Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo b...
- Autores:
-
Ruiz Madera, Christian Camilo
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Militar Nueva Granada
- Repositorio:
- Repositorio UMNG
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unimilitar.edu.co:10654/21214
- Acceso en línea:
- http://hdl.handle.net/10654/21214
- Palabra clave:
- ALGORITMOS
INTELIGENCIA ARTIFICIAL
PROCESAMIENTO DIGITAL DE IMAGENES
Carbonation
Segmentation
Concrete
Artificial intelligence
Carbonatación
Segmentación
Concreto
Inteligencia artificial
- Rights
- License
- Derechos Reservados - Universidad Militar Nueva Granada, 2019
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dc.title.spa.fl_str_mv |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
dc.title.translated.spa.fl_str_mv |
Implementation of artificial intelligence algorithms and digital image processing in the determination of carbonation depth in concrete structures |
title |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
spellingShingle |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto ALGORITMOS INTELIGENCIA ARTIFICIAL PROCESAMIENTO DIGITAL DE IMAGENES Carbonation Segmentation Concrete Artificial intelligence Carbonatación Segmentación Concreto Inteligencia artificial |
title_short |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
title_full |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
title_fullStr |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
title_full_unstemmed |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
title_sort |
Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto |
dc.creator.fl_str_mv |
Ruiz Madera, Christian Camilo |
dc.contributor.advisor.spa.fl_str_mv |
Caballero Gomez, Jose Luis |
dc.contributor.author.spa.fl_str_mv |
Ruiz Madera, Christian Camilo |
dc.subject.lemb.spa.fl_str_mv |
ALGORITMOS INTELIGENCIA ARTIFICIAL PROCESAMIENTO DIGITAL DE IMAGENES |
topic |
ALGORITMOS INTELIGENCIA ARTIFICIAL PROCESAMIENTO DIGITAL DE IMAGENES Carbonation Segmentation Concrete Artificial intelligence Carbonatación Segmentación Concreto Inteligencia artificial |
dc.subject.keywords.spa.fl_str_mv |
Carbonation Segmentation Concrete Artificial intelligence |
dc.subject.proposal.spa.fl_str_mv |
Carbonatación Segmentación Concreto Inteligencia artificial |
description |
El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo basado en procesamiento digital de imágenes (PDI) usando técnicas como operaciones morfológicas y apoyándose en la información de los canales RGB como herramienta principal de segmentación. El segundo algoritmo está basado en inteligencia artificial, más específicamente en redes de Kohonen, aunque usando también algunas técnicas de PDI para refinar los resultados. En este proceso, el algoritmo por inteligencia artificial evidenció mejores resultados que el algoritmo usando solo PDI en aproximadamente un 20% en la segmentación de la zona no carbonatada de la probeta de concreto. |
publishDate |
2019 |
dc.date.accessioned.none.fl_str_mv |
2019-05-17T16:39:31Z 2019-12-26T22:10:39Z |
dc.date.available.none.fl_str_mv |
2019-05-17T16:39:31Z 2019-12-26T22:10:39Z |
dc.date.issued.none.fl_str_mv |
2019-04-11 |
dc.type.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.local.spa.fl_str_mv |
Trabajo de grado |
dc.type.dcmi-type-vocabulary.spa.fl_str_mv |
Text |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10654/21214 |
url |
http://hdl.handle.net/10654/21214 |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.references.spa.fl_str_mv |
