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Details

  • Name

    Ana Pereira
  • Role

    External Research Collaborator
  • Since

    19th January 2022
Publications

2023

LSTS Toolchain Framework for Deep Learning Implementation into Autonomous Underwater Vehicle

Authors
Aubard, M; Madureira, A; Madureira, L; Campos, R; Costa, M; Pinto, J; Sousa, J;

Publication
OCEANS 2023 - LIMERICK

Abstract
The development of increasingly autonomous underwater vehicles has long been a research focus in underwater robotics. Recent advances in deep learning have shown promising results, offering the potential for fully autonomous behavior in underwater vehicles. However, its implementation requires improvements to the current vehicles. This paper proposes an onboard data processing framework for Deep Learning implementation. The proposed framework aims to increase the autonomy of the vehicles by allowing them to interact with their environment in real time, enabling real-time detection, control, and navigation.

2023

Roadmap on artificial intelligence and big data techniques for superconductivity

Authors
Yazdani-Asrami, M; Song, WJ; Morandi, A; De Carne, G; Murta-Pina, J; Pronto, A; Oliveira, R; Grilli, F; Pardo, E; Parizh, M; Shen, BY; Coombs, T; Salmi, T; Wu, D; Coatanea, E; Moseley, DA; Badcock, RA; Zhang, MJ; Marinozzi, V; Tran, N; Wielgosz, M; Skoczen, A; Tzelepis, D; Meliopoulos, S; Vilhena, N; Sotelo, G; Jiang, ZA; Grosse, V; Bagni, T; Mauro, D; Senatore, C; Mankevich, A; Amelichev, V; Samoilenkov, S; Yoon, TL; Wang, Y; Camata, RP; Chen, CC; Madureira, AM; Abraham, A;

Publication
SUPERCONDUCTOR SCIENCE & TECHNOLOGY

Abstract
This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 yr time-frame.

2023

The Impact of the Size of the Partition in the Performance of Bat Algorithm

Authors
Sousa, B; Santos, AS; Madureira, AM;

Publication
Lecture Notes in Networks and Systems

Abstract
In this article the influence of the maximum partition size on the performance of a discrete version of the Bat Algorithm (BA) is studied. The Bat Algorithm is a population-based meta-heuristic based on swarm intelligence developed for continuous problems with exceptional results. Thus, it has a set of parameters that must be studied in order to enhance the performance of the meta-heuristic. This paper aims to investigate whether the maximum size of the partitions used for the search operations throughout the algorithm should not also be considered as a parameter. First, a literature review was conducted, with special focus on the parameterization of the meta-heuristics and each of the parameters currently used in the algorithm, followed by its implementation in VBA in Microsoft Excel. After a thorough parameterization of the discrete algorithm, different maximum partition sizes were applied to 30 normally distributed instances to draw broader conclusions. In addition, they were also tested for different sizes of the problem to see if they had an influence on the results obtained. Finally, a statistical analysis was carried out, where it was possible to conclude that there was no maximum partition value for which superiority could be proven, and so the size of the partition should be considered a parameter in the bat algorithm and included in the parametrization of BA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

A Novel Approach to the Two-Dimensional Cargo Load Problem

Authors
Mateus, F; Santos, AS; Brito, MF; Madureira, AM;

Publication
Lecture Notes in Networks and Systems

Abstract
The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories

Authors
Antal, L; Aubard, M; Ábrahám, E; Madureira, A; Madureira, L; Costa, M; Pinto, J; Campos, R;

Publication
Lecture Notes in Networks and Systems

Abstract
Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging. A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Supervised
thesis

2022

Estimativa do Peso de Corvinas e Deteção de Períodos de Alimentação

Author
JOÃO LEAL MADUREIRA DIAS

Institution
IPP-ISEP

2018

Data Mining techniques for Prediction of secondary and 3D structures of proteins

Author
Luís Miguel da Costa Oliveira

Institution
UP-FEUP

2016

Sensors based on Taper Device for Knot Configuration

Author
André Gomes

Institution
UP-FCUP

2016

Cavity Ring-down Characterization Technique

Author
regina Magalhâes

Institution
UP-FCUP