首页|Studies from Technical University of Denmark (DTU) in the Area of Machine Learning Described (Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion ...)
Studies from Technical University of Denmark (DTU) in the Area of Machine Learning Described (Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion ...)
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New research on artificial intelligence is the subject of a new report. According to news reporting originating from Kongens Lyngby, Denmark, by NewsRx correspondents, research stated, “With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data.” Financial supporters for this research include Technical University of Denmark Discovery Grant. Our news journalists obtained a quote from the research from Technical University of Denmark (DTU): “Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis.”
Technical University of Denmark (DTU)Kongens LyngbyDenmarkEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mdpi