A. M. Carvajal, C. Silva, J. Valiente, and A. Venegas, “Efectos de la Carbonatación Acelerada en Distintos Tipos de Cemento y Hormigones,” Rev. la construcción, vol. 6, no. 1, 2007. E. I. Serrano Ixtepan, D.; Moreno, “Películas barrera: Una opción contra la carbonatación del concreto reforzado,” Ing. Rev. Académica la FI-UADY, vol. 10, no. 2, pp. 37–45, 2006. P. Garcés Terradillos, M. Á. Llorca, and E. Gómez, CORROSIÓN DE ARMADURAS EN ESTRUCTURAS DE HORMIGÓN ARMADO, 1st ed. España, 2008. D. J. Prada Campos, “Evaluación de la presencia de carbonatación en puentes vehiculares de concreto, localidades de Usaquén y Fontibón,” 2014. G. R. Rodriguez, W. A. A. Chaparro, and R. V. Aravena, “SOFTWARE PARA EL CÁLCULO DE LA VELOCIDAD DE DETERIORO DE LOS HORMIGONES SOMETIDOS A CARBONATACIÓN,” Rev. Latinoam. Metal. y Mater., vol. 0, no. 0, pp. 45–54, Feb. 2014. C. Jiang, Q. Huang, X. Gu, and W. Zhang, “Experimental investigation on carbonation in fatigue-damaged concrete,” Cem. Concr. Res., vol. 99, pp. 38–52, Sep. 2017. H. Xu, Z. Chen, S. Li, W. Huang, and D. Ma, “Carbonation Test Study on Low Calcium Fly Ash Concrete,” Appl. Mech. Mater., vol. 34, pp. 327–331, 2010. S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, Jun. 2017. L. Antonio, B. Mehul, O. Alessandro, and V. David, “The role of cognitive architectures in general artificial intelligence,” Cogn. Syst. Res., vol. 48, pp. 1–3, May 2018. I. Janotka, M. Bačuvčík, and P. Paulík, “Low carbonation of concrete found on 100-year-old bridges,” Case Stud. Constr. Mater., vol. 8, pp. 97–115, Jun. 2018. D. Li et al., “Evaluating the effect of external and internal factors on carbonation of existing concrete building structures,” Constr. Build. Mater., vol. 167, pp. 73–81, Apr. 2018. S. R. da Silva and J. J. de Oliveira Andrade, “Investigation of mechanical properties and carbonation of concretes with construction and demolition waste and fly ash,” Constr. Build. Mater., vol. 153, pp. 704–715, Oct. 2017. J. He, Q. Gao, Y. Wu, J. He, and X. Pu, “Study on improvement of carbonation resistance of alkali-activated slag concrete,” Constr. Build. Mater., vol. 176, pp. 60–67, Jul. 2018. S. C. Paul, B. Panda, Y. Huang, A. Garg, and X. Peng, “An empirical model design for evaluation and estimation of carbonation depth in concrete,” Measurement, vol. 124, pp. 205–210, Aug. 2018. J. Tang, J. Wu, Z. Zou, A. Yue, and A. Mueller, “Influence of axial loading and carbonation age on the carbonation resistance of recycled aggregate concrete,” Constr. Build. Mater., vol. 173, pp. 707–717, Jun. 2018. G. Li and X. Zhou, “The Research of Predicting the Carbonation Depth of Concrete with Time-series Analysis.” R. Xiang, “Prediction of Concrete Carbonation Depth Based on Support Vector Regression,” in 2009 Third International Symposium on Intelligent Information Technology Application, 2009, pp. 172–175. Hui Li and Chunhua Lu, “Artificial neural network analysis of concrete carbonation under sustained loads,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010, pp. V10-160-V10-164. Y. Liu, S. Zhao, and C. Yi, “The Forecast of Carbonation Depth of Concrete Based on RBF Neural Network,” in 2008 Second International Symposium on Intelligent Information Technology Application, 2008, pp. 544–548. H.-K. Shen, P.-H. Chen, and L.-M. Chang, “Human-visual-perception-like intensity recognition for color rust images based on artificial neural network,” Autom. Constr., vol. 90, pp. 178–187, Jun. 2018. F. Wang, L. Man, B. Wang, Y. Xiao, W. Pan, and X. Lu, “Fuzzy-based algorithm for color recognition of license plates,” Pattern Recognit. Lett., vol. 29, no. 7, pp. 1007–1020, May 2008. L. Shuhua and G. Gaizhi, “The application of improved HSV color space model in image processing,” in 2010 2nd International Conference on Future Computer and Communication, 2010, pp. V2-10-V2-13. P. P. Garcia Garcia, “Reconocimiento de imagenes usando redes neuronales artificiales,” Universidad Complutense de Madrid, 2013. H. Li, Z. Liu, Y. Huang, and Y. Shi, “Quaternion generic Fourier descriptor for color object recognition,” Pattern Recognit., vol. 48, no. 12, pp. 3895–3903, Dec. 2015. Q. Zhang, L. Zhuo, J. Li, J. Zhang, H. Zhang, and X. Li, “Vehicle color recognition using Multiple-Layer Feature Representations of lightweight