Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography

Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify vibrating sources. This article aims to compare and implement various regularization methods in the context of NAH. Specifically, it compares commonly used Tikhonov regular...

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Autores:
Martinod, T.
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/34775
Acceso en línea:
https://hdl.handle.net/10784/34775
Palabra clave:
Holografía Acústica en el Campo Cercano
Problemas Mal Planteados
Regularización
Near-field Acoustic Holography
Posed problems
Regularization
Rights
License
Acceso abierto
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spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2024-11-06T21:43:22Z20242024-11-06T21:43:22Zhttps://hdl.handle.net/10784/34775Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify vibrating sources. This article aims to compare and implement various regularization methods in the context of NAH. Specifically, it compares commonly used Tikhonov regularization, sparsity-based regularization, and neural networks (NN) regularization for a planar NAH array with measurements obtained from an experimental setup. Additionally, it theoretically introduces Green’s function-based regularization. The first three types of regularization methods yield images consistent with the results, and statistical indicators are used to determine which method performs best at different frequencies.La regularización del problema inverso es un tema complejo cuando se utilizan técnicas de Holografía Acústica en el Campo Cercano (NAH) para identificar fuentes vibratorias. Este artículo tiene como objetivo comparar e implementar varios métodos de regularización en el contexto de NAH. Específicamente, se comparan la regularización de Tikhonov, la regularización basada en la escasez y la regularización mediante redes neuronales (NN) para un arreglo NAH plano con mediciones obtenidas de un montaje experimental. Además, se introduce teóricamente la regularización basada en la función de Green. Los primeros tres tipos de métodos de regularización producen imágenes consistentes, y se utilizan indicadores estadísticos para determinar qué método tiene el mejor rendimiento a diferentes frecuencias.application/pdfengSound-Based Imaging Regularization Approaches in Near-field Acoustic HolographyImágenes Basadas en Sonido: Enfoques de Regularización en Holografía Acústica en el Campo CercanoarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Acceso abiertohttp://purl.org/coar/access_right/c_abf2Holografía Acústica en el Campo CercanoProblemas Mal PlanteadosRegularizaciónNear-field Acoustic HolographyPosed problemsRegularizationMartinod, T.ae1a3589-6291-4c5e-9b4f-185bcf7fe7b2-1Universidad EAFITCuadernos de Ingeniería Matemática4ORIGINALSound-Based Imaging Regularization Approaches in Near-field Acoustic Holography.pdfArtículo Principalapplication/pdf738454https://repository.eafit.edu.co/bitstreams/0066b2fe-b7ec-4b94-9909-1882ad42ae60/downloadc5578a0b42bf0f27a5f41585a672ec6cMD51LICENSELicense.txtLicensetext/plain2584https://repository.eafit.edu.co/bitstreams/aa06119d-7ea5-4734-afd8-cf645799552b/downloaddb71ab3fdf552b62aa0a746cef2840e4MD52THUMBNAILPortada.pngPortadaimage/png1747211https://repository.eafit.edu.co/bitstreams/074b044e-0c4d-45c1-a00e-c9f8b3f2dfd3/download5bc3017473813e875879dcd10727c474MD5310784/34775oai:repository.eafit.edu.co:10784/347752024-12-04 11:49:59.506open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
dc.title.spa.fl_str_mv Imágenes Basadas en Sonido: Enfoques de Regularización en Holografía Acústica en el Campo Cercano
title Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
spellingShingle Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
Holografía Acústica en el Campo Cercano
Problemas Mal Planteados
Regularización
Near-field Acoustic Holography
Posed problems
Regularization
title_short Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
title_full Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
title_fullStr Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
title_full_unstemmed Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
title_sort Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography
dc.creator.fl_str_mv Martinod, T.
dc.contributor.author.none.fl_str_mv Martinod, T.
dc.contributor.affiliation.spa.fl_str_mv Universidad EAFIT
dc.subject.keyword.eng.fl_str_mv Holografía Acústica en el Campo Cercano
Problemas Mal Planteados
Regularización
topic Holografía Acústica en el Campo Cercano
Problemas Mal Planteados
Regularización
Near-field Acoustic Holography
Posed problems
Regularization
dc.subject.keyword.spa.fl_str_mv Near-field Acoustic Holography
Posed problems
Regularization
description Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify vibrating sources. This article aims to compare and implement various regularization methods in the context of NAH. Specifically, it compares commonly used Tikhonov regularization, sparsity-based regularization, and neural networks (NN) regularization for a planar NAH array with measurements obtained from an experimental setup. Additionally, it theoretically introduces Green’s function-based regularization. The first three types of regularization methods yield images consistent with the results, and statistical indicators are used to determine which method performs best at different frequencies.
publishDate 2024
dc.date.available.none.fl_str_mv 2024-11-06T21:43:22Z
dc.date.issued.none.fl_str_mv 2024
dc.date.accessioned.none.fl_str_mv 2024-11-06T21:43:22Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.spa.fl_str_mv Artículo
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10784/34775
url https://hdl.handle.net/10784/34775
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.format.eng.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
institution Universidad EAFIT
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