Results 21 to 30 of about 90,914 (289)

clip-mesh: generating textured meshes from text using pretrained image-text models [PDF]

, 2023
The following thesis introduces a novel technique for generating textured mesh models without any 3D supervision based solely on a text prompt. This is done by deforming the control shape of a limit subdivided surface along with its texture and normal ...
mohammad khalid, nasir
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Prediction of fatigue crack growth using convolutional neural network (2nd Report, Prediction of crack propagation on different levels)

Nihon Kikai Gakkai ronbunshu, 2023
In this paper, the prediction of crack propagation with two cracks using machine learning is described. The analysis results of crack propagation by s-version FEM (s-FEM), which combines the automatic mesh generation technique, are used for generation of
Rekisei OZAWA, Takuya TOYOSHI, Ryuhei TAICHI, Yoshitaka WADA   +3 more
doaj   +1 more source

OpenFOAM advanced training. Moving meshes, rigid body motion, adaptive mesh refinement, and overset meshes

, 2022
These learning resources (slides and tutorials) are related to the following training session:OpenFOAM advanced training.Moving meshes, rigid body motion, adaptive mesh refinement, and overset meshesThis training session was delivered in FEB2022 using OpenFOAM 9.The overset meshes training is based on OpenFOAM ESI (2020 or newer).The video lectures are
openaire   +1 more source

Mesh Discriminative Features for 3D Steganalysis [PDF]

, 2014
We propose a steganalytic algorithm for triangle meshes, based on the supervised training of a classifier by discriminative feature vectors. After a normalization step, the triangle mesh is calibrated by one step of Laplacian smoothing and then a feature
Yang, Ying, Ivrissimtzis, Ioannis
core   +1 more source

Beyond coverage: improving the quality of antenatal care delivery through integrated mentorship and quality improvement at health centers in rural Rwanda

BMC Health Services Research, 2018
Background Inadequate antenatal care (ANC) can lead to missed diagnosis of danger signs or delayed referral to emergency obstetrical care, contributing to maternal mortality.
Anatole Manzi, Laetitia Nyirazinyoye, Joseph Ntaganira, Hema Magge, Evariste Bigirimana, Leoncie Mukanzabikeshimana, Lisa R. Hirschhorn, Bethany Hedt-Gauthier   +7 more
doaj   +1 more source

Broadband Communications for High Speed Trains via NDN Wireless Mesh Network [PDF]

Tsinghua Science and Technology, 2017
With increasing investment and deployment of high speed trains (HST), a critical demand of high bandwidth and the better user experience under high speed mobility arises. Although, TCP/IP has been studied in a static, walking and lower mobility environment, it can not work well at high speed (>50 m/s) cases.
Fan Wu 0014, Yang Wang, Runtong Chen, Xinfang Xie   +3 more
openaire   +1 more source

Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy

BMC Bioinformatics, 2009
Background Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use
Hakenberg Jörg, Gandon Fabien, Doms Andreas, Dietze Heiko, Andreopoulos Bill, Alexopoulou Dimitra, Khelif Khaled, Schroeder Michael, Wächter Thomas   +8 more
doaj   +1 more source

Training transformers for mesh-based simulations

Neural Computing and Applications
Abstract Simulating physics using Graph Neural Networks (GNNs) is predominantly driven by message-passing architectures, which face challenges in scaling and efficiency, particularly in handling large, complex meshes. These architectures have inspired numerous enhancements, including multigrid approaches and
Paul Garnier, Vincent Lannelongue, Jonathan Viquerat, Elie Hachem   +3 more
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Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes

AIAA SCITECH 2023 Forum, 2023
Mesh-based numerical solvers are an important part in many design tool chains. However, accurate simulations like computational fluid dynamics are time and resource consuming which is why surrogate models are employed to speed-up the solution process. Machine Learning based surrogate models on the other hand are fast in predicting approximate solutions
Sebastian Strönisch, Maximilian Sander, Andreas Knüpfer, Marcus Meyer   +3 more
openaire   +2 more sources

Mesh-Selecting for Computational Efficient PA Behavioral Modeling and DPD Linearization

, 2021
This letter proposes a mesh-selecting (MeS) method for complex-valued signals oriented at significantly reducing the training data required to extract the parameters of mathematical models for characterizing the nonlinear behavior of power amplifiers or ...
Gilabert Pinal, Pere Lluís, Wang, Teng
core   +1 more source