Universidad Tecnológica de Bolívar
Repositorio UTB (Universidad Tecnológica de Bolívar)Not a member yet
11796 research outputs found
Sort by
Analysing a license plate-based vehicle restriction policy with optional exemption charge: The case in Cali, Colombia
No full textSeveral cities have restricted the use of private vehicles based on the last digit of a vehicle's license plate to reduce traffic congestion and pollution. However, the effectiveness of this measure has been questioned. In 2017, a hybrid scheme, License Plate Restriction Charging (LPRC), was implemented in Cali, Colombia. With this scheme, drivers can pay a charge (monthly, quarterly, or yearly) to circumvent the restriction, while the revenue is used to subsidise the BRT System. Cali was the first city in Latin America to implement such a scheme, while Colombia's capital, Bogota, adopted a similar policy in 2020. This article analyses the evolution of the measure using official information. In addition, we conducted a stated preferences survey and estimated a choice model to evaluate the behaviour of car owners to policy variables. Results show that LPRC price is the most relevant attribute in decision-making. Increasing the number of days with traffic restrictions and extending the hours of vehicle use restriction increases drivers' probability of paying for the LPRC. As currently implemented in Cali, the LPRC is a fixed cost that does not vary according to the car use level, encouraging users who pay for the exemption to use their car as much as possible to make the most out of the payment. Furthermore, the revenue from the charge contributes only marginally to financing the BRT. Finally, we propose several changes in the policy to improve its efficiency. Among them, consider a daily payment and hardening the current driving restriction. © 2023 The Author(s)APCs y acuerdos transformativos 2023, Elsevie
Love Thy Neighbor: La Vida Musical De Justo Almario
No full textSu siguiente producción fue en 2008. Como miembro de la Christian Jazz Artist, fue convocado para la grabación de un disco instrumental con fines religiosos, en el estilo smooth jazz. El grupo lo conformaron Justo Almario en saxofones, flauta y clarinete, Rique Pantoja en los teclados, Guillermo Guzmán en el bajo y Walter Rodríguez en la percusión. El álbum se titula Holy, Holy, Holy, Hymns to a different drum. Justo solo se vincula en este disco como instrumentista, sin participar de los arreglos, producción ni composiciones. Escuchemos uno de los temas de este disco, como ejemplo de la estética del álbum completo
Love Thy Neighbor: La Vida Musical De Justo Almario
No full textA pesar de su corta edad, las experiencias de Justico con la música eran múltiples. Además de las mencionadas orquestas y músicos, de las clases y de las presentaciones, también el clarinetista Pedro Nel Arango lo llevaba a tocar con su banda en diferentes eventos, y asistía también con frecuencia a los ensayos de la Banda Departamental y de la Orquesta de Lucho Bermúdez, la más importante del país en ese entonces
An Effective Power Dispatch of Photovoltaic Generators in DC Networks via the Antlion Optimizer
No full textThis paper studies the problem regarding the optimal power dispatch of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grid-connected and standalone networks. The mathematical model employed considers the reduction of operating costs, energy losses, and CO2 emissions as objective functions, and it integrates all technical and operating constraints implied by DC grids in a scenario of variable PV generation and power demand. As a solution methodology, a master–slave strategy was proposed, whose master stage employs Antlion Optimizer (ALO) for identifying the values of power to be dispatched by each PV-DG installed in the grid, whereas the slave stage uses a matrix hourly power flow method based on successive approximations to evaluate the objective functions and constraints associated with each solution proposed within the iterative process of the ALO. Two test scenarios were considered: a grid-connected network that considers the operating characteristics of the city of Medellín, Antioquia, and a standalone network that uses data from the municipality of Capurganá, Chocó, both of them located in Colombia. As comparison methods, five continuous optimization methods were used which were proposed in the specialized literature to solve optimal power flow problems in DC grids: the crow search algorithm, the particle swarm optimization algorithm, the multiverse optimization algorithm, the salp swarm algorithm, and the vortex search algorithm. The effectiveness of the proposed method was evaluated in terms of the solution, its repeatability, and its processing times, and it obtained the best results with respect to the comparison methods for both grid types. The simulation results obtained for both test systems evidenced that the proposed methodology obtained the best results with regard to the solution, with short processing times for all of the objective functions analyzed
