A Novel Framework Based on Deep Learning and Unmanned Aerial Vehicles to Assess the Quality of Rice Fields

2016 ◽  
pp. 84-93 ◽  
Author(s):  
Nguyen Cao Tri ◽  
Tran Van Hoai ◽  
Hieu N. Duong ◽  
Nguyen Thanh Trong ◽  
Vo Van Vinh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Rice Fields ◽  
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 Active stabilization of unmanned aerial vehicle imaging platform

2020 ◽  
Vol 26 (19-20) ◽  
pp. 1791-1803 ◽  
Author(s):  
Mohit Verma ◽  
Vicente Lafarga ◽  
Mael Baron ◽  
Christophe Collette
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Vibration Isolation ◽  
Isolation System ◽  
Aerial Vehicles ◽  
Active Stabilization ◽  
Aerial Vehicle ◽  
Active Vibration ◽  
Improved Performance

The advancement in technology has seen a rapid increase in the use of unmanned aerial vehicles for various applications. These unmanned aerial vehicles are often equipped with the imaging platform like a camera. During the unmanned aerial vehicle flight, the camera is subjected to vibrations which hamper the quality of the captured images/videos. The high-frequency vibrations from the unmanned aerial vehicle are transmitted to the camera. Conventionally, passive rubber mounts are used to isolate the camera from the drone vibrations. The passive mounts are able to provide reduction in response near the resonance. However, this comes at the cost of amplification of response at the higher frequency. This article proposes an active vibration isolation system which exhibits improved performance at the higher frequencies than the conventional system. The active isolation system consists of a contact-less voice coil actuator supported by four springs. Experiments are carried out to study the effect of vibrations on the quality of images captured. The characterization of drone vibrations is also carried out by recording the acceleration during different flight modes. The performance of the proposed isolation system is experimentally validated on a real drone camera subjected to the recorded drone acceleration spectrum. The isolation system is found to perform better than the conventional rubber mounts and is able to reduce the vibrations to a factor of one-fourth. It can be effectively used to improve the image acquisition quality of the unmanned aerial vehicles.

scholarly journals Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3160 ◽  
Author(s):  
Kirshna Kumar ◽  
Sushil Kumar ◽  
Omprakash Kaiwartya ◽  
Ajay Sikandar ◽  
Rupak Kharel ◽  
...  
Keyword(s):  
Ad Hoc Networks ◽  
Unmanned Aerial Vehicles ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Qos Provisioning ◽  
Smart Services ◽  
Aerial Vehicles ◽  
Service Oriented ◽  
Hoc Networks

Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

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 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

scholarly journals Indicators for the Quality of Funds for Radioelectronic Countermeasure of Unmanned Aerial Vehicles

2019 ◽  
Vol LVI (1) ◽  
Author(s):  
Nikolay Gueorguiev ◽  
Aleksandar Kolarov ◽  
Vencislav Pehlivanski ◽  
Ognian Todorov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles
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