Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos

ilustraciones, diagramas

Autores:: Espejo Mora, Edgar

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
dc.title.translated.eng.fl_str_mv	Comparative study of image classification algorithms based on functional data analysis. Case study on fracture surfaces of mechanical elements
title	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
spellingShingle	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje supervisado Fractografía Análisis de datos funcionales Autocorrelación espacial Supervised learning Fractography Functional Data Analysis Spatial autocorrelation classification algorithm artificial neural network strength of materials machine learning red neuronal artificial resistencia de materiales aprendizaje automático
title_short	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
title_full	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
title_fullStr	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
title_full_unstemmed	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
title_sort	Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos
dc.creator.fl_str_mv	Espejo Mora, Edgar
dc.contributor.advisor.spa.fl_str_mv	Bohorquez Castañeda, Martha Patricia
dc.contributor.author.spa.fl_str_mv	Espejo Mora, Edgar
dc.contributor.researchgroup.spa.fl_str_mv	Estadística Espacial
dc.contributor.orcid.spa.fl_str_mv	Espejo-Mora, Edgar [0000-0002-3745-102X]
dc.contributor.scopus.spa.fl_str_mv	Espejo-Mora, Edgar [57208106992]
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Aprendizaje supervisado Fractografía Análisis de datos funcionales Autocorrelación espacial Supervised learning Fractography Functional Data Analysis Spatial autocorrelation classification algorithm artificial neural network strength of materials machine learning red neuronal artificial resistencia de materiales aprendizaje automático
dc.subject.proposal.spa.fl_str_mv	Aprendizaje supervisado Fractografía Análisis de datos funcionales Autocorrelación espacial
dc.subject.proposal.eng.fl_str_mv	Supervised learning Fractography Functional Data Analysis Spatial autocorrelation
dc.subject.wikidata.eng.fl_str_mv	classification algorithm artificial neural network strength of materials machine learning
dc.subject.wikidata.spa.fl_str_mv	red neuronal artificial resistencia de materiales aprendizaje automático
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-17T15:34:13Z
dc.date.available.none.fl_str_mv	2024-07-17T15:34:13Z
dc.date.issued.none.fl_str_mv	2024-07-16
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86524
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86524 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[Alonso et al., 2012] Andrés M. Alonso, David Casado, Juan Romo, Supervised classification for functional data: A weighted distance approach, Computational Statistics & Data Analysis, Volume 56, Issue 7, 2012, Pages 2334-2346, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2012.01.013. [Ashraf et al., 2012] Hemeida, Ashraf & Hassan, Moatamad. (2022). Image classification based deep learning: A Review. Aswan University Journal of Sciences and Technology. 2. 10.21608/aujst.2022.259887. [Blanquero et al., 2019] Rafael Blanquero, Emilio Carrizosa, Asunción Jiménez-Cordero, Belén Martín-Barragán, Variable selection in classification for multivariate functional data, Information Sciences, Volume 481, 2019, Pages 445-462, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.12.060. [Chen et al., 2021] Chen, L.; Li, S.; Bai, Q.; Yang, J.; Jiang, S.; Miao, Y. Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens. 2021, 13, 4712. https://doi.org/10.3390/rs13224712. [Delaigle et al., 2012] Delaigle, Aurore & Hall, Peter. (2012). Achieving near Perfect Classification for Functional Data. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 74. 267 - 286. 10.1111/j.1467-9868.2011.01003.x. [Dryden et al., 2009] Ian L. Dryden, Alexey Koloydenko, Diwei Zhou "Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging," The Annals of Applied Statistics, Ann. Appl. Stat. 3(3), 1102-1123, (September 2009) [Febrero et al., 2012] Febrero-Bande M, Oviedo de la Fuente M (2012). “Statistical Computing in Functional Data Analysis: The R Package fda.usc.” Journal of Statistical Software, 51(4), 1–28. https://www.jstatsoft.org/v51/i04/. [Ghigliet et al., 2017] Ghiglietti, Andrea & Ieva, Francesca & Paganoni, Anna. (2017). Statistical inference for stochastic processes: Two-sample hypothesis tests. Journal of Statistical Planning and Inference. 