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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

007
Publicações

2021

Prototyping IoT-Based Virtual Environments: An Approach toward the Sustainable Remote Management of Distributed Mulsemedia Setups

Autores
Adao, T; Pinho, T; Padua, L; Magalhaes, LG; Sousa, JJ; Peres, E;

Publicação
Applied Sciences

Abstract
Business models built upon multimedia/multisensory setups delivering user experiences within disparate contexts—entertainment, tourism, cultural heritage, etc.—usually comprise the installation and in-situ management of both equipment and digital contents. Considering each setup as unique in its purpose, location, layout, equipment and digital contents, monitoring and control operations may add up to a hefty cost over time. Software and hardware agnosticity may be of value to lessen complexity and provide more sustainable management processes and tools. Distributed computing under the Internet of Things (IoT) paradigm may enable management processes capable of providing both remote control and monitoring of multimedia/multisensory experiences made available in different venues. A prototyping software to perform IoT multimedia/multisensory simulations is presented in this paper. It is fully based on virtual environments that enable the remote design, layout, and configuration of each experience in a transparent way, without regard of software and hardware. Furthermore, pipelines to deliver contents may be defined, managed, and updated in a context-aware environment. This software was tested in the laboratory and was proven as a sustainable approach to manage multimedia/multisensory projects. It is currently being field-tested by an international multimedia company for further validation.

2021

Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

Autores
Aguiar, AS; Magalhaes, SA; dos Santos, FN; Castro, L; Pinho, T; Valente, J; Martins, R; Boaventura Cunha, J;

Publicação
Agronomy

Abstract
The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages: the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.

2020

Workload control and optimised order release: an assessment by simulation

Autores
Fernandes, NO; Thurer, 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.

2020

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Autores
Oliveira, PM; Pires, EJS; 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.