Universidad Tecnológica de Bolívar
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SunspotCalc: Una aplicación basada en Web y Python para calcular la rotación diferencial del sol y su fotosfera
No full textEn este manuscrito presentamos una aplicación web con soporte en lenguaje de programación PYTHON, REACTJS y JAVASCRIPT, libre y abierta, para el desarrollo de una actividad de enseñanza-aprendizaje de la astronomía, específicamente para el cálculo de la rotación diferencial del Sol para estudiantes y publicó en general en edad escolar entre 10 y 18 años. El propósito fundamental es la de difundir el conocimiento del Sol y algunas de sus propiedades. La aplicación web es autocontenida y con suficiente guía y ayuda para que cualquiera pueda usarla, además de su dinamismo y diseño innovador, pretende presentar estrategias agradables para la enseñanza y aprendizaje de la ciencia en torno al Sol.In this manuscript we present a web application with support in PYTHON, REACTJS and JAVASCRIPT programming language, free and open, for the development of a teaching-learning activity of astronomy, specifically for the calculation of the differential rotation of the Sun for students and general public in school age between 10 and 18 years old. The main purpose is to spread the knowledge of the Sun and some of its properties. The web application is self-contained and with enough guidance and help for anyone to use it, in addition to its dynamism and innovative design, it aims to present pleasant strategies for teaching and learning science around the Sun
FACTS placement for reactive power planning with weak node constraints using an improved symbiotic search algorithm
No full textIn this paper, an economically feasible and reliable operation of the IEEE 57 bus system for Optimal Power Flow (OPF) is proposed. The Improved Symbiotic Organisms Search (ISOS) algorithm is proposed for effective reactive power planning as an OPF issue. Further, the optimal position of Flexible AC Transmission Systems (FACTS) is taken into consideration, by including the existing system controlling variables like reactive power generators output, transformer tapping and capacitors connected at shunt. The objective of the work is two-fold; i.e., to reduce the energy loss and to enhance the voltage profile within the prescribed limit by ensuring the economic operation and investment cost of FACTS in the system. In this work, two FACTS devices like Static Var Compensator (SVC) and Thyristor-Controlled Series Controller (TCSC) have been taken into consideration. Voltage sensitivity indicator and reactive power flow are two tools that are utilised in order to locate weak nodes for the implementation of FACTS. Finally, the performance of the ISOS algorithm is compared with that of three other state- of-the -art optimization techniques, such as, Symbiotic Organisms Search (SOS), Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). A Non-parametric statistical analysis is also performed to investigate the dominance of the ISOS algorithm over others
The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation
No full textOver the next 5 yr, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4 m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage IV dark energy galaxy survey. At z 10 million galaxies spanning 14,000 deg2 . In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target an r 80% fiber assignment efficiency. Finally, BGS Bright and BGS Faint will achieve >95% redshift success over any observing condition. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise baryon acoustic oscillation and redshift-space distortion measurements at z < 0.4. It presents opportunities for new methods that require highly complete and dense samples (e.g., N-point statistics, multitracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter
A machine learning model to predict standardized tests in engineering programs in Colombia
No full textThis research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytics approach based on three stages was developed: first, analysis and debugging of the database; second, multivariate analysis; and third, machine learning techniques. The results show an association between the performance levels in the Highschool test and the university test results. In addition, the machine learning algorithm that adequately fits the research problem is the Generalized Linear Network Model. For the training stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.810, 0.820, 0.813, and 0.827, respectively; in the evaluation stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.820, 0.820, 0.827 and 0.813 respectively
Annual Operating Costs Minimization in Electrical Distribution Networks via the Optimal Selection and Location of Fixed-Step Capacitor Banks Using a Hybrid Mathematical Formulation
No full textThe minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33-and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04% and 34.29% with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
Love Thy Neighbor: La Vida Musical De Justo Almario
No full textHay un hecho en esta historia que ayuda a comprender la euforia que despertaron estos
jóvenes. Recordemos que pocos años antes Pello Torres había estado precisamente en la misma ciudad, Barrancabermeja, tocando para estadounidenses temas “americanos”.
“Cuando le hice la Orquesta di Lido, yo le había conseguido unos arreglos muy especiales a Justo, americanizados, como Only Yoy, Tenderly, Blue Moon. Comenzó a tocar la orquesta y no bailó nadie. Nadie. Todo el mundo era viendo el espectáculo. [Solloza]. Total que lo cargaron, me cargaron a mí, cogieron esos pelaos y a todo el mundo lo pasearon en ese campo
Optimal Power Dispatch of PV Generators in AC Distribution Networks by Considering Solar, Environmental, and Power Demand Conditions from Colombia
No full textThis paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consid eration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids
A study of the Beeclust algorithm for robot swarm aggregation
No full textSwarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at the robot level to accomplish a task at the swarm level. The characteristics and properties of natural swarms have been studied to solve this problem. From these studies, basic behaviors have been defined, one of them being aggregation. This work explores the classical aggregation algorithm known as Beeclust. The Beeclust algorithm was implemented in MATLAB. Test were performed to determine its effectiveness in forming aggregates and the factors that affect its efficiency. © 2022 IEEE
Principales dificultades del alumnado universitario novel a la hora de elaborar un texto científico
No full textThe present research-action work is a descriptive mixed analysis of the micro-writing, macro-writing, methodological and writing in a public presentation errors committed by 46 novel psychology students when they making a compilation of scientific articles for an academic subject; choosing the highest quality project of the semester and comparing it with other academic works of the course. The objective of this pedagogical experience is based on the fact that students must learn researching skills such as important in research as the ability to obtain reliable and valid scientific information in from different databases, synthesizing all these data in a genuine document that is administrated by specific rules of writing and finally, they must make a public presentation of the results in a classroom within a stipulated time. Principal conclusions of this study indicate that university students have significant lacks when it comes to making an integrated and structured summary based on a specific regulation. © 2020 Universidad de Extremadura. All rights reserved
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections
No full textInclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different schemes. However, the adjustments allowed by these devices are limited. Thus, scenario grouping techniques are proposed to reduce the number of required configurations. This paper aims to evaluate the performance of different grouping techniques with input parameters for coordination strategies of electrical overcurrent protections, where it is required to associate the different modes of operation of a distribution network. For the clustering process, unsupervised learning techniques such as K-means, K-medoids and Agglomerative Hierarchical Clustering were employed. Additionally, for the input characteristics, fault currents, nominal currents and other parameters obtained from the electrical system were taken into account. From the results obtained when evaluating different combinations of techniques and inputs, it is important to mention that the characteristics that describe the different modes of operation necessary for the grouping are decisive for the coordination strategies of electrical protections and that it is not possible to establish a significant difference between the clustering techniques evaluated. Lastly, the combination that presents the best performance was K-means: Manhattan and maximum short-circuit phase currents per relay with a sum of operation time of 428.72s and zero restriction violation. © 2022 IEEE