convolutional neural network,” Signal Processing, vol. 147, pp. 146–153, Jun. 2018. Á. García-Martín and J. M. Martínez, “Post-processing approaches for improving people detection performance,” Comput. Vis. Image Underst., vol. 133, pp. 76–89, 2015. S. Zhu, L. Wang, and S. Duan, “Memristive pulse coupled neural network with applications in medical image processing,” Neurocomputing, vol. 227, pp. 149–157, 2017. L. Guo, L. Chen, C. L. P. Chen, and J. Zhou, “Integrating guided filter into fuzzy clustering for noisy image segmentation,” Digit. Signal Process., vol. 83, pp. 235–248, 2018. C. Sha, J. Hou, and H. Cui, “A robust 2D Otsu’s thresholding method in image segmentation,” J. Vis. Commun. Image Represent., vol. 41, pp. 339–351, 2016. N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Journals Mag., vol. 9, no. 1, pp. 62–66, 1979. M. Singh and A. S. Arora, “A robust anti-spoofing technique for face liveness detection with morphological operations,” Optik (Stuttg)., vol. 139, pp. 347–354, 2017. F. G. Ortiz Zamora and Fundación Biblioteca Virtual Miguel de Cervantes., Procesamiento morfológico de imágenes en color : aplicación a la reconstrucción geodésica. Fundación Biblioteca Virtual Miguel de Cervantes, 2002. C. M. 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Lopez, “Una aproximación práctica a las redes neuronales artificiales,” Universidad del Valle, 2009. H. M. Gámez Albán, J. P. Orejuela Cabrera, Ó. A. Salas Achipiz, and J. J. Bravo Bastidas, “APLICACIÓN DE MAPAS DE KOHONEN PARA LA PRIORIZACIÓN DE ZONAS DE MERCADO: UNA APROXIMACIÓN PRÁCTICA,” Rev. EIA, no. 25, pp. 157–169, 2016. E. Khvorostukhina, A. L’vov, and S. Ivzhenko, “Performance improvements of a Kohonen self-organizing training algorithm,” in 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2017, pp. 456–458. H. Hikawa and Y. Maeda, “Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function,” IEEE Trans. Neural Networks Learn. Syst., vol. 26, no. 11, pp. 2861–2873, Nov. 2015. S. Moshfe, A. Khoei, K. Hadidi, and B. Mashoufi, “A fully programmable nano-watt analogue CMOS circuit for Gaussian functions,” in 2010 International Conference on Electronic Devices, Systems and Applications, 2010, pp. 82–87. G. Cheng, T. Liu, K. Wang, and J. Han, “Soft Competitive Learning and Growing Self-Organizing Neural Networks for Pattern Classification,” in 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2006, pp. 378–381. Z. Li, T. Antao, and L. Yang, “Hand gesture recognition of sEMG based on modified Kohonen network,” in 2011 International Conference on Electronics, Communications and Control (ICECC), 2011, pp. 1476–1479. H. Geng, “QUALITY FUNCTION DEPLOYMENT AND DESIGN OF EXPERIMENTS,” Manufacturing Engineering Handbook. McGraw Hill Professional, Access Engineering, 2004. W. Z. Taffese, E. Sistonen, and J. Puttonen, “CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods,” Constr. Build. Mater., vol. 100, pp. 70–82, Dec. 2015. L. Zhuguo and L. Sha, “Carbonation resistance of fly ash and blast furnace slag based geopolymer concrete,” Constr. Build. Mater., vol. 163, pp. 668–680, 2018. M. Liwu, Z. Feng, D. Min, J. Fei, A.-T. Abir, and W. Aiguo, “Accelerated carbonation and performance of concrete made with steel slag as binding materials and aggregates,” Cem. Concr. Compos., vol. 83, pp. 138–145, 2017. R. Emmanuel, L. Ahmed, and C. François, “A performance based approach for durability of concrete exposed to carbonation,” Constr. Build. Mater., vol. 23, no. 1, pp. 190–199, 2009. R. Guedes, R. Sant Ana, L. Goncalves, A. Oliveira, B. Cardoso, and A. Garcez, “Assessment of the durability of grout submitted to accelerated carbonation test,” Constr. Build. Mater., vol. 159, pp. 261–268, 2018. C. Jeong-Il, L. Yun, K. Yun Yong, and L. Bang Yeon, “Image-processing technique to detect carbonation regions of concrete sprayed with a phenolphthalein solution,” Constr. Build. Mater., vol. 154, pp. 451–461, 2017. P. Joaquin, “Estudio comparativo de algoritmos disponibles en ITK para la segmentación de imágenesmédicas,” Escuela Superior de Ingenieros –Universidad de Sevila, 2010. A. Revert, K. De Weerdt, K. Hombostei, and M. Geiker, “Carbonation-induced corrosion: Investigation of the corrosion onset,” Constr. Build. Mater., vol. 162, pp. 847–856, 2018. |
