Examinando por Autor "Velasquez, D."

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    Ítem
    Analysis of vibrations in a plate using interferometric methods
    (SPIE-INT SOC OPTICAL ENGINEERING, 2004-01-01) Rueda, E.; Angel, L.; Velasquez, D.; Universidad EAFIT. Departamento de Ciencias Básicas; Óptica Aplicada
    In this work some frequencies and modes of vibration of a thin plate were determined using interferometric methods like: real-time holographic interferometry, time-average holographic interferometry, digital speckle pattern interferometry DSPI. And the results obtained are compared with those obtained with the program of finite elements COSMOS.
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    Ítem
    Design and construction of an electromechanical velocity modulator for Mössbauer spectroscopy
    (SPRINGER, 2011-11-01) Velasquez, A. A.; Carmona, A.; Velasquez, D.; Angel, L.; Universidad EAFIT. Departamento de Ciencias Básicas; Electromagnetismo Aplicado (Gema)
    In this paper we report the design, construction and characterization of an electromechanical velocity modulator for application in Mössbauer spectroscopy. The modulator was constructed with copper coils, Neodymium magnets, steel cores and polymeric membranes. The magnetic field in the driving and velocity sensing stages was analyzed by the finite element method, which showed a linear relation between the magnetic field in the region of motion of both coils and the position of the coils within the steel cores. The results obtained by computational simulation allowed us to optimize geometries and dimensions of the elements of the system. The modulator presented its first resonance frequency at 16. 7 Hz, this value was in good agreement with that predicted by a second order model, which showed a resonant frequency of 16. 8 Hz. The linearity of the velocity signal of the modulator was analyzed through an optical method, based on a Michelson-Morley interferometer, in which the modulator moved one of the mirrors. Results showed a satisfactory linearity of the velocity signal obtained in the sensing coil, whose correlation with a straight line was around 0. 99987 for a triangular reference waveform. © 2011 Springer Science+Business Media B.V.
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    Ítem
    A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia
    (Universitatea Politehnica Bucuresti, 2020-01-19) Velasquez, D.; Sánchez, A.; Sarmiento Garavito, Sebastián; Toro, M.; Maiza Galparsoro, Mikel; Sierra Araujo, Basilio; Velasquez, D.; Sánchez, A.; Sarmiento Garavito, Sebastián; Toro, M.; Maiza Galparsoro, Mikel; Sierra Araujo, Basilio; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety,scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease. © 2020 by the authors.
  • No hay miniatura disponible
    Ítem
    A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia
    (Universitatea Politehnica Bucuresti, 2020-01-19) Velasquez, D.; Sánchez, A.; Sarmiento Garavito, Sebastián; Toro, M.; Maiza Galparsoro, Mikel; Sierra Araujo, Basilio; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)
    Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety,scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease. © 2020 by the authors.