Alternative Approach to Chemical Accuracy: A Neural Networks-Based First-Principles Method for Heat of Formation of Molecules Made of H, C, N, O, F, S, and Cl

2014 ◽  
Vol 118 (39) ◽  
pp. 9120-9131 ◽  
Author(s):  
Jian Sun ◽  
Jiang Wu ◽  
Tao Song ◽  
LiHong Hu ◽  
KaiLu Shan ◽  
...  
Keyword(s):  
Neural Networks ◽  
First Principles ◽  
Heat Of Formation ◽  
Alternative Approach ◽  
First Principles Method

Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

2018 ◽  
Vol 148 (24) ◽  
pp. 241738 ◽  
Author(s):  
GuanYa Yang ◽  
Jiang Wu ◽  
ShuGuang Chen ◽  
WeiJun Zhou ◽  
Jian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
First Principles ◽  
Heat Of Formation ◽  
Accurate Prediction ◽  
First Principles Method

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.

Integrating Neural Networks with First Principles Models for Dynamic Modeling

1992 ◽  
Vol 25 (5) ◽  
pp. 327-332 ◽  
Author(s):  
Hong-Te Su ◽  
N. Bhat ◽  
P.A. Minderman ◽  
T.J. McAvoy
Keyword(s):  
Neural Networks ◽  
Dynamic Modeling ◽  
First Principles

High catalytic activity for CO oxidation on single Fe atom stabilized in graphene vacancies

RSC Advances ◽  
2016 ◽  
Vol 6 (96) ◽  
pp. 93985-93996 ◽  
Author(s):  
Yanan Tang ◽  
Jincheng Zhou ◽  
Zigang Shen ◽  
Weiguang Chen ◽  
Chenggang Li ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Co Oxidation ◽  
First Principles ◽  
High Catalytic Activity ◽  
First Principles Method

The geometric, electronic and catalytic characters of Fe atom embedded graphene (including monovacancy and divacancy) are investigated using the first-principles method, which gives a reference on designing graphene-based catalysts for CO oxidation.

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