Paulo Moura Oliveira

Abstract
A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends. © The Author(s) 2020.

Abstract
The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

Abstract

Abstract
O objetivo é investigar as ferramentas e metodologias utilizadas no Ensino Superior (ES) de Engenharia Elétrica e de Computadores (EEC), identificando as unidades curriculares que as utilizaram. A análise foi baseada na percepção dos coordenadores de curso. Utilizou-se a triangulação entre múltiplas metodologias e diferentes métodos de análise, combinando a análise léxica, análise de keywords e análise de conteúdo. Entrevistou-se coordenadores de curso em três universidades em Portugal e no Brasil. Na análise de conteúdo emergiram duas categorias: percepção dos coordenadores sobre metodologias não tradicionais e percepção sobre ferramentas de software como recursos didáticos. As iniciativas utilizando processos e metodologias de ensino não tradicionais foram reduzidas e pontuais, com ações individuais dos docentes e não de padrões de ensino discutidos e institucionalizados. A precariedade da infraestrutura dos laboratórios e das licenças de software foi um relato comum.Palavras-chave: Metodologias não Tradicionais. Coordenadores de Curso. Ferramentas. Engenharia Elétrica. Ensino Superior.

Abstract
The potentialities of using mobile devices such as smartphones for teaching/learning purposes are huge. However, in some teaching areas its use is still residual. The use of mobile applications in the context of teaching PLC programming techniques is addressed in this work. The MIT App-Inventor II is deployed to develop mobile applications for learning purposes. An android based application entitled Time-Counts is proposed here, developed to support the teaching/learning process of timers. Preliminary results regarding its use by students are presented. © 2019, Springer International Publishing AG, part of Springer Nature.