scholarly journals Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

2021 ◽  
Author(s):  
Kai Fukami ◽  
Romit Maulik ◽  
Nesar Ramachandra ◽  
Koji Fukagata ◽  
Kunihiko Taira
Keyword(s):  
Deep Learning ◽  
Voronoi Tessellation ◽  
Global Field ◽  
Field Reconstruction

scholarly journals Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 978
Author(s):  
Olusola Oluwakemi Abayomi-Alli ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas ◽  
Sanjay Misra
Keyword(s):  
Deep Learning ◽  
Facial Palsy ◽  
Data Augmentation ◽  
Voronoi Tessellation ◽  
Class Imbalance ◽  
Misclassification Rate ◽  
Voronoi Cells ◽  
Face Images ◽  
Functional Consequences ◽  
Voronoi Decomposition

Face palsy has adverse effects on the appearance of a person and has negative social and functional consequences on the patient. Deep learning methods can improve face palsy detection rate, but their efficiency is limited by insufficient data, class imbalance, and high misclassification rate. To alleviate the lack of data and improve the performance of deep learning models for palsy face detection, data augmentation methods can be used. In this paper, we propose a novel Voronoi decomposition-based random region erasing (VDRRE) image augmentation method consisting of partitioning images into randomly defined Voronoi cells as an alternative to rectangular based random erasing method. The proposed method augments the image dataset with new images, which are used to train the deep neural network. We achieved an accuracy of 99.34% using two-shot learning with VDRRE augmentation on palsy faces from Youtube Face Palsy (YFP) dataset, while normal faces are taken from Caltech Face Database. Our model shows an improvement over state-of-the-art methods in the detection of facial palsy from a small dataset of face images.

A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS

2021 ◽  
Vol 4 (1) ◽  
pp. 27
Author(s):  
José Pablo Quesada-Molina ◽  
Stefano Mariani
Keyword(s):  
Deep Learning ◽  
Voronoi Tessellation ◽  
Characteristic Size ◽  
Micro Electro Mechanical Systems ◽  
Polysilicon Film ◽  
Polysilicon Films ◽  
Chip Testing ◽  
On Chip ◽  
Micromechanical Characterization ◽  
Local Stiffness

The path towards miniaturization for micro-electro-mechanical systems (MEMS) has recently increased the effects of stochastic variability at the (sub)micron scale on the overall performance of the devices. We recently proposed and designed an on-chip testing device to characterize two sources of variability that majorly affect the scattering in response to the external actions of inertial (statically determinate) micromachines: the morphology of the polysilicon film constituting the movable parts of the device, and the environment-affected over-etch linked to the microfabrication process. A fully stochastic model of the entire device has been set to account for these two sources on the measurable response of the devices, e.g., in terms of the relevant C-V curves up to pull-in. A complexity in the mentioned model is represented by the need to assess the stochastic (local) stiffness of polysilicon, depending on its unknown (local) microstructure. In this work, we discuss a deep learning approach to the micromechanical characterization of polysilicon films, based on densely connected neural networks (NNs). Such NNs extract relevant features of the polysilicon morphology from SEM-like Voronoi tessellation-based digital microstructures. The NN-based model or surrogate is shown to correctly catch size effects at a varying ratio between the characteristic size of the structural components of the device, and the morphology-induced length scale of the aggregate of silicon grains. This property of the model looks to indeed be necessary to prove the generalization capability of the learning process, and to next feed Monte Carlo simulations resting on the model of the entire device.

scholarly journals Deep Learning for Nanofluid Field Reconstruction in Experimental Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64692-64706
Author(s):  
Tianyuan Liu ◽  
Yunzhu Li ◽  
Yonghui Xie ◽  
Di Zhang
Keyword(s):  
Deep Learning ◽  
Experimental Analysis ◽  
Field Reconstruction

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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