Three-Dimensional Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.1c00504
Abstract: Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques such as three-dimensional (3D)-convolutional neural networks become a natural choice to associate a molecular property… read more here.
Keywords: neural networks; molecular topological; convolutional neural; dimensional convolutional ... See more keywords
Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.14.024522
Abstract: Abstract. Although there are many state-of-the-art methods for hyperspectral classification, data deficiency is a problem that should be addressed before popularizing hyperspectral technology. To solve this problem, it is worth exploring methods based on small… read more here.
Keywords: semisupervised hyperspectral; adversarial autoencoder; dimensional convolutional; three dimensional ... See more keywords
A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network
Sign Up to like & getrecommendations! Published in 2020 at "Shock and Vibration"
DOI: 10.1155/2020/1850286
Abstract: This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The… read more here.
Keywords: methodology; network; diagnosis; svd one ... See more keywords