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Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine

Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning mac...

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Detalles Bibliográficos
Autores principales: Lin, Haiqi, He, Xing, Wang, Shuai, Yang, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200106/
https://www.ncbi.nlm.nih.gov/pubmed/34199788
http://dx.doi.org/10.3390/s21113877
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author Lin, Haiqi
He, Xing
Wang, Shuai
Yang, Ping
author_facet Lin, Haiqi
He, Xing
Wang, Shuai
Yang, Ping
author_sort Lin, Haiqi
collection PubMed
description Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning machine method is proposed to realize high precision wavefront restoration under dynamic non-uniform intensity distribution. The simulation results show that this method has better accuracy of wavefront restoration than the classical modal algorithm under dynamic non-uniform intensity distribution. The root mean square error of the residual wavefront for the proposed method is only 2.9% of the initial value.
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spelling pubmed-82001062021-06-14 Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine Lin, Haiqi He, Xing Wang, Shuai Yang, Ping Sensors (Basel) Communication Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning machine method is proposed to realize high precision wavefront restoration under dynamic non-uniform intensity distribution. The simulation results show that this method has better accuracy of wavefront restoration than the classical modal algorithm under dynamic non-uniform intensity distribution. The root mean square error of the residual wavefront for the proposed method is only 2.9% of the initial value. MDPI 2021-06-04 /pmc/articles/PMC8200106/ /pubmed/34199788 http://dx.doi.org/10.3390/s21113877 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Lin, Haiqi
He, Xing
Wang, Shuai
Yang, Ping
Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title_full Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title_fullStr Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title_full_unstemmed Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title_short Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine
title_sort wavefront restoration technology of dynamic non-uniform intensity distribution based on extreme learning machine
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200106/
https://www.ncbi.nlm.nih.gov/pubmed/34199788
http://dx.doi.org/10.3390/s21113877
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