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Graph neural networks for dynamic modeling of roller bearings
Related publications (49)
Graph Laplacian Mixture Model
Pascal Frossard, Hermina Petric Maretic
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Edge colorings of graphs without monochromatic stars
In this note, we improve on results of Hoppen, Kohayakawa and Lefmann about the maximum number of edge colorings without monochromatic copies of a star of a fixed size that a graph on n vertices may admit. Our results rely on an improved application of an ...
ELSEVIER2020Pressure Profile Measurements within The Gas Film of Journal Foil Bearings Using An Instrumented Rotor with Telemetry
Jürg Alexander Schiffmann, Karim Magdi Zakaria Abdelrahman Shalash
Foil bearings are strong candidates to support oil-free turbomachinery. Although foil bearings are a widely used technology, models describing their behavior are not validated using the film pressure, which is the fundamental variable of any fluid film bea ...
2020Strain and filler ratio transitions from chains network to filler network damage in EPDM during single and cyclic loadings
Chains and filler network damage were investigated during single and multiple cycles on a series of vulcanized EPDM containing various filler contents. In both series of experiments, a strain and a filler ratio transitions for damage mechanisms were identi ...
2020Extrapolating Paths with Graph Neural Networks
Andreas Loukas, Jean-Baptiste Francis Marie Juliette Cordonnier
We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the int ...
IJCAI, Inc.2019Graph Learning with Partial Observations: Role of Degree Concentration
Ali H. Sayed, Augusto José Rabelo Almeida Santos
In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...
IEEE2019Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs
Convolutional neural networks (CNNs) are powerful tools in Deep Learning mainly due to their ability to exploit the translational symmetry present in images, as they are equivariant to translations. Other datasets present different types of symmetries (e.g ...
2019Design of Load-Bearing Systems for Open-Ended Downstream Reuse
This paper discusses the design of load-bearing systems for buildings with regard to their current lack of open-ended reusability. The reason for dismantling load-bearing systems today tends to be less related to material degradation than to a loss of func ...
2019Pressure Profile Measurements Within The Gas Film Of Journal Foil Bearings Using An Instrumented Rotor With Telemetry
Jürg Alexander Schiffmann, Karim Magdi Zakaria Abdelrahman Shalash
Foil bearings are strong candidates to support oil -free turbomachinery. Although foil bearings are a widely used technology, models describing their behavior are not validated using the film pressure, which is the fundamental variable of any fluid film be ...
Amer Soc Mechanical Engineers2019Deep learning on graph for semantic segmentation of point cloud
Alexandre Thierry Robert Cherqui
This master thesis provides in-depth explanations of how deep learning and graph theory can be used together to perform pointwise classification in 3D point clouds obtained by combinations of geospatial images. That scene understanding problem arises in a ...
2018