Publications associées à Sonomètre

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer, Stanislas Ducotterd

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Zenodo2023

The neuromechanics of animal locomotion: From biology to robotics and back

Auke Ijspeert, Pavan P Ramdya

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating anima ...

AMER ASSOC ADVANCEMENT SCIENCE2023

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Yara Kayyali El Alem

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

EPFL2023

Compound Admittance Matrix Estimation of Three-phase Untransposed Power Distribution Grids using Synchrophasor Measurements

Jean-Yves Le Boudec, Mario Paolone, Fabrizio Sossan, Rahul Kumar Gupta

The paper considers the problem of estimating the parameters of the compound admittance matrix of three-phase untransposed low-and medium-voltage electrical distribution networks using synchrophasor measurements from phasor measurement units (PMUs). The wo ...

2021

Analysis of the first COLIBRI fuel rods oscillation campaign in the CROCUS reactor for the European project CORTEX

Andreas Pautz, Vincent Pierre Lamirand, Mathieu Hursin, Carlo Fiorina, Oskari Ville Pakari, Pavel Frajtag, Axel Guy Marie Laureau, Adolfo Rais, Daniel Godat

The Horizon2020 European project CORTEX aims at developing an innovative core monitoring technique that allows detecting anomalies in nuclear reactors, such as excessive vibrations of core internals, flow blockage, or coolant inlet perturbations. The techn ...

2021

A phenomenological extended-reaction boundary model for time-domain wave-based acoustic simulations under sparse reflection conditions using a wave splitting method

Jan Sickmann Hesthaven

In environmental acoustics and in room acoustics, many surfaces exhibit extended-reaction (ER) behavior, i.e., their surface impedance varies with the angle of the incident sound wave. This paper presents a phenomenological method for modeling such angle d ...

ELSEVIER SCI LTD2021

Distributed Predictive Drone Swarms in Cluttered Environments

Dario Floreano, Fabrizio Schiano, Enrica Soria

Recent works in aerial robotics show that the self-organized and cohesive flight of swarms can emerge from the exchange of purely local information between neighboring agents. However, most of the current swarm models are not capable of flight in densely c ...

2022

Effects of impurities and local behavior characterization using active thermography on solid oxide electrolysis cells

Guillaume Jeanmonod

Ambitious policies are needed from every country to drastically curtail the anthropogenic emission of greenhouse gases responsible for the on-going modification of the earth's climate. A quick transition towards a society fully based on renewable materials ...

EPFL2021

Low frequency sound field reconstruction in a non-rectangular room using a small number of microphones

Hervé Lissek, Vu Thach Pham

An accurate knowledge of the sound field distribution inside a room is required to identify and optimally locate corrective measures for room acoustics. However, the spatial recovery of the sound field would result in an impractically high number of microp ...

2020

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-gr ...

IEEE2020