Ayala-Chauvin, Manuel Ignacio
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Ayala-Chauvin, Manuel Ignacio
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Ambato
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mayala@uti.edu.ec
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57215428914

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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms

2022 , Espinosa Pinos, Carlos Alberto , Ayala-Chauvin, Manuel Ignacio , Buele, Jorge

Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Evolution, Collaborations, and Impacts of Big Data Research in Ecuador: Bibliometric Analysis

2024 , Avilés-Castillo, Fátima , Ayala-Chauvin, Manuel Ignacio , Buele J.

Big Data has been gaining significant attention globally due to its potential to drive innovation, guide decision-making, and stimulate economic growth. As part of this global trend, Ecuador has also witnessed a surge in Big Data-related research over the past decade. This study comprehensively analyzes Big Data research evolution, collaborations, and impacts in Ecuador from 2012 to 2023. By examining the patterns of publication, researcher demographics, primary languages, significant publishers, most cited research papers, patterns of author collaboration, and prevalent keywords, we strive to construct a detailed portrayal of the Big Data research landscape in the country. Our investigation reveals a noticeable increase in Big Data research activity post-2015, particularly within major cities like Quito and Guayaquil. Notably, the study also underscores the predominance of English in research publications, with leading publishers such as IEEE and Springer playing significant roles. The diverse themes of the most cited articles illustrate the wide-ranging applications of Big Data research within the country. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Control System Test Platform for a DC Motor

2022 , Saá-Tapia F. , Mayorga-Miranda L. , Ayala-Chauvin, Manuel Ignacio , Domènech-Mestres C.

Currently, control systems are used to improve the behavior of actuators that are part of an equipment or process. However, to enhance their performance, it is necessary to perform tests to evaluate the responses of its operation depending on the type of controller. In this sense, a test platform was developed to compare and optimize the speed control of a DC motor with three types of controllers: Predictive Model Control (MPC), Proportional Integral Derivative (PID) and Fuzzy Logic. Data acquisition was performed using the Arduino MEGA board and LabVIEW software. The mathematical model of the three controllers was developed, taking into account the electrical and physical properties of the DC motor. Through MATLAB IDENT, the state space (SS) and transfer function F(S) equations were generated for the MPC and PID controller, respectively; on the other hand, input/output ranges for the Fuzzy Logic controller were input/output ranges defined by assigning belonging functions and linguistic variables. Experimental tests were carried out with these models under no-load and load. Tests performed in vacuum show that performance index with the motor at 100 rpm results in a PID of 0.2245, a Fuzzy Logic of 0.3212 and an MPC of 0.3576. On the other hand, with load at 100 rpm, a PID of 0.2343, a Fuzzy Logic of 0.3871 and an MPC of 0.3104 were obtained. It was determined that the Fuzzy Logic controller presents a higher over impulse; the PID and MPC have a faster stabilization time and with negligible over impulses. Finally, the MPC controller presents a better performance index analysis according to the Integral Square Error criterion (ISE). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Radar system for the reconstruction of 3D Objects: A preliminary study

2020 , Barberán, J. , Moreta, D. , Ayala-Chauvin, Manuel Ignacio , Obregón, J. , Domínguez, R. , Buele, J.L. , Obregón G.

Objects recognition and their reconstruction is a process that involves a significant economic investment. This manuscript presents the basis for the design of a system that detects objects, extracts its main characteristics and digitally reconstructs them in three dimensions considering a reduced economic investment. The popular technological tool Kinect on its version 2.0 and MATLAB software have been linked to develop an efficient algorithm. Next, the process to obtain this prototype is briefly described, as well as the results from the corresponding experimental tests. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

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Static reactive power compensator design, based on three-phase voltage converter

2021 , Ayala-Chauvin, Manuel Ignacio , Kavrakov B.S. , Buele, Jorge , Varela Aldas, José

At present, electrical network stability is of the utmost importance because of the increase in electric demand and the integration of distributed generation deriving from renewable energy. In this paper, we proposed a static reactive power compensator model with common direct current voltage sources. Converter parameters were calculated and designed to fulfill specifications. In order to ascertain the device response for different operating modes as reactive power consumer and generator, we developed the model’s power and control circuits in Matlab Simulink. Simulations were performed for different conditions, and as a result, the current and voltage waveforms and the circular power chart were obtained. This paper has theoretically proven it is possible to achieve the consumption or generation of purely active or reactive power by implementing a static reactive power compensator with common DC voltage sources. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Lifecycle assessment of electricity generation transition in ecuador

2021 , Ayala-Chauvin, Manuel Ignacio , Samaniego-Ojeda C. , Riba G. , Maldonado-Correa J.

