Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

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

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Eficiência energética de sistemas de abastecimento de água recorrendo a técnicas de optimização e uso de microhidroturbinas

Doutoramento em Engenharia MecânicaA eficiência energética representa um papel significativo no esforço para a sustentabilidade por parte das empresas das águas, uma vez que, mundialmente, 35% dos custos totais com a produção de água (12 mil milhões de euros) estão a ser gastos em energia. Os principais obstáculos à melhoria da eficiência energética dos sistemas de abastecimento de água estão essencialmente relacionados com a complexidade dos sistemas e também com os baixos níveis de resiliência a nível operacional. O principal objectivo desta tese é o desenvolvimento de uma metodologia computacional automática capaz de: (i) aplicar diferentes técnicas para operação optimizada de qualquer rede de água, (ii) procurar por possíveis localizações para recuperação de energia utilizando turbinas e, posteriormente, seleccionar o tipo de turbina mais adequado. Quatro tópicos fundamentais que representam um papel crucial na melhoria da eficiência energética das redes são abordados: (i) modelação e simulação das redes, (ii) optimização operacional, (iii) previsão de consumos de água e (iv) aproveitamento de energia utilizando hidroturbinas. Uma abordagem de optimização para lidar simultaneamente com válvulas, bombas de velocidade fixa e bombas de velocidade variável é proposta. Os períodos de operação de todas as válvulas e bombas, assim como as velocidades das bombas de velocidade variável são utilizados como variáveis de decisão num problema de optimização de forma a minimizar os custos energéticos associados à operação de uma rede. O simulador hidráulico EPANET 2.0 é utilizado para verificar e garantir que as soluções obtidas são viáveis. Redes benchmark são testadas com diferentes técnicas de optimização, incluindo vários algoritmos, tais como Nelder-Mead Simplex, Algoritmos Genéticos (GA), optimização por bando de partículas e evolução diferencial. Propõe-se uma nova formulação para prever a variação de curvas de eficiência das bombas com a velocidade e esta é comparada com os poucos métodos existentes para o efeito, incluindo o utilizado pelo EPANET. Um processo automático para a análise de qualquer rede hidráulica de forma a identificar locais e seleccionar turbinas para aproveitamento de energia é implementado e validado com um caso de estudo. Finalmente, modelos para previsão de consumos de água a curto prazo são desenvolvidos e testados com dados recolhidos de uma rede de água Portuguesa. Modelos de previsão tradicionais baseados em modelos de alisamento exponencial e modelos naïve são desenvolvidos utilizando folhas de cálculo, enquanto que os modelos baseados em redes neuronais artificiais são desenvolvidos no Matlab. Analisa-se ainda o efeito de diferentes variáveis de entrada (incluindo variáveis antrópicas e meteorológicas) nestes últimos modelos.Energy efficiency plays a large role in the sustainability effort of water utilities since, globally, 35% of the total expenses with water production (12 billion euros) are being spent on energy. The main obstacle for the efficiency improvement in water supply systems is mostly related to the complexity of the systems and also to the low levels of resilience in their operations. The main goal of this thesis is the development of an automatic computational methodology capable of (i) applying distinct techniques for the optimal operation of any water network and (ii) searching for possible locations for energy recovery using turbines and then selecting the most adequate type of turbine. Four major topics that play crucial roles in the networks efficiency improvement are addressed: (i) modelling and simulation of the networks, (ii) operational optimisation, (iii) water demand forecasting and (iv) energy recovery using hydroturbines. An optimisation approach dealing simultaneously with valves, fixed-speed pumps and variable-speed pumps is proposed. The operating periods of all valves and pumps and also the speed settings of variable-speed pumps are used as decision variables in an optimisation problem in order to minimise the energy costs associated to a network operation. The hydraulic simulator EPANET 2.0 is used to verify and ensure the feasibility of the obtained solutions. Benchmark networks are tested with distinct optimisation techniques including several algorithms, such as Nelder-Mead Simplex, Genetic Algorithms (GA), Particle Swarm Optimisation (PSO) and Differential Evolution (DE). A novel formula for predicting the pumps efficiency changes with speed is proposed and compared with the few existing methods, including the one used by EPANET. An automatic process for the analysis of any water network in order to locate sites and select turbines for energy recovery is implemented and validated with a case-study. Finally, models for predicting short-term water demands are developed and tested with data collected from a Portuguese water network. Traditional forecasting models based on exponential smoothing and naïve models are developed using a spreadsheet while artificial neural network-based models are developed in Matlab. The effect of distinct input variables (including anthropic and meteorological variables) in the ANN-based models is analysed

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Sustainable Business Models

The dynamically changing world economy, in an era of intensive development and globalization, creates new needs in both the theoretical models of management and in the practical discussion related to the perception of business. Because of new economic phenomena related to the crisis, there is a need for the design and operationalization of innovative business models for companies. Due to the fact that in times of crisis, the principles of strategic balance are particularly important; these business models can be sustainable business models. Moreover, it is essential to skillfully use different methods and concepts of management to ensure the continuity of business. It seems that sustainable business models, in their essence, can support companies' effectiveness and contribute to their stable, sustainable functioning in the difficult, ever-changing market. This Special Issue aims to discuss the key mechanisms concerning the design and operationalization of sustainable business models, from a strategic perspective. We invite you to contribute to this Issue by submitting comprehensive reviews, case studies, or research articles. Papers selected for this Special Issue are subject to a rigorous peer review procedure, with the aim of rapid and wide dissemination of research results, developments, and applications

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed