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A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers

Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The se...

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Autores principales: Elshalakani, Mohamed, Muthuramalingam, Muthukumar, Bruecker, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349333/
https://www.ncbi.nlm.nih.gov/pubmed/32580301
http://dx.doi.org/10.3390/s20123522
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author Elshalakani, Mohamed
Muthuramalingam, Muthukumar
Bruecker, Christoph
author_facet Elshalakani, Mohamed
Muthuramalingam, Muthukumar
Bruecker, Christoph
author_sort Elshalakani, Mohamed
collection PubMed
description Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The sensor is composed of a bundle of spatially-distributed optical fibers that act as artificial seal-like whiskers and interact with the body’s wake in the form of time-variant (bending) deflections. Supervised learning is employed to relate the vibrations of the artificial whiskers to the position of an upstream cylinder. The labeled training data are prepared based on the processing and reduction of the recorded bending responses of the artificial whiskers while the cylinder is placed at various locations. An iterative training algorithm is performed on two neural-network models while using the 10-fold cross-validation technique. The models are able to predict the coordinates of the cylinder in the two-dimensional (2D) space with a high degree of accuracy. The current implementation of the sensor can passively sense the wake generated by the cylinder at Re ≃ 6000 and estimate its position with an average error smaller than the characteristic diameter D of the cylinder and for inter-distances (in the water tunnel) up to 25-times D.
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spelling pubmed-73493332020-07-22 A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers Elshalakani, Mohamed Muthuramalingam, Muthukumar Bruecker, Christoph Sensors (Basel) Article Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The sensor is composed of a bundle of spatially-distributed optical fibers that act as artificial seal-like whiskers and interact with the body’s wake in the form of time-variant (bending) deflections. Supervised learning is employed to relate the vibrations of the artificial whiskers to the position of an upstream cylinder. The labeled training data are prepared based on the processing and reduction of the recorded bending responses of the artificial whiskers while the cylinder is placed at various locations. An iterative training algorithm is performed on two neural-network models while using the 10-fold cross-validation technique. The models are able to predict the coordinates of the cylinder in the two-dimensional (2D) space with a high degree of accuracy. The current implementation of the sensor can passively sense the wake generated by the cylinder at Re ≃ 6000 and estimate its position with an average error smaller than the characteristic diameter D of the cylinder and for inter-distances (in the water tunnel) up to 25-times D. MDPI 2020-06-22 /pmc/articles/PMC7349333/ /pubmed/32580301 http://dx.doi.org/10.3390/s20123522 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Elshalakani, Mohamed
Muthuramalingam, Muthukumar
Bruecker, Christoph
A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title_full A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title_fullStr A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title_full_unstemmed A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title_short A Deep-Learning Model for Underwater Position Sensing of a Wake’s Source Using Artificial Seal Whiskers
title_sort deep-learning model for underwater position sensing of a wake’s source using artificial seal whiskers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349333/
https://www.ncbi.nlm.nih.gov/pubmed/32580301
http://dx.doi.org/10.3390/s20123522
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