Experimental study on prediction model for maximum rebound ratio

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

The oxidative capacity of past atmospheres is highly uncertain. We present here a new climate-biosphere-chemistry modeling framework to determine oxidant levels in the present and past troposphere. We use the GEOS-Chem chemical transport model driven by me ...

Experimental study on prediction model for maximum rebound ratio

Chat with Graph Search

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

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

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

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

SafeAMC: Adversarial training for robust modulation classification models

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

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

On flow duration curve modelling in Alpine catchments

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

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Angular Accuracy of Steerable Feature Detectors

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

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

Dealing with Correlations in Discrete Choice Models

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

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

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

An improved model for predicting electrical conductance in nanochannels

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

Factors controlling variability in the oxidative capacity of the troposphere since the Last Glacial Maximum