Ecuador’s energy mix has greatly reduced its dependency on fossil fuels the last 15 years, down to a marginal role (5%) in electricity generation in 2017. The development plan for the Ecuadorian power network aims to keep adding hydropower to meet the increasing demand. A prospective lifecycle assessment (LCA) of the future power network (2012–2050) can determine the feasibility of the development plan and its environmental sustainability in the long run. For a quantitative analysis of the energy transition over the entire lifecycle, the simulation software® Global Emission Model of Integrated System (GEMIS) is used. The results show that the current development path of the Ecuadorian energy system reduces the emissions of CO2 per kWh generated by 65% due to the large share of renewable energies, mainly hydropower, which costs 1% of Gross Domestic Product. The obtained LCA footprints are similar to the literature benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

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Electric Monitoring System for Residential Customers Using Wireless Technology

2022 , Buele, Jorge , Morales-Sánchez J.C. , Varela Aldas, José , Palacios-Navarro G. , Ayala-Chauvin, Manuel Ignacio

Power grids continue to develop and it is increasingly difficult to guarantee the quality of service offered to the user. In several developing countries, consumption is calculated on the basis of visual inspection, which is prone to errors. Consequently, this document outlines the construction of electrical consumption telemetering equipment. This is designed to reduce human error through manual measures and have a web backup that can be accessed from anywhere. To develop the prototype voltage and current sensors are used, and the signal is conditioned for the control stage. The processing unit is the Arduino Mega embedded board, which incorporates a GPRS Shield (General Packet Radio Services) that handles communication with a LAMP server (Linux, Apache, MySQL, PHP) connected to the Internet. It also incorporates a block of connection and disconnection of the electrical service that would leave the whole house without service. Two functionalities are used to present the data, one is local on the LCD display of the equipment installed in the home (user) and the second is remote access to a website (server). The results show that in comparison with a standard voltage device it presents an error of 0.28% and 4.12% in current. In this way, the use of this prototype for real-time monitoring of electricity consumption is validated, since it works similarly to a conventional one. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

2023 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo F. , Buele, Jorge

Latin America has shown increased big data adoption since 2012; Ecuador is now entering this transformative field. Science and engineering in Ecuador have benefited most from data analysis, with untapped potential in health and services sectors. Big data is shaping sectors in Ecuador, including disaster prediction, agriculture, smart city development, and electoral data analysis. Despite public sector inefficiencies, residential ICT adoption provides opportunities for Ecuador’s smart city advancements. Despite some data underutilization, big data’s transformative potential is evident in Ecuador’s healthcare and education advancements. Highlights: Data analysis is increasingly critical in aiding decision-making within public and private institutions. This paper scrutinizes the status quo of big data and data analysis and its applications within Ecuador, focusing on its societal, educational, and industrial impact. A detailed literature review was conducted from academic databases such as SpringerLink, Scopus, IEEE Xplore, Web of Science, and ACM, incorporating research from inception until May 2023. The search process adhered to the PRISMA statement, employing specific inclusion and exclusion criteria. The analysis revealed that data implementation in Ecuador, while recent, has found noteworthy applications in six principal areas, classified using ISCED: education, science, engineering, health, social, and services. In the scientific and engineering sectors, big data has notably contributed to disaster mitigation and optimizing resource allocation in smart cities. Its application in the social sector has fortified cybersecurity and election data integrity, while in services, it has enhanced residential ICT adoption and urban planning. Health sector applications are emerging, particularly in disease prediction and patient monitoring. Educational applications predominantly involve student performance analysis and curricular evaluation. This review emphasizes that while big data’s potential is being gradually realized in Ecuador, further research, data security measures, and institutional interoperability are required to fully leverage its benefits. © 2023 by the authors.

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Big Data as a Tool for Analyzing Academic Performance in Education

2024 , Ayala-Chauvin, Manuel Ignacio , Chucuri-Real B. , ESCUDERO VILLA, PEDRO FERNANDO , Buele, Jorge

Educational processes are constantly evolving and need upgrading according to the needs of the students. Every day an immense amount of data is generated that could be used to understand children’s behavior. This research proposes using three machine learning algorithms to evaluate academic performance. After debugging and organizing the information, the respective analysis is carried out. Data from eight academic cycles (2014–2021) of an elementary school are used to train the models. The algorithms used were Random Trees, Logistic Regression, and Support Vector Machines, with an accuracy of 93.48%, 96.86%, and 97.1%, respectively. This last algorithm was used to predict the grades of a new group of students, highlighting that most students will have acceptable grades and none with a grade lower than 7/10. Thus, it can be corroborated that the daily stored data of an elementary school is sufficient to predict the academic performance of its students using computational algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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System for Monitoring and Controlling Industrial Lighting with Amazon Alexa

2021 , Ayala-Chauvin, Manuel Ignacio , Saá F. , Villarroel-Córdova F. , de la Fuente-Morato A.

Intelligent devices, used along with sensors, are becoming more commonplace in industrial contexts. One such device, Amazon Echo (which runs Amazon Alexa), can be used to interact with other industrial systems via voice commands. Taking advantage of this, a skill to control the illumination system of a company has been developed, while also being able to measure power consumption in real time. Besides Echo, the system employs easily obtainable electronic components such as NodeMCU4 and Sonoff Pow, while running open-source software like IDE Arduino and Amazon Developer. Besides the voice commands, the system can be controlled via a cell phone touch app and a manual system. Tests show the skill successfully controls the illumination system and provides accurate power consumption data in real time. This skill can also be replicated in other industrial contexts. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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