Differential morphological profile neural network for maneuverability hazard detection in unmanned aerial system imagery

2021 ◽  
Author(s):  
James Alex Hurt ◽  
Grant Scott ◽  
David Huangal ◽  
Jeffrey Dale ◽  
Trevor M. Bajkowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial System ◽  
Hazard Detection

Metadata enabled contextual sensor fusion for unmanned aerial system-based explosive hazard detection

2021 ◽  
Author(s):  
Matthew Deardorff ◽  
Brendan Alvey ◽  
Derek T. Anderson ◽  
James M. Keller ◽  
Grant Scott ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Unmanned Aerial System ◽  
Hazard Detection

Improving explosive hazard detection with simulated and augmented data for an unmanned aerial system

2021 ◽  
Author(s):  
Brendan J. Alvey ◽  
Derek T. Anderson ◽  
James M. Keller ◽  
Andrew Buck ◽  
Grant J. Scott ◽  
...  
Keyword(s):  
Unmanned Aerial System ◽  
Hazard Detection

Semi-Supervised Learning and Deep Neural Network on Detection of Roadway Cracking Using Unmanned Aerial System Imagery

2021 ◽  
Author(s):  
Long Ngo Hoang Truong ◽  
Edward Clay ◽  
Omar E. Mora ◽  
Wen Cheng ◽  
Maninder Kaur ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Deep Neural Network ◽  
Unmanned Aerial System

scholarly journals Adaptive and Resilient Flight Control System for a Small Unmanned Aerial System

2013 ◽  
Vol 2013 ◽  
pp. 1-25 ◽  
Author(s):  
Gonzalo Garcia ◽  
Shahriar Keshmiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Parameter Identification ◽  
Real Time ◽  
Flight Control ◽  
Unmanned Aerial System ◽  
Flight Control System ◽  
Network Parameter ◽  
Artificial Neural

The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainties. The fault tolerant inner loop is enhanced by an adaptive real-time artificial neural network parameter identification to monitor important changes in the aircraft’s dynamics due to nonlinear and unsteady aerodynamics. The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. Numerically estimated stability and control derivatives are obtained by delta-based methods. New nonlinear guidance logic, stable in Lyapunov sense, is developed to guide the aircraft. The designed flight control system has better performance compared to a commercial off-the-shelf autopilot system in guiding and controlling an unmanned air system during a trajectory following.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document