Zobrazeno 1 - 10
of 194
pro vyhledávání: '"Adil Rasheed"'
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-21 (2024)
Abstract With the advent of big data, it has become increasingly difficult to obtain high-quality data. Solutions are required to remove undesired outlier samples from massively large datasets. Ship operators rely on high-frequency in-service dataset
Externí odkaz:
https://doaj.org/article/d6fe8d8f5ed14265a6ced9c9288cd1f4
Publikováno v:
IEEE Access, Vol 12, Pp 13213-13232 (2024)
Generative adversarial networks (GANs) have drawn considerable attention in recent years for their proven capability in generating synthetic data which can be utilised for multiple purposes. While GANs have demonstrated tremendous successes in produc
Externí odkaz:
https://doaj.org/article/bb4c8adab78442f5ba7d5d643a9949cd
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machin
Externí odkaz:
https://doaj.org/article/10d4a5eb7a0641b7b53ff790b7ac8b0a
Autor:
Trevor Simard, Richard Jung, Pietro Di Santo, Alisha Labinaz, Spencer Short, Pouya Motazedian, Shan Dhaliwal, Dhruv Sarma, Adil Rasheed, F. Daniel Ramirez, Michael Froeschl, Marino Labinaz, David R. Holmes, Mohamad Alkhouli, Benjamin Hibbert
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 10 (2023)
IntroductionPatients undergoing coronary stent implantation incur a 2% annual rate of adverse events, largely driven by in-stent restenosis (ISR) due to neointimal (NI) tissue proliferation, a process in which smooth muscle cell (SMC) biology may pla
Externí odkaz:
https://doaj.org/article/aa59808e112b41c6b41942f41a9058af
Autor:
Emil Johannesen Haugstvedt, Alberto Mino Calero, Erlend Torje Berg Lundby, Adil Rasheed, Jan Tommy Gravdahl
Publikováno v:
IEEE Access, Vol 11, Pp 131435-131452 (2023)
Sparsity-promoting techniques show promising results in improving the generalization of neural networks. However, the literature contains limited information on how different sparsity techniques affect generalization when using neural networks to mod
Externí odkaz:
https://doaj.org/article/479db49c90fa4166b65eca4e84119558
Autor:
Florian Stadtmann, Adil Rasheed, Trond Kvamsdal, Kjetil Andre Johannessen, Omer San, Konstanze Kolle, John Olav Tande, Idar Barstad, Alexis Benhamou, Thomas Brathaug, Tore Christiansen, Anouk-Letizia Firle, Alexander Fjeldly, Lars Froyd, Alexander Gleim, Alexander Hoiberget, Catherine Meissner, Guttorm Nygard, Jorgen Olsen, Havard Paulshus, Tore Rasmussen, Elling Rishoff, Francesco Scibilia, John Olav Skogas
Publikováno v:
IEEE Access, Vol 11, Pp 110762-110795 (2023)
This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels
Externí odkaz:
https://doaj.org/article/3db25e99bdfa4a6385b8d19f703f6eb1
Publikováno v:
IEEE Access, Vol 11, Pp 35035-35058 (2023)
A digital twin is a powerful tool that can help monitor and optimize physical assets in real-time. Simply put, it is a virtual representation of a physical asset, enabled through data and simulators, that can be used for a variety of purposes such as
Externí odkaz:
https://doaj.org/article/bf5bb0c86a06405582a8a58dc95416c5
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interac
Externí odkaz:
https://doaj.org/article/dfd0be333b6b4b4689f0c29365054eb6
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and
Externí odkaz:
https://doaj.org/article/dc7beb6894c94891922172b414818dd9
Publikováno v:
International Journal of Naval Architecture and Ocean Engineering, Vol 15, Iss , Pp 100550- (2023)
The hydrodynamic performance of a sea-going ship can be analyzed using data from different sources, like onboard recorded in-service data, AIS data, and noon reports. Each of these sources is known to have its inherent problems. The current work high
Externí odkaz:
https://doaj.org/article/43ce511d60394b7289f7948c5b5afdf8