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Derechos Reservados - Universidad Militar Nueva Granada, 2019 |
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Caballero Gomez, Jose LuisRuiz Madera, Christian CamiloIngeniero en MecatrónicaCalle 1002019-05-17T16:39:31Z2019-12-26T22:10:39Z2019-05-17T16:39:31Z2019-12-26T22:10:39Z2019-04-11http://hdl.handle.net/10654/21214El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo basado en procesamiento digital de imágenes (PDI) usando técnicas como operaciones morfológicas y apoyándose en la información de los canales RGB como herramienta principal de segmentación. El segundo algoritmo está basado en inteligencia artificial, más específicamente en redes de Kohonen, aunque usando también algunas técnicas de PDI para refinar los resultados. En este proceso, el algoritmo por inteligencia artificial evidenció mejores resultados que el algoritmo usando solo PDI en aproximadamente un 20% en la segmentación de la zona no carbonatada de la probeta de concreto.1. Capítulo 1 16 Introducción 16 1.1 Planteamiento del problema 16 1.2 Justificación 18 1.3 Objetivos 19 1.3.1 Objetivo general 19 1.3.2 Objetivos específicos 19 1.4 Antecedentes 20 2. Capítulo 2 24 Marco referencial 24 2.1 Carbonatación 24 2.2 Procesamiento digital de imágenes 25 2.2.1 Segmentación de imágenes 25 2.2.2 Operaciones morfológicas 27 2.3 Inteligencia artificial 29 2.3.1 Neuronas biológicas 29 2.3.2 Neurona artificial 30 2.3.3 Redes neuronales 31 2.3.4 Mapa autoorganizativo de Kohonen (KSOM) 33 2.3.5 Funcionamiento del KSOM 38 3. Capítulo 3 43 Diseño de cámara de iluminación 43 3.1 Matriz QFD 43 3.2 Diseño conceptual 45 3.3 Diseño CAD 47 3.4 Comparación de brillo y contraste 48 4. Capítulo 4 49 Metodología 49 4.1 Metodología PDI 49 4.2 Metodología IA 51 4.3 Procesamiento digital de imágenes 51 4.3.1 Escala de grises 51 4.3.2 Umbralización 53 4.3.3 Operaciones morfológicas 54 4.3.4 Selección área más grande no carbonatada 56 4.3.5 Resultados adicionales 57 4.3.6 Algoritmo para probetas circulares 58 4.4 Implementación KSOM 63 4.5 Calidad de la segmentación 69 4.6 Medición de la profundidad de carbonatación 70 4.6.1 Comparación de resultados 75 4.7 Ensayos experimentales 78 4.7.1 Interfaz grafica 79 5. Análisis de resultados 80 6. Conclusiones y recomendaciones 82 6.1 Conclusiones 82 6.2 Recomendaciones 83The project presents the development and implementation of two algorithms for the segmentation of the non-carbonated zone in specimens of concrete structures with reinforced steel starting from a photograph of the phenolphthalein test applied to the specimen. First, an algorithm based on digital image processing (PDI) was implemented using techniques such as morphological operations and relying on RGB channel information as the main segmentation tool. The second algorithm is based on artificial intelligence, more specifically on Kohonen networks, but also using some PDI techniques to refine the results. In this process, the artificial intelligence algorithm showed better results than the algorithm using only PDI by approximately 20% in the segmentation of the non-carbonated zone of the concrete test tube.Pregradoapplication/pdfspaDerechos Reservados - Universidad Militar Nueva Granada, 2019https://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadashttp://purl.org/coar/access_right/c_abf2Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concretoImplementation of artificial intelligence algorithms and digital image processing in the determination of