Results 81 to 90 of about 1,925,096 (328)

Isointense infant brain MRI segmentation with a dilated convolutional neural network [PDF]

, 2017
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D ...
Moeskops, Pim, Pluim, Josien P. W.
core   +2 more sources

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network [PDF]

North American Chapter of the Association for Computational Linguistics, 2017
In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional ...
D. Q. Nguyen, T. Nguyen, Dat Quoc Nguyen, Dinh Q. Phung   +3 more
semanticscholar   +1 more source

Using In Situ TEM to Understand the Surfaces of Electrocatalysts at Reaction Conditions: Single‐Atoms to Nanoparticles

Advanced Functional Materials, EarlyView.
This review summarizes recent advances in closed‐cell in situ TEM strategies for accurate determination of the activity and stability of single‐atom catalyst systems during operation. Operando conditions causing dynamic changes of SAC systems are highlighted and we explain why ensemble average‐based optical techniques may benefit from the technological
Martin Ek, Soshan Cheong, Julie Cairney, Richard D. Tilley, Ingemar Persson   +4 more
wiley   +1 more source

Forest fire image recognition based on convolutional neural network

Journal of Algorithms & Computational Technology, 2019
In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms.
Yuanbin Wang, Langfei Dang, Jieying Ren
doaj   +1 more source

Performance of a Convolutional Neural Network-Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection

Turkish Journal of Orthodontics, 2022
Objective: The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically enabling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used ...
Mehmet Uğurlu
doaj   +1 more source

Enhanced CNN for image denoising

, 2019
Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train.
Fei, Lunke, Luo, Nan, Tian, Chunwei, Wang, Junqian, Wen, Jie, Xu, Yong   +5 more
core   +1 more source

Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders [PDF]

, 2021
Romit Maulik, Bethany Lusch, Prasanna Balaprakash   +2 more
openalex   +1 more source

Symplectic convolutional neural networks

We propose a new symplectic convolutional neural network (CNN) architecture by leveraging symplectic neural networks, proper symplectic decomposition, and tensor techniques. Specifically, we first introduce a mathematically equivalent form of the convolution layer and then, using symplectic neural networks, we demonstrate a way to parameterize the ...
Yildiz, S., Janik, K., Benner, P. ; https://orcid.org/0000-0003-3362-4103   +2 more
openaire   +3 more sources

Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing

Advanced Functional Materials, EarlyView.
A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim, Taewi Kim, Jieun Park, Insic Hong, Myungrae Hong, Seonggyeong Park, Beomjin Kim, Sehyuk Ahn, Siwoo Jang, Seungyong Han, Je‐Sung Koh, Daeshik Kang   +11 more
wiley   +1 more source

A Frequency-Domain Convolutional Neural Network Architecture Based on the Frequency-Domain Randomized Offset Rectified Linear Unit and Frequency-Domain Chunk Max Pooling Method

IEEE Access, 2020
It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain.
Jinhua Lin, Lin Ma, Jingxia Cui
doaj   +1 more source