180. 49-68. 10.1016/j.jspi.2016.08.004. [Jiaohua et al., 2020] Jiaohua Qin, Wenyan Pan, Xuyu Xiang, Yun Tan, Guimin Hou, A biological image classification method based on improved CNN, Ecological Informatics, Volume 58, 2020, 101093, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2020.101093. [Krishna et al., 2018] krishna, M & Neelima, M & Mane, Harshali & Matcha, Venu. (2018). Image classification using Deep learning. International Journal of Engineering & Technology. 7. 614. 10.14419/ijet.v7i2.7.10892. [Krizhevsky et al., 2012] Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. 25. 10.1145/3065386. [Kumar et al., 2012] Anil Kumar, C.P. Gandhi, Yuqing Zhou, Rajesh Kumar, Jiawei Xiang, Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images, Applied Acoustics, Volume 167, 2020, 107399, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2020.107399. [Llop et al., 2008] P. Llop, L. Forzani, R. Fraiman, On local times, density estimation and supervised classification from functional data, Journal of Multivariate Analysis, Volume 102, Issue 1, 2011, Pages 73-86, ISSN 0047-259X, https://doi.org/10.1016/j.jmva.2010.08.002. [Nilsback et al., 2008] M. -E. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 722-729, doi: 10.1109/ICVGIP.2008.47. [Oviedo et al., 2011] Manuel Oviedo de la Fuente, Manuel Febrero Bande, Localización: X Congreso SGAPEIO, Congreso Galego de Estatística e Investigación de Operacións: Pazo de Congresos e Exposicións, Pontevedra, 3, 4 y 5 de novembro de 2011, 2011, ISBN 978-84-938642-2-4. [Pedregosa et al., 2011] Pedregosa, F., Varoquaux, Ga"el, Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830. [R Core Team] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. [Radhesyam et al., 2020] Radhesyam Vaddi, Prabukumar Manoharan, Hyperspectral image classification using CNN with spectral and spatial features integration, Infrared Physics & Technology, Volume 107, 2020, 103296, ISSN 1350-4495, https://doi.org/10.1016/j.infrared.2020.103296. [Ramsay et al., 2005] J. O. Ramsay, B. W. Silverman, Functional Data Analysis, Springer, 2005, New York, ISBN 978-0-387-40080-8, https://doi.org/10.1007/b98888. [Sabat-Tomala et al., 2020] Sabat-Tomala, A.; Raczko, E.; Zagajewski, B. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens. 2020, 12, 516. https://doi.org/10.3390/rs12030516 [Sheykhmousa et al., 2020] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi and S. Homayouni, "Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6308-6325, 2020, doi: 10.1109/JSTARS.2020.3026724. [Shuoyang et al., 2021] Shuoyang Wang, Zuofeng Shang, Guanqun Cao and Jun S. Liu, Optimal classification for functional data, 2021, arXiv:2103.00569 [stat.ME] [Van Rossum et al., 1995] Van Rossum G and Drake Jr FL. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995. [Waqar et al., 2019] M. Waqar Akram, Guiqiang Li, Yi Jin, Xiao Chen, Changan Zhu, Xudong Zhao, Abdul Khaliq, M. Faheem, Ashfaq Ahmad, CNN based automatic detection of photovoltaic cell defects in electroluminescence images, Energy, Volume 189, 2019, 116319, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2019.116319. [Watanabe et al., 2019] Akihiko Watanabe, Naoto Hirose, Hyoungseop Kim, Ichiro Omura, Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices, Microelectronics Reliability, Volumes 100–101, 2019, 113399, ISSN 0026-2714, https://doi.org/10.1016/j.microrel.2019.113399. [Xinyu et al., 2023] Xinyu Huang, Ziyang Pan, A Functional Data Classification Model Utilizing Functional Mahalanobis Distance and Regenerative Kernel Methods. Journal of Electronics and Information Science (2023) Vol. 8: 104-110. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080613. [Yeonjoo et al., 2019] Yeonjoo Park, Douglas G. Simpson, Robust probabilistic classification applicable to irregularly sampled functional data, Computational Statistics & Data Analysis, Volume 131, 2019, Pages 37-49, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2018.08.001. [Yinfeng et al., 2016] Yinfeng Meng, Jiye Liang, Yuhua Qian, Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, Volume 97, 2016, Pages 224-236, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2015.12.016. [Zhao et al., 2020] Yudi Zhao, Kuangrong Hao, Haibo He, Xuesong Tang, Bing Wei, A visual long-short-term memory based integrated CNN model for fabric defect image classification, Neurocomputing, Volume 380, 2020, Pages 259-270, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2019.10.067.