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning
No full textAutomatic image recognition is a convenient option for labeling and categorizing fruits and vegetables in supermarkets. This paper proposes the design and implementation of an automatic classification system for Colombian fruits, by training a convolutional neural network. A database was created to train and test the system, which consisted of 4980 images, labeled in 22 classes, each corresponding to pictures of the same kind of fruit, trying to reproduce the variability of a real case scenario with occlusions, different positions, rotations, lightings, colors, etc., and the use of bags. On-training data augmentation was used to further increase the robustness of the model. Additionally, transfer learning was implemented by taking the parameters of a pretrained model used for fruit classification as the new initial parameters of the proposed convolutional network, achieving an increase of the classification accuracy compared with the same model when trained with random initial weights. The final classification accuracy of the network was 98.12% which matches the scores achieved on previous works that performed fruit classification on less challenging datasets. Considering top-3 classification we report an accuracy of 99.95%. © 2020 IOP Publishing Ltd. All rights reserved
Determinación de los cambios en la cobertura vegetal del bosque de manglar; de la Ciénaga de la Virgen Caribe colombiano, asociados a asentamientos no planificados mediante el uso de sensores remotos y sistemas de información geográfica
No full textLa Ciénaga de la Virgen constituye uno de los principales ecosistemas de la ciudad de Cartagena de Indias y es el lugar predilecto para el crecimiento del bosque de manglar. Sin embargo, el ecosistema ha sido sometido a presiones antrópica tales como los asentamientos no planificados. Por tanto, surge la necesidad de identificar los cambios en los bosques de manglar asociados a estos asentamientos. Para esto, se hizo uso de herramientas SIG, sensores remotos y un análisis multitemporal. El principal hallazgo en cuanto a la cobertura de manglar fue un aumento del área de 8,09 〖km〗^2 en 1991 a 8,73 〖km〗^2 en 2021, contrario a lo esperado. En cuanto a la cobertura de territorios artificializados, también se encontró un aumento de 5,95 〖km〗^2 en 1991 a 9,33 〖km〗^2 en 2021. De igual manera, se evaluaron normativas como el Plan de Ordenamiento territorial (del 2001), el acotamiento de Ronda Hídrica y la delimitación del humedal realizado por el Instituto Alexander Von Humboldt y se obtuvo un grado de incumplimiento del 4, 10 y 11% respectivamente. Finalmente, cabe mencionar que la vegetación de manglar encontrada en la Ciénaga de la Virgen corresponde a las especies de Avicennia germinans (mangle negro o salado o prieto), Conocarpus erectus (mangle zaragoza), Laguncularia Racemosa (mangle bobo o blanco) y Rhizophora mangle (mangle rojo).Universidad Tecnológica de BolívarIngeniero AmbientalPregrad
Machine Learning aplicado a la inspección post-mortem de la carne de res en las plantas de exportación del departamento de Córdoba Colombia
