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Federal enterprise architecture

Publications associées (120)

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be consider ...

Pergamon-Elsevier Science Ltd2024

An improved analytical framework for flow prediction inside and downstream of wind farms

Fernando Porté Agel, Marwa Souaiby

This study evaluates available analytical wake models for flow prediction inside and downstream of wind farms of different sizes and layouts using large-eddy simulation (LES), and introduces an enhanced analytical framework. All the tested analytical wake ...

2024

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Devis Tuia, Gaston Jean Lenczner, Jan Dirk Wegner, Jan Pisl, Marc Conrad Russwurm, Lloyd Haydn Hughes

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

Iop Publishing Ltd2024

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Chiheb Ben Mahmoud

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Washington2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

IEEE2023

Transformer Models for Vision

Jean-Baptiste Francis Marie Juliette Cordonnier

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins.All these successful methods depend on a gradient-based learning algorithm to train a model on massive a ...

EPFL2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

Comparative approach of the environmental impact induced by different architectural visions of a new periurban neighbourhood

Sophie Lufkin, Sergi Aguacil Moreno, Martine Laprise

The objective of this research work is to compare the environmental impact, expressed in terms of non-renewable cumulative energy demand (CEDnr) and greenhouse gas (GHG) emissions, of nine architectural visions for a new neighbourhood located in the periur ...

2022

Model-based Systems Engineering Papers Analysis based on Word Cloud Visualization

Jinzhi Lu, Xiaochen Zheng

With the continuous improvement of system scale and complexity, model-based system engineering (MBSE) is of great importance in the practice of system engineering (SE). MBSE has been widely concerned in industry, especially in the field of complex equipmen ...

IEEE2022

The emergence of cognitive digital twin: vision, challenges and opportunities

Jinzhi Lu, Xiaochen Zheng

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multip ...

TAYLOR & FRANCIS LTD2021

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