Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Interest
Topics
Details

Details

  • Name

    Ana Pereira
  • Role

    External Research Collaborator
  • Since

    19th January 2022
Publications

2024

Analysis of Constructive Heuristics with Cuckoo Search Algorithm, Firefly Algorithm and Simulated Annealing in Scheduling Problems

Authors
Moreira, C; Costa, C; Santos, S; Madureira, M; Barbosa, M;

Publication
Lecture Notes in Mechanical Engineering

Abstract
Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, Firefly Algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the Firefly Algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

Supervised
thesis

2023

Técnicas de encriptação e anonimização para Big Data

Author
DAVID MIGUEL COUTINHO MARQUES

Institution
IPP-ISEP

2023

Blockchain na Indústria 4.0: Caso de estudo na indústria farmacêutica

Author
LUÍS FRANCISCO RODRIGUES DE SOUSA

Institution
IPP-ISEP

2022

Estimativa do Peso de Corvinas e Deteção de Períodos de Alimentação

Author
JOÃO LEAL MADUREIRA DIAS

Institution
IPP-ISEP

2022

Deteção Automática de Incêndios através do uso de CNN

Author
NUNO FILIPE MACHADO LOPES DA SILVA

Institution
IPP-ISEP

2022

Deteção automática dos locais com maior índice de sinistralidade

Author
TADEU TEIXEIRA GUIMARÃES JÚNIOR

Institution
IPP-ISEP