No full textEn el presente trabajo de investigación se hace un estudio con base en los registros de la entidad sanitaria de Colombia de las dos plantas de beneficio bnimal en el departamento de Córdoba que exportan canales cárnicos. El primer resultado que se generó fue la creación de una base de datos con variables encontradas en la inspección post-mortem, en especial en la maduración de la carne. Se dividió la investigación en dos partes asociadas a los valores que tomaba la variable de res- puesta, en el primer caso, se consideró binaria identificando si un canal pasaba o no el control de calidad para ser exportada, en la segunda parte se consideró Multiclase con 3 niveles asociados al rechazo de canales. Se buscó establecer un buen nivel de predicción y homogeneización en las categorías que tomaba la variable de respuesta en ambos casos. Como referencia se toman los datos arrojados por un modelo co- gísticos que en su versión Clásica se utilizó para ver significancia en los predictores y luego fue entrenado con un conjunto de prueba y validado con otra muestra. En la clasificación binaria se corrieron los algoritmos de machine learning K-Nearest Neighbor, Random Forest, Maquinas Vectores de Soporte y LigthGBM, optimizados en sus hiperparámetros con las herramientas GridSearchCV y RandomSearchCV que utilizan validación cruzada, con los mejores hiperparámetros de cada algoritmo se ensambló uno nuevo denominado Stacking, que también fue comparado. Los algo- ritmos Multiclases ejecutados fueron, K-Nearest Neighbor, Random Forest, Regresión Lineal de Ridge y Arboles de decisión optimizados solamente con la herramienta Ran- domizedSearchCV, también se ensambló nuevamente un Stacking. El algoritmo con mejores resultados y equilibrado en las métricas de desempeño del estudio binario fue el LGBM, logrando exactitud = 71, AUC = 71, sensibilidad = 72, Especificidad = 70 y precisión = 84. Por el lado multiclase el Stacking obtuvo los mejores resultados con exactitud = 72, precisión = 73 y Recall = 97. El trabajo se desarrolló en el sowfware Python en su versión 3.9.13 corrido mediante el editor libre de Microsoft Visual Studio Code. Como conclusión se establece que los algoritmos de machine learning optimizados en sus hiperparámetros permiten predecir mejor la calidad de la carne de canal bovina de exportación en el Departamento de Córdoba.Magister en Estadística AplicadaMaestrí
Love Thy Neighbor: La Vida Musical De Justo Almario
No full textEste tema, “Tabaquera”, compuesto por Carlos Suaza Oriol, es una cumbia tradicional cuya melodía hacía parte de los conjuntos de gaitas y se hizo famosa en la década de los 60 por la grabación del conjunto de Antolín Lenes, de Ciénaga de Oro (Córdoba). Es una obra con un lenguaje musical muy local
del Caribe colombiano, pero orquestada en formato big band y con unos arreglos claramente influidos por las big bands del jazz estadounidense y las big bands de los centros de producción musical de la Cuenca
del Caribe, principalmente de México y Cuba
Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm
No full textIn this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of CO 2 emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less CO 2 emissions, and shorter computational processing times. All the simulations were performed in MATLAB. © 2021, King Fahd University of Petroleum & Minerals.Ocampo-Toro, J. A., Garzon-Rivera, O. D., Grisales-Noreña, L. F., Montoya-Giraldo, O. D., & Gil-González, W. (2021). Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm. Arabian Journal for Science and Engineering, 46(10), 9995-10006
The applications of Industry 4.0 (I4.0) technologies in the palm oil industry in Colombia (Latin America)
No full textSeven percent of world palm oil (PO) production comes from Latin America (LATAM), 15% certified as sustainable PO with the adoption of the Round Table model for Sustainable Palm Oil (RSPO), pointing to sustainable agriculture that contributes to hunger reduction and a reduction of dependence on hydrocarbons for energy. Colombia is the fourth global producer of PO and the first one in LATAM and expects to produce over two million tons in 161 municipalities and 21 departments in 2021.
This document will focus on the Colombian PO industry. The contribution of this study is twofold: First, it provides a more comprehensive review of the PO industry technology literature based on Scopus and Clarivate Analytics, using the reporting checklist of preferred reporting items for systematic reviews and meta-analyses (PRISMA). Second, as far as the authors know, this is one of the first studies to address the technological solutions applied by Colombia's PO producers and aims to help fill this research gap.
Evidence for the use of Internet of Things (IoT), big data (BD), and cloud computing in the Colombian PO industry was found in the extraction plants, in crop and pest management, in the use of seeds with smart tags, and in biofuel generation from PO, positioning it as a country with multiple lessons to offer to the PO industry