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Sobre

Sobre

Tatiana M. Pinho recebeu, em 2018, o grau de doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade de Trás-os-Montes e Alto Douro (UTAD), Portugal, e INESC TEC, ao abrigo de uma bolsa da Fundação para a Ciência e a Tecnologia (FCT). Em 2011, licenciou-se em Engenharia de Energias na UTAD, e recebeu o grau de mestre em Engenharia de Energias pela mesma universidade, em 2013. Atualmente é investigadora de pós-doutoramento no INESC TEC e os seus interesses de investigação incluem instrumentação, modelação e controlo adaptativo, em particular Model Predictive Control, aplicado a sistemas agro-florestais. 

Tópicos
de interesse
Detalhes

Detalhes

001
Publicações

2020

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Autores
Oliveira, PM; Solteiro Pires, EJ; Boaventura Cunha, J; Pinho, TM;

Publicação
Transactions of the Institute of Measurement and Control

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.

2020

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Autores
Oliveira, J; Oliveira, PM; Boaventura Cunha, J; Pinho, T;

Publicação
Robotics

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.

2019

Workload control and optimised order release: an assessment by simulation

Autores
Fernandes, NO; Thürer, M; Pinho, TM; Torres, P; Carmo Silva, S;

Publicação
International Journal of Production Research

Abstract
An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to ‘classical’ release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

2019

Digital Ampelographer: A CNN Based Preliminary Approach

Autores
Adão, T; Pinho, TM; Ferreira, A; Sousa, AMR; Pádua, L; Sousa, J; Sousa, JJ; Peres, E; Morais, R;

Publicação
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract

2019

Cyberphysical Network for Crop Monitoring and Fertigation Control

Autores
Coelho, JP; Rosse, HV; Cunha, JB; Pinho, TM;

Publicação
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract