Publication

Graph neural networks for dynamic modeling of roller bearings

Related publications (49)

Graph Chatbot

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.

A Time-Vertex Signal Processing Framework

Nathanaël Perraudin, Benjamin Ricaud, Andreas Loukas, Francesco Grassi

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate th ...

Institute of Electrical and Electronics Engineers2018

Force Analysis of a Slotless Lorentz-Type Active Magnetic Bearing Actuator

Yves Perriard, Douglas Martins Araujo, Leopoldo Rossini, Guzmán Borque Gallego

This article presents the analysis of the three-dimensional forces applied by a slotless Lorentz-type fully-active magnetic bearing actuator. An analytical model is presented for defining the forces applied by every radial and axial bearing of the actuator ...

2018

Comparative Evaluation of Foil Bearings with Different Compliant Structures for Improved Manufacturability

Jürg Alexander Schiffmann, Karim Magdi Zakaria Abdelrahman Shalash

Potential geometrical deviations in bump foil bearings due to manufacturing uncertainty can have significant effects on both the local stiffness and clearance, and hence, affecting the overall bearing performance. The manufacturing uncertainty of bump type ...

Amer Soc Mechanical Engineers2017

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016

Privacy and Dynamics of Social Networks

Pedram Pedarsani

Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a h ...

EPFL2013

The Range of a Random Walk on a Comb

János Pach, Gábor Tardos

The graph obtained from the integer grid Z x Z by the removal of all horizontal edges that do not belong to the x-axis is called a comb. In a random walk on a graph, whenever a walker is at a vertex v, in the next step it will visit one of the neighbors of ...

Electronic Journal Of Combinatorics2013

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Space crossing numbers

We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...

2011

Conway's conjecture for monotone thrackles

János Pach

A drawing of a graph in the plane is called a thrackle if every pair of edges meet precisely once, either at a common vertex or at a proper crossing. According to Conway's conjecture, every thrackle has at most as many edges as vertices. We prove this conj ...

Mathematical Association of America2011

Semi-Supervised Learning with Spectral Graph Wavelets

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

2011