Publications associées à Experimental study on prediction model for maximum rebound ratio

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Michel Bierlaire, Janody Pougala, Timothy Michael Hillel

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

2023

Near-bed stratification controls bottom hypoxia in ice-covered alpine lakes

Damien Bouffard, Pascal Perolo, Tomy Doda, Nicolas Thomas Georges Escoffier, Janine Rüegg, Hannah Elisa Chmiel, Hugo Nicolás Ulloa Sánchez, Camille Roland Marie Minaudo

In ice-covered lakes, near-bottom oxygen concentration decreases for most of the wintertime, sometimes down to the point that bottom waters become hypoxic. Studies insofar have reached divergent conclusions on whether climate change limits or reinforces th ...

American Society of Limnology and Oceanography2023

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Alexandre Massoud Alahi, Megh Hiren Shukla

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022

SafeAMC: Adversarial training for robust modulation classification models

Pascal Frossard, Javier Alejandro Maroto Morales

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

IEEE2022

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Jiannong Fang, Fernando Porté Agel, Lorenzo Donadio

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real ...

2021

Prediction Error–Based Parameter Estimation for Multi-Region MFD Networks

Nikolaos Geroliminis, Isik Ilber Sirmatel

City-level traffic management remains a challenging problem. Model predictive perimeter control approaches employing macroscopic fundamental diagram (MFD) based models of large-scale urban road traffic represent a high-performance solution with substantial ...

2020

On flow duration curve modelling in Alpine catchments

Ana Clara Pereira Barbosa Santos

Mountain regions are considered to be the natural "water towers" of the world due to their importance as sources of many rivers. Reliable tools to estimate the availability and variability of streamflows in such regions are still rare. In this context, the ...

EPFL2019

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Modeling chemical reaction systems is an important but complex task. The identified kinetic model must be able to explain all the underlying rate processes such as chemical reactions and heat and mass transfers. Traditionally, the modeling task is carried ...

2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Félix Benedito Clément Musil, Michele Ceriotti, Mikhail Langovoy, Michael John Willatt

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

Angular Accuracy of Steerable Feature Detectors

Michaël Unser, Arash Amini, Zsuzsanna Püspöki, John Paul Ward, Julien René Pierre Fageot

The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well ...

SIAM PUBLICATIONS2019

Importance of the advection scheme for the simulation of water isotopes over Antarctica by atmospheric general circulation models: A case study for present-day and Last Glacial Maximum with LMDZ-iso

Etienne Gabriel Henri Vignon

Atmospheric general circulation models (AGCMs) are known to have a warm and isotopically enriched bias over Antarctica. We test here the hypothesis that these biases are partly consequences of a too diffusive advection. Exploiting the LMDZ-iso model, we sh ...

2019

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...

EPFL2018

Dealing with Correlations in Discrete Choice Models

Anna Fernández Antolín

The focus of this thesis is to develop methods to address research challenges related to correlation patterns in discrete choice models. In the context of correlations within alternatives, we extend the novel methodology of the multiple indicator solution ...

EPFL2018

A computer-based prediction platform for the reaction of ozone with organic compounds in aqueous solution: kinetics and mechanisms

Urs von Gunten, Minju Lee, Kathrin Fenner

Ozonation of secondary wastewater effluents can reduce the discharge of micropollutants by transforming their chemical structures. Therefore, a better understanding of the formation of transformation products during ozonation is important. In this study, a ...

Royal Soc Chemistry2017

An efficient method for fiber bridging traction identification based on the R-curve: Formulation and experimental validation

Joël Cugnoni, Thomas Gmür, John Botsis, Guillaume Frossard

Fiber bridging is one of the main toughening mechanisms in mode I interlaminar and intralaminar fracture of laminated composites. Intact fibers exert closing forces on both faces of the crack, restraining crack propagation. An effective identification proc ...

Elsevier2017

Experiments versus theory for the initiation and propagation of radial hydraulic fractures in low permeability materials

Brice Tanguy Alphonse Lecampion

We compare numerical predictions of the initiation and propagation of radial fluid-driven fractures with laboratory experiments performed in different low permeability materials (PMMA, cement). In particular, we choose experiments where the time evolution ...

2017

Assessment of a recent tribocorrosion model for wear of metal-on-metal hip joints: Comparison between model predictions and simulator results

Stefano Mischler, Shoufan Cao

A composite running-in wear model for metal-on-metal artificial hip joints, which combines tribo-corrosion and lubrication aspects, was published recently. In order to check the quality of the model prediction, wear rates from nineteen well-controlled simu ...

Elsevier Science Sa2016

An improved model for predicting electrical conductance in nanochannels

Philippe Renaud, Arnaud Bertsch, Mojtaba Taghipoor

Nanochannel conductance measurements are commonly performed to characterize nanofluidic devices Theoretical analysis and experimental investigations imply that the nanochannel conductance does not follow the macro-scale models. It is generally accepted tha ...

2015

Robust system identification and model predictions in the presence of systematic uncertainty

Ian Smith, Romain Pasquier

Model-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under othe ...

Elsevier2015