carbonation depth in concrete structuresinfo:eu-repo/semantics/bachelorThesisTrabajo de gradoTexthttp://purl.org/coar/resource_type/c_7a1fALGORITMOSINTELIGENCIA ARTIFICIALPROCESAMIENTO DIGITAL DE IMAGENESCarbonationSegmentationConcreteArtificial intelligenceCarbonataciónSegmentaciónConcretoInteligencia artificialFacultad de IngenieríadIngeniería en MecatrónicaIngeniería - Ingeniería en MecatrónicaUniversidad Militar Nueva GranadaA. M. Carvajal, C. Silva, J. Valiente, and A. Venegas, “Efectos de la Carbonatación Acelerada en Distintos Tipos de Cemento y Hormigones,” Rev. la construcción, vol. 6, no. 1, 2007.E. I. Serrano Ixtepan, D.; Moreno, “Películas barrera: Una opción contra la carbonatación del concreto reforzado,” Ing. Rev. Académica la FI-UADY, vol. 10, no. 2, pp. 37–45, 2006.P. Garcés Terradillos, M. Á. Llorca, and E. Gómez, CORROSIÓN DE ARMADURAS EN ESTRUCTURAS DE HORMIGÓN ARMADO, 1st ed. España, 2008.D. J. Prada Campos, “Evaluación de la presencia de carbonatación en puentes vehiculares de concreto, localidades de Usaquén y Fontibón,” 2014.G. R. Rodriguez, W. A. A. Chaparro, and R. V. Aravena, “SOFTWARE PARA EL CÁLCULO DE LA VELOCIDAD DE DETERIORO DE LOS HORMIGONES SOMETIDOS A CARBONATACIÓN,” Rev. Latinoam. Metal. y Mater., vol. 0, no. 0, pp. 45–54, Feb. 2014.C. Jiang, Q. Huang, X. Gu, and W. Zhang, “Experimental investigation on carbonation in fatigue-damaged concrete,” Cem. Concr. Res., vol. 99, pp. 38–52, Sep. 2017.H. Xu, Z. Chen, S. Li, W. Huang, and D. Ma, “Carbonation Test Study on Low Calcium Fly Ash Concrete,” Appl. Mech. Mater., vol. 34, pp. 327–331, 2010.S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, Jun. 2017.L. Antonio, B. Mehul, O. Alessandro, and V. David, “The role of cognitive architectures in general artificial intelligence,” Cogn. Syst. Res., vol. 48, pp. 1–3, May 2018.I. Janotka, M. Bačuvčík, and P. Paulík, “Low carbonation of concrete found on 100-year-old bridges,” Case Stud. Constr. Mater., vol. 8, pp. 97–115, Jun. 2018.D. Li et al., “Evaluating the effect of external and internal factors on carbonation of existing concrete building structures,” Constr. Build. Mater., vol. 167, pp. 73–81, Apr. 2018.S. R. da Silva and J. J. de Oliveira Andrade, “Investigation of mechanical properties and carbonation of concretes with construction and demolition waste and fly ash,” Constr. Build. Mater., vol. 153, pp. 704–715, Oct. 2017.J. He, Q. Gao, Y. Wu, J. He, and X. Pu, “Study on improvement of carbonation resistance of alkali-activated slag concrete,” Constr. Build. Mater., vol. 176, pp. 60–67, Jul. 2018.S. C. Paul, B. Panda, Y. Huang, A. Garg, and X. Peng, “An empirical model design for evaluation and estimation of carbonation depth in concrete,” Measurement, vol. 124, pp. 205–210, Aug. 2018.J. Tang, J. Wu, Z. Zou, A. Yue, and A. Mueller, “Influence of axial loading and carbonation age on the carbonation resistance of recycled aggregate concrete,” Constr. Build. Mater., vol. 173, pp. 707–717, Jun. 2018.G. Li and X. Zhou, “The Research of Predicting the Carbonation Depth of Concrete with Time-series Analysis.”R. 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Mater., vol. 162, pp. 847–856, 2018.ORIGINALRuizMaderaChristianCamilo2019.pdfTesisapplication/pdf3761556http://repository.unimilitar.edu.co/bitstream/10654/21214/1/RuizMaderaChristianCamilo2019.pdffd1c1b1a7335c17635aa5dbd4c5fd0dbMD51LICENSElicense.txttext/plain2898http://repository.unimilitar.edu.co/bitstream/10654/21214/2/license.txt520e8f0b4e8d2d5c25366f2f78f584b0MD52THUMBNAILRuizMaderaChristianCamilo2019.pdf.jpgIM Thumbnailimage/jpeg6145http://repository.unimilitar.edu.co/bitstream/10654/21214/3/RuizMaderaChristianCamilo2019.pdf.jpg06e9761b6f7afb7594dc75eefe02b971MD5310654/21214oai:repository.unimilitar.edu.co:10654/212142020-06-30 13:05:00.128Repositorio Institucional UMNGbibliodigital@unimilitar.edu.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 |