Aerospace and Electronic Systems - August 2018 - 3

ARTIFICIAL NEURAL NETWORKS FOR IMPACT FORCE
RECONSTRUCTION ON COMPOSITE PLATES AND RELEVANT
UNCERTAINTY PROPAGATION
Impacts are one of the main causes of damage in composite panels.
The determination of the impact location and the reconstruction of impact force are necessary to evaluate the health of a structure. These
data may be measured indirectly from the measurements of responses
of sensors located on the system subjected to the impact. In this study,
a composite panel model developed in Abaqus/CAE is first validated
and then numerical simulations based on the model are used to obtain
data for several impacts, characterized by different impact locations,
different impactor velocities and masses. Subsequently, these data are
used to model the complex nonlinear behavior of the composite laminate by a nonlinear system identification approach. This is based on
the use of artificial neural networks, which are employed to reconstruct the impact forces and the impact locations. Finally, an analysis
of uncertainty propagation of one of the employed neural networks is
carried out.

A REMOTE-CONTROLLED PLATFORM FOR UAS TESTING
In this article, a new version of "DronesBench" system, designed for
full remote controlling of UAS testing over the Internet is presented.
For assessing the correct behavior of the remote system, several tests
have been performed by considering a client connected with a computer, located at the Military University of Technology from Poland
(WAT), and a client connected to the server with a smartphone. The
server is located at the L.E.S.I.M. laboratory, University of Sannio,
Italy. This version of the "DronesBench" system has been tested in
real conditions by using two UASs (quadrotor and hexarotor type) and
preliminary results show that the system is capable to detect and identify UASs' faults.

UAV SYSTEM FOR PHOTOVOLTAIC PLANTS INSPECTION
New systems, based on the use of unmanned aerial vehicles (UAVs),
have been developed to substitute for human workers in specific
jobs, like inspecting photovoltaic (PV) plants to reduce maintenance
costs and intervention times. UAVs allow the detection of defects in
PV plants through the fusion of computer vision algorithms and high
accuracy Global Navigation Satellite System (GNSS) positioning
techniques. Particularly, UAVs used together with GNSS positioning
techniques allow PV plant operators to receive reliable information on
defects, including the ability to detect, locate, and tag anomalies for
the diagnosis of PV systems. The goal of the authors of this article is to
describe some critical aspects of direct georeferencing, image acquisition, panel recognition, and faulty module detection in the specific
case of PV plant inspection.

AUGUST 2018

IEEE A&E SYSTEMS MAGAZINE

3

Aerospace and Electronic Systems - August 2018

Table of Contents for the Digital Edition of Aerospace and Electronic Systems - August 2018

Contents
Aerospace and Electronic Systems - August 2018 - Cover1
Aerospace and Electronic Systems - August 2018 - Cover2
Aerospace and Electronic Systems - August 2018 - Contents
Aerospace and Electronic Systems - August 2018 - 2
Aerospace and Electronic Systems - August 2018 - 3
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Aerospace and Electronic Systems - August 2018 - Cover3
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