Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

A geographic opportunistic forwarding strategy for vehicular named data networking

Studies in Computational Intelligence, 616

Bidirectional battery charger with grid-to-vehicle, vehicle-to-grid and vehicle-to-home technologies

This paper presents the development of na on-board bidirectional battery charger for Electric Vehicles (EVs) targeting Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Home (V2H) technologies. During the G2V operation mode the batteries are charged from the power grid with sinusoidal current and unitary power factor. During the V2G operation mode the energy stored in the batteries can be delivered back to the power grid contributing to the power system stability. In the V2H operation mode the energy stored in the batteries can be used to supply home loads during power outages, or to supply loads in places without connection to the power grid. Along the paper the hardware topology of the bidirectional battery charger is presented and the control algorithms are explained. Some considerations about the sizing of the AC side passive filter are taken into account in order to improve the performance in the three operation modes. The adopted topology and control algorithms are accessed through computer simulations and validated by experimental results achieved with a developed laboratory prototype operating in the different scenarios.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Development of a thermoelectric generator for the exhaust of a vehicle

Dissertação de mestrado integrado em Engenharia Mecânica

Contributos dos ambientes digitais online para a aprendizagem em contexto de atividades de apoio extraescolar

Dissertação de mestrado em Ciências da Educação (área de especialização em Tecnologia Educativa)

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Development of nanohydrogels as a vehicle for the release of bioactive compounds in food matrices

PhD in Chemical and Biological Engineering

Desenvolvimento de um motor BMW para servir de Range Extender

Dissertação de mestrado integrado em Engenharia Mecânica

Emotional and behavioral problems in preschoolers: risk factors and assessment Issues

Tese de Doutoramento em Psicologia Aplicada.

Operation modes for the electric vehicle in smart grids and smart homes : present and proposed modes

This paper presents the main operation modes for an electric vehicle (EV) battery charger framed in smart grids and smart homes, i.e., are discussed the present-day and are proposed new operation modes that can represent an asset towards EV adoption. Besides the well-known grid to vehicle (G2V) and vehicle to grid (V2G), this paper proposes two new operation modes: Home-to-vehicle (H2V), where the EV battery charger current is controlled according to the current consumption of the electrical appliances of the home (this operation mode is combined with the G2V and V2G); Vehicle-for-grid (V4G), where the EV battery charger is used for compensating current harmonics or reactive power, simultaneously with the G2V and V2G operation modes. The vehicle-to-home (V2H) operation mode, where the EV can operate as a power source in isolated systems or as an off-line uninterruptible power supply to feed priority appliances of the home during power outages of the electrical grid is presented in this paper framed with the other operation modes. These five operation modes were validated through experimental results using a developed 3.6 kW bidirectional EV battery charger prototype, which was specially designed for these operation modes. The paper describes the developed EV battery charger prototype, detailing the power theory and the voltage and current control strategies used in the control system. The paper presents experimental results for the various operation modes, both in steady-state and during transients.