scholarly journals SOLUTION OF THE PROBLEM OF OPTIMUM CONTROL OF THE VERTICAL LIFT OF THE PROBE ROCKET WITH REFINING THE AERODYNAMIC RESISTANCE MODEL

2021 ◽  
pp. 46-50
Author(s):  
T.Yu. Mozzhorina ◽  
A.C. Popov
Keyword(s):  
Aerodynamic Resistance ◽  
Optimum Control ◽  
Resistance Model ◽  
Vertical Lift

Kajian Kinerja Kapal Bantu Hidro-Oceanography

2019 ◽  
Vol 4 (1) ◽  
pp. 35
Author(s):  
Meitha Soetardjo ◽  
Dian Purnamasari
Keyword(s):  
Preliminary Design ◽  
Resistance Model ◽  
Supporting Force

Bantu Hidro-Oceanography (BHO) adalah salah satu satuan bantu dalam bidang survei Hidro-Oceanography yang termasuk dalam susunan tempur pendukung (Supporting Force). Untuk kepentingan tersebut maka preliminary design kapal Hidro-Oceanography dilaksanakan sebagai langkah awal dalam perencanaan dan pengadaan kapal Hidro-Oceanography untuk mengkaji karakteristik umum desain yaitu hidrostatik dan stabilitas sehingga aspek kelayakan kapal dapat terpenuhi. Analisa hasil kajian kinerja kapal Hidro-Oceanography dipersiapkan untuk pengujian tahanan/resistance model kapal.Keywords : Preliminary design1, Hydrostatic2, Stability3

OPTIMUM CONTROL OF SPECIALIZED ELECTRIC DRIVE

2020 ◽  
Author(s):  
A. M.  Litvinenko ◽  
◽  
D. S.  Baranov ◽  
A. E.  Novikov ◽  
◽  
...  
Keyword(s):  
Electric Drive ◽  
Optimum Control

Assessment of Cabin Dimensions to Accommodate Infantry Soldiers for the Future Vertical Lift/Joint Multi-Role Medium-Class Aircraft

2014 ◽  
Author(s):  
David B. Durbin ◽  
Jim A. Faughn ◽  
Richard W. Kozycki ◽  
Jamison S. Hicks ◽  
Ron L. Carty
Keyword(s):  
The Future ◽  
Vertical Lift

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

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