scholarly journals Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 924 ◽  
Author(s):  
Duona Zhang ◽  
Wenrui Ding ◽  
Baochang Zhang ◽  
Chunyu Xie ◽  
Hongguang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Aerial Vehicles

scholarly journals A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

2021 ◽  
Vol 13 ◽  
pp. 175682932110168
Author(s):  
Hasan Karali ◽  
Gokhan Inalhan ◽  
M Umut Demirezen ◽  
M Adil Yukselen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Computationally Intensive ◽  
Lifting Line ◽  
Lifting Line Method ◽  
Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

scholarly journals Urban Natural Environment Analysis through Deep Learning Techniques on Automatically Acquired Images from Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Marco A Moreno-Armendáriz ◽  
Hiram Calvo ◽  
Carlos A Duchanoy ◽  
Anayantzin P López-Juárez ◽  
Israel A Vargas-Monroy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Natural Environment ◽  
Environment Analysis ◽  
Aerial Vehicles ◽  
Learning Techniques
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