

A broker based consumption mechanism for social clouds

The new consumption without ownership paradigm is leading towards a “rental economy” where people can now rent and use various services from third-parties within a market of “shared” resources. The elimination of ownership has increased the marginal utility of consumption and reduced the risks associated with permanent ownership. In the absence of ownership the consumption in the global marketplace has become more dynamic and has positively impacted various economic and social sectors. The concept of “consumption without ownership” can also be used in the area of cloud computing where the interaction between clients and providers generally involves the use of data storage and computational resources. Although a number of commercial providers are currently on the market, it is often beneficial for a user to consider capability from a number of different ones. This would prevent vendor lock-in and more economic choice for a user. Based on this observation, work on “Social Clouds” has involved using social relationships formed between individuals and institutions to establish Peer-2-Peer resource sharing networks, enabling market forces to determine how demand for resources can be met by a number of different (often individually owned) providers. In this paper we identify how trading and consumption within such a network could be enhanced by the dynamic emergence (or identification) of brokers – based on their social position in the network (based on connectivity metrics within a social network). We investigate how offering financial incentives to such brokers, once discovered, could help improve the number of trades that could take place with a network, thereby increasing consumption. A social score algorithm is described and simulated with PeerSim to validate our approach. We also compare the approach to a distributed dominating set algorithm – the closest approximation to our approach

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry