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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Prevention of Failures in the Footwear Production Process by Applying Machine Learning

2022 , Tierra-Arévalo M. , Ayala-Chauvin, Manuel Ