Searched for: subject%3A%22Artificial%255C%2BNeural%255C%2BNetworks%22
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Llasag Rosero, Raúl (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)
Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....
journal article 2024
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Safaei, Mohammad (author), Hejazian, Mahsa (author), Pedrammehr, Siamak (author), Pakzad, Sajjad (author), Ettefagh, Mir Mohammad (author), Fotouhi, M. (author)
Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced...
journal article 2024
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Mirkhalaf, Mohsen (author), Rocha, I.B.C.M. (author)
During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites. One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using...
journal article 2024
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Paredes-Vallés, Federico (author)
In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...
doctoral thesis 2023
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Harmankaya, Hüseyin (author)
Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload...
master thesis 2023
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Makaveev, Momchil (author)
This project proposes and evaluates a novel concept for an airspeed instrument aimed at small hybrid unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer formed over the vehicle’s body to its airspeed. The instrument consists of two microphones,...
master thesis 2023
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de Araujo Alves Lima, Rosemere (author), Tao, R. (author), Bernasconi, A. (author), Carboni, Michele (author), Carrere, Nicolas (author), Teixeira De Freitas, S. (author)
Understanding the relationship between the sensors’ outputs and the damage evolution within the joints is becoming increasingly crucial to improving structural health monitoring systems and collecting data to improve the joint’s design. Therefore, a study of the acoustic emission method associated with visual fracture evaluation was proposed to...
poster 2023
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Alves Maia, M. (author), Rocha, I.B.C.M. (author), Kerfriden, P. (author), van der Meer, F.P. (author)
Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element analysis (FE<sup>2</sup>) due to the exceedingly high computational costs often associated with it...
journal article 2023
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Odendaal, Kirsten (author), Alkemade, Aaron (author), Kana, A.A. (author)
The adverse human contribution to global climate change has forced the yachting industry to acknowledge the need to reduce its environmental impact due to the client's increasing pressure and potential future regulations to limit the ecological effects. Unfortunately, current real-world data presents a significant disparity between predicted...
journal article 2023
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Ruland, Oscar (author), Mkhoyan, T. (author), De Breuker, R. (author), Wang, Xuerui (author)
journal article 2023
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Yalcin Kavus, Bahar (author), Gülüm Taş, P. (author), Taskin, Alev (author)
Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in...
journal article 2023
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Kamel Targhi, Elahe (author), Emami Niri, Mohammad (author), Zitha, P.L.J. (author)
Cross-linked polymer gel is widely used in the oil and gas industry to block high permeability conduits and reduce water cut. The complex nature of this fluid, especially regarding flow in porous media, makes its numerical simulation very time-consuming. This study presents an approach to designing an Artificial Neural Network (ANN) model...
journal article 2023
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Onat, N.B. (author), Roldan Montero, I. (author), Fioranelli, F. (author), Yarovoy, Alexander (author), Aslan, Y. (author)
Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in...
conference paper 2023
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Ottati, F. (author), Gao, Chang (author), Chen, Qinyu (author), Brignone, Giovanni (author), Casu, Mario R. (author), Eshraghian, Jason K. (author), Lavagno, Luciano (author)
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep...
journal article 2023
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Chen, Siyu (author), Chen, Can (author), Ma, Tao (author), Han, Chengjia (author), Luo, Haoyuan (author), Wang, Siqi (author), Gao, Y. (author), Yang, Yaowen (author)
Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement...
journal article 2023
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Keulen, D. (author), van der Hagen, Erik (author), Geldhof, Geoffroy (author), Le Bussy, Olivier (author), Pabst, Martin (author), Ottens, M. (author)
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or...
journal article 2023
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Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)
Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...
journal article 2023
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Bani Mohammad Ali, Amir (author), Valizadeh Sotubadi, Saleh (author), Alimirzaei, Sajad (author), Ahmadi Najafabadi, Mehdi (author), Pahlavan, Lotfollah (author)
Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring...
journal article 2023
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Waaijer, Joost (author)
The goal of this thesis is to build an artificial neural network(ANN) surrogate model, that predicts the crashworthiness performance of a structure. The structure used in this research is a thermoplastic fibre-reinforced composite aircraft subfloor section which is part of the SmarT multifUNctioNal and INteGrated thermoplastic fuselage (STUNNING...
master thesis 2022
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Kaneko, Kei (author)
Flight envelope prediction is a challenging task where one of the difficulties is that widely used methods, like the level set methods, are impractical for systems with more than four coupled state dimensions due to the “curse of dimensionality”. Monte-Carlo simulation based approach suffers less from this, however a large number of simulations...
master thesis 2022
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