A multi-feature fusion transfer learning method for displacement prediction of rainfall reservoir-induced landslide with step-like deformation characteristics

2021 ◽  
pp. 106494
Author(s):  
Jingjing Long ◽  
Changdong Li ◽  
Yong Liu ◽  
Pengfei Feng ◽  
Qingjun Zuo
Keyword(s):  
Transfer Learning ◽  
Feature Fusion ◽  
Deformation Characteristics ◽  
Learning Method ◽  
Displacement Prediction ◽  
Fusion Transfer

scholarly journals A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method

2021 ◽  
Vol 11 (3) ◽  
pp. 997
Author(s):  
Jiaping Li ◽  
Wai Lun Lo ◽  
Hong Fu ◽  
Henry Shu Hung Chung
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Feature Fusion ◽  
Extraction Methods ◽  
Features Extraction ◽  
Image Feature ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Learning Method ◽  
Visibility Estimation

Meteorological visibility is an important meteorological observation indicator to measure the weather transparency which is important for the transport safety. It is a challenging problem to estimate the visibilities accurately from the image characteristics. This paper proposes a transfer learning method for the meteorological visibility estimation based on image feature fusion. Different from the existing methods, the proposed method estimates the visibility based on the data processing and features’ extraction in the selected subregions of the whole image and therefore it had less computation load and higher efficiency. All the database images were gray-averaged firstly for the selection of effective subregions and features extraction. Effective subregions are extracted for static landmark objects which can provide useful information for visibility estimation. Four different feature extraction methods (Densest, ResNet50, Vgg16, and Vgg19) were used for the feature extraction of the subregions. The features extracted by the neural network were then imported into the proposed support vector regression (SVR) regression model, which derives the estimated visibilities of the subregions. Finally, based on the weight fusion of the visibility estimates from the subregion models, an overall comprehensive visibility was estimated for the whole image. Experimental results show that the visibility estimation accuracy is more than 90%. This method can estimate the visibility of the image, with high robustness and effectiveness.

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

A multi-task transfer learning method with dictionary learning

2020 ◽  
Vol 191 ◽  
pp. 105233 ◽  
Author(s):  
Xin Zheng ◽  
Luyue Lin ◽  
Bo Liu ◽  
Yanshan Xiao ◽  
Xiaoming Xiong
Keyword(s):  
Transfer Learning ◽  
Dictionary Learning ◽  
Learning Method ◽  
Task Transfer
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