Image-based thickener mud layer height prediction with attention mechanism-based CNN

2021 ◽  
Author(s):  
Chenyu Fang ◽  
Dakuo He ◽  
Kang Li ◽  
Yan Liu ◽  
Fuli Wang
Keyword(s):  
Attention Mechanism ◽  
Layer Height ◽  
Height Prediction

An Attention Mechanism Extension of Automatic HTML Generation from Web Page Design Images

2020 ◽  
Vol 140 (12) ◽  
pp. 1393-1401
Author(s):  
Hiroki Chinen ◽  
Hidehiro Ohki ◽  
Keiji Gyohten ◽  
Toshiya Takami
Keyword(s):  
Attention Mechanism ◽  
Web Page ◽  
Web Page Design ◽  
Page Design

STUDY OF THE EFFECT OF AIR CONSUMPTION, LIQUID LAYER HEIGHT AND TEMPERATURE ON THE PROCESS OF FLOTATION SEPARATION OF GROUND PLASTICS

2020 ◽  
Vol 3 (441) ◽  
pp. 36-43
Author(s):  
А. Leudanski ◽  
◽  
Y. Apimakh ◽  
А. Volnenko ◽  
D. Zhumadullayev ◽  
...  
Keyword(s):  
Liquid Layer ◽  
Layer Height

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

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