Geosmin and 2-methylisoborneol adsorption using different carbon materials: Isotherm, kinetic, multiple linear regression, and deep neural network modeling using a real drinking water source

2021 ◽  
pp. 127967
Author(s):  
Taeho Bong ◽  
Jin-Kyu Kang ◽  
Viviane Yargeau ◽  
Hye-Lim Nam ◽  
Sang-Hyup Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Drinking Water ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Network Modeling ◽  
Water Source ◽  
Neural Network Modeling ◽  
Drinking Water Source

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

scholarly journals I-vector features and deep neural network modeling for language recognition

2019 ◽  
Vol 147 ◽  
pp. 36-43 ◽  
Author(s):  
Wei Wang ◽  
Wenjie Song ◽  
Chen Chen ◽  
Zhaoxin Zhang ◽  
Yi Xin
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Language Recognition

scholarly journals Deep Neural Network Modeling of Multiple Oxide/Nitride Deposited Dielectric Films for 3D-NAND Flash

2020 ◽  
Vol 29 (6) ◽  
pp. 190-194
Author(s):  
Jeong Eun Choi ◽  
Jungho Song ◽  
Yong Ho Lee ◽  
Sang Jeen Hong
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Dielectric Films ◽  
Neural Network Modeling ◽  
Nand Flash
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