Zobrazeno 1 - 10 of 14 pro vyhledávání: '"André Pilastri"'
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Akademický článek
Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection
Autor: Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri, Paulo Cortez
Publikováno v: Computers, Vol 11, Iss 4, p 54 (2022)
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightenin
Externí odkaz: https://doaj.org/article/5e4653427bd0479abc89cc30fefa28ef
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2
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics
Autor: Rui Ribeiro, André Pilastri, Carla Moura, José Morgado, Paulo Cortez
In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3303aa499a02afc9dc0927d7569706f3
https://hdl.handle.net/1822/85534
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4
International revenue share fraud prediction on the 5G edge using federated learning
Autor: Luís Ferreira, Leopoldo Silva, Francisco Morais, Carlos Manuel Martins, Pedro Miguel Pires, Helena Rodrigues, Paulo Cortez, André Pilastri
Publikováno v: Computing.
Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectur
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9f85a098ff72c1df12a3a63281524314
https://doi.org/10.1007/s00607-023-01174-w
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5
Using supervised and one-class automated machine learning for predictive maintenance
Autor: Luís Ferreira, André Pilastri, Filipe Romano, Paulo Cortez
Predictive Maintenance (PdM) is a critical area that is benefiting from the Industry 4.0 advent. Recently, several attempts have been made to apply Machine Learning (ML) to PdM, with the majority of the research studies assuming an expert-based ML mo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc3f6c489b3a527a8376299b712fe80a
https://hdl.handle.net/1822/81437
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6
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio
Autor: Gabriel Coelho, Luís Miguel Matos, Pedro José Pereira, André Ferreira, André Pilastri, Paulo Cortez
The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the m
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e3a709cb0b4a4458d401388b478a379
https://hdl.handle.net/1822/81433
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7
A federated machine learning approach to detect international revenue share fraud on the 5G edge
Autor: Luís Ferreira, Leopoldo Silva, Diana Pinho, Francisco Morais, Carlos Manuel Martins, Pedro Miguel Pires, Pedro Fidalgo, Helena Rodrigues, Paulo Cortez, André Pilastri
The fifth-generation (5G) of broadband cellular networks is giving rise to new paradigms of distributed computing, such as Edge Computing and Multi-access Edge Computing (MEC). The possibility of hosting Machine Learning (ML) applications close to th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e527289ed9ac69c203cfbee71f97d93
https://hdl.handle.net/1822/79635
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8
Production time prediction for contract manufacturing industries using automated machine learning
Autor: Afonso Sousa, Luís Ferreira, Rui Ribeiro, João Xavier, André Pilastri, Paulo Cortez
Publikováno v: IFIP Advances in Information and Communication Technology ISBN: 9783031083365
The estimation of production time is an essential part of the manufacturing domain, allowing companies to optimize their production plan and meet the dates required by the customers. In the last years, there have been several approaches that use Mach
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::557353b7a3a4c0786e574fbaec98a5d8
https://hdl.handle.net/1822/81438
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