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The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks
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class="card"> <div class="card-body"><strong>Paper Count:</strong> 87408</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrien%20Marque">Adrien Marque</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Delahaye"> Daniel Delahaye</a>, <a href="https://publications.waset.org/abstracts/search?q=Pierre%20Mar%C3%A9chal"> Pierre Mar茅chal</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabelle%20Berry"> Isabelle Berry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and a specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty. <iframe src="https://publications.waset.org/abstracts/188367.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20direction" title="wind direction">wind direction</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20level" title=" uncertainty level"> uncertainty level</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicle" title=" unmanned aerial vehicle"> unmanned aerial vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title=" convolution neural network"> convolution neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=SPD%20matrices" title=" SPD matrices"> SPD matrices</a> </p> <a href="https://publications.waset.org/abstracts/188367/the-uav-feasibility-trajectory-prediction-using-convolution-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">50</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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