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On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models
Blachnik, M.; Przyłucki, R.; Golak, S.; Ściegienka, P.; Wieczorek, T. On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models. Sensors2023, 23, 6806.
Blachnik, M.; Przyłucki, R.; Golak, S.; Ściegienka, P.; Wieczorek, T. On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models. Sensors 2023, 23, 6806.
Blachnik, M.; Przyłucki, R.; Golak, S.; Ściegienka, P.; Wieczorek, T. On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models. Sensors2023, 23, 6806.
Blachnik, M.; Przyłucki, R.; Golak, S.; Ściegienka, P.; Wieczorek, T. On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models. Sensors 2023, 23, 6806.
Abstract
Scanning underwater areas, using magnetometers, in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a set enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To meet this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance of the numerical model with the results of physical tests. In addition, the paper discusses the methodology of simplifying the computational model, allowing for almost three times reduction of the calculation time. In addition, in the work we present the methodology of creating an appropriate data set, enabling the generation of any number of training samples.
Keywords
digital twin; UXO; unexploded ordnance; training data set; magnetic field; FEM; magnetometer; Finite element method; AUV
Subject
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
