Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks

2020 ◽  
Vol 60 (10) ◽  
pp. 4457-4473
Author(s):  
Alex D. Casey ◽  
Steven F. Son ◽  
Ilias Bilionis ◽  
Brian C. Barnes
Keyword(s):  
Neural Networks ◽  
Electronic Structure ◽  
Convolutional Neural Networks ◽  
Material Properties ◽  
Energetic Material

scholarly journals Machine learning material properties from the periodic table using convolutional neural networks

2018 ◽  
Vol 9 (44) ◽  
pp. 8426-8432 ◽  
Author(s):  
Xiaolong Zheng ◽  
Peng Zheng ◽  
Rui-Zhi Zhang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Enthalpy Of Formation ◽  
Convolutional Neural Networks ◽  
Material Properties ◽  
Periodic Table ◽  
Chemical Information ◽  
Learning Material ◽  
The Enthalpy Of Formation

Convolutional neural networks directly learned chemical information from the periodic table to predict the enthalpy of formation and compound stability.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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