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dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	xii, 87 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bohorquez Castañeda, Martha Patricia30ce5bd3b87c5753716782261f6b93ba600Espejo Mora, Edgar83b44f857d08e7309435dbb8a3917009600Estadística EspacialEspejo-Mora, Edgar [0000-0002-3745-102X]Espejo-Mora, Edgar [57208106992]2024-07-17T15:34:13Z2024-07-17T15:34:13Z2024-07-16https://repositorio.unal.edu.co/handle/unal/86524Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasPara este trabajo se usaron fotografías tomadas a superficies de fractura de elementos mecánicos, que fallaron mediante fractura dúctil, fractura frágil y fractura por fatiga. Cada uno de estos tipos de fractura deja una textura característica, a partir de la cual un experto en análisis de fallas puede usarla para clasificarlas. De las imágenes se extrajeron datos funcionales y con ellos se evaluó la exactitud de varios modelos de clasificación. De cada imagen de 200 x 200 pixeles, se extrajeron 400 datos funcionales correspondientes a cada línea de pixeles en X e Y. Como modelos se usaron métodos estadísticos basados en distancias, modelo lineal generalizado (MLG), modelo aditivo generalizado (MAG) y modelos basados en medidas de profundidad. También se usaron modelos de aprendizaje de máquina como K-vecinos más cercanos, máquina de soporte vectorial (MSV), regresión logística, árbol de decisión, bosque aleatorio y red neuronal tipo perceptrón. En los modelos estadísticos se evaluó también el efecto sobre la exactitud de clasificación, de incluir información de autocorrelación espacial intra dato funcional o del operador de covarianza. Como conclusiones relevantes se obtuvo que la inclusión de la información de autocorrelación espacial a los clasificadores basados en métodos estadísticos, mejora la exactitud de los mismos y que los datos funcionales extraídos de las imágenes tienen la información suficiente para entrenar modelos de clasificación. (Texto tomado de la fuente).For this work, photographs taken of fracture surfaces of mechanical elements were used, which failed through ductile fracture, brittle fracture and fatigue fracture. Each of these fracture types leaves a characteristic texture, from which a failure analysis expert can use to classify them. Functional data was extracted from the images and the accuracy of various classification models was evaluated. From each image of 200 x 200, 400 functional data were extracted corresponding to each line of pixels X and Y. Statistical methods based on distances, generalized linear model (GLM), generalized additive model (GAM) and models based on depth measurements were used. Machine learning models such as K-nearest neighbors, support vector machine (SVM), logistic regression, decision tree, random forest and perceptron-type neural network were also used. In the statistical models, the effect on classification accuracy of including spatial autocorrelation information or the covariance operator was also evaluated. As relevant conclusions, it was obtained that the inclusion of spatial autocorrelation information to classifiers based on statistical methods, improves their accuracy and that the functional data extracted from the images have sufficient information to train classification models.MaestríaMagíster en Ciencias - EstadísticaDatos funcionales espacialesxii, 87 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaAprendizaje supervisadoFractografíaAnálisis de datos funcionalesAutocorrelación espacialSupervised learningFractographyFunctional Data AnalysisSpatial autocorrelationclassification algorithmartificial neural networkstrength of materialsmachine learningred neuronal artificialresistencia de materialesaprendizaje automáticoEstudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicosComparative study of image classification algorithms based on functional data analysis. Case study on fracture surfaces of mechanical elementsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Alonso et al., 2012] Andrés M. Alonso, David Casado, Juan Romo, Supervised classification for functional data: A weighted distance approach, Computational Statistics & Data Analysis, Volume 56, Issue 7, 2012, Pages 2334-2346, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2012.01.013.[Ashraf et al., 2012] Hemeida, Ashraf & Hassan, Moatamad. (2022). Image classification based deep learning: A Review. Aswan University Journal of Sciences and Technology. 2. 10.21608/aujst.2022.259887.[Blanquero et al., 2019] Rafael Blanquero, Emilio Carrizosa, Asunción Jiménez-Cordero, Belén Martín-Barragán, Variable selection in classification for multivariate functional data, Information Sciences, Volume 481, 2019, Pages 445-462, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.12.060.[Chen et al., 2021] Chen, L.; Li, S.; Bai, Q.; Yang, J.; Jiang, S.; Miao, Y. Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens. 2021, 13, 4712. https://doi.org/10.3390/rs13224712.[Delaigle et al., 2012] Delaigle, Aurore & Hall, Peter. (2012). Achieving near Perfect Classification for Functional Data. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 74. 267 - 286. 10.1111/j.1467-9868.2011.01003.x.[Dryden et al., 2009] Ian L. Dryden, Alexey Koloydenko, Diwei Zhou "Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging," The Annals of Applied Statistics, Ann. Appl. Stat. 3(3), 1102-1123, (September 2009)[Febrero et al., 2012] Febrero-Bande M, Oviedo de la Fuente M (2012). “Statistical Computing in Functional Data Analysis: The R Package fda.usc.” Journal of Statistical Software, 51(4), 1–28. https://www.jstatsoft.org/v51/i04/.[Ghigliet et al., 2017] Ghiglietti, Andrea & Ieva, Francesca & Paganoni, Anna. (2017). Statistical inference for stochastic processes: Two-sample hypothesis tests. Journal of Statistical Planning and Inference. 180. 49-68. 10.1016/j.jspi.2016.08.004.[Jiaohua et al., 2020] Jiaohua Qin, Wenyan Pan, Xuyu Xiang, Yun Tan, Guimin Hou, A biological image classification method based on improved CNN, Ecological Informatics, Volume 58, 2020, 101093, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2020.101093.[Krishna et al., 2018] krishna, M & Neelima, M & Mane, Harshali & Matcha, Venu. (2018). Image classification using Deep learning. International Journal of Engineering & Technology. 7. 614. 10.14419/ijet.v7i2.7.10892.[Krizhevsky et al., 2012] Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. 25. 10.1145/3065386.[Kumar et al., 2012] Anil Kumar, C.P. Gandhi, Yuqing Zhou, Rajesh Kumar, Jiawei Xiang, Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images, Applied Acoustics, Volume 167, 2020, 107399, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2020.107399.[Llop et al., 2008] P. Llop, L. Forzani, R. Fraiman, On local times, density estimation and supervised classification from functional data, Journal of Multivariate Analysis, Volume 102, Issue 1, 2011, Pages 73-86, ISSN 0047-259X, https://doi.org/10.1016/j.jmva.2010.08.002.[Nilsback et al., 2008] M. -E. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 722-729, doi: 10.1109/ICVGIP.2008.47.[Oviedo et al., 2011] Manuel Oviedo de la Fuente, Manuel Febrero Bande, Localización: X Congreso SGAPEIO, Congreso Galego de Estatística e Investigación de Operacións: Pazo de Congresos e Exposicións, Pontevedra, 3, 4 y 5 de novembro de 2011, 2011, ISBN 978-84-938642-2-4.[Pedregosa et al., 2011] Pedregosa, F., Varoquaux, Ga"el, Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.[R Core Team] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.[Radhesyam et al., 2020] Radhesyam Vaddi, Prabukumar Manoharan, Hyperspectral image classification using CNN with spectral and spatial features integration, Infrared Physics & Technology, Volume 107, 2020, 103296, ISSN 1350-4495, https://doi.org/10.1016/j.infrared.2020.103296.[Ramsay et al., 2005] J. O. Ramsay, B. W. Silverman, Functional Data Analysis, Springer, 2005, New York, ISBN 978-0-387-40080-8, https://doi.org/10.1007/b98888.[Sabat-Tomala et al., 2020] Sabat-Tomala, A.; Raczko, E.; Zagajewski, B. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens. 2020, 12, 516. https://doi.org/10.3390/rs12030516[Sheykhmousa et al., 2020] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi and S. Homayouni, "Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6308-6325, 2020, doi: 10.1109/JSTARS.2020.3026724.[Shuoyang et al., 2021] Shuoyang Wang, Zuofeng Shang, Guanqun Cao and Jun S. Liu, Optimal classification for functional data, 2021, arXiv:2103.00569 [stat.ME][Van Rossum et al., 1995] Van Rossum G and Drake Jr FL. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.[Waqar et al., 2019] M. Waqar Akram, Guiqiang Li, Yi Jin, Xiao Chen, Changan Zhu, Xudong Zhao, Abdul Khaliq, M. Faheem, Ashfaq Ahmad, CNN based automatic detection of photovoltaic cell defects in electroluminescence images, Energy, Volume 189, 2019, 116319, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2019.116319.[Watanabe et al., 2019] Akihiko Watanabe, Naoto Hirose, Hyoungseop Kim, Ichiro Omura, Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices, Microelectronics Reliability, Volumes 100–101, 2019, 113399, ISSN 0026-2714, https://doi.org/10.1016/j.microrel.2019.113399.[Xinyu et al., 2023] Xinyu Huang, Ziyang Pan, A Functional Data Classification Model Utilizing Functional Mahalanobis Distance and Regenerative Kernel Methods. Journal of Electronics and Information Science (2023) Vol. 8: 104-110. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080613.[Yeonjoo et al., 2019] Yeonjoo Park, Douglas G. Simpson, Robust probabilistic classification applicable to irregularly sampled functional data, Computational Statistics & Data Analysis, Volume 131, 2019, Pages 37-49, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2018.08.001.[Yinfeng et al., 2016] Yinfeng Meng, Jiye Liang, Yuhua Qian, Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, Volume 97, 2016, Pages 224-236, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2015.12.016.[Zhao et al., 2020] Yudi Zhao, Kuangrong Hao, Haibo He, Xuesong Tang, Bing Wei, A visual long-short-term memory based integrated CNN model for fabric defect image classification, Neurocomputing, Volume 380, 2020, Pages 259-270, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2019.10.067.AdministradoresBibliotecariosConsejerosEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarProveedores de ayuda financiera para estudiantesPúblico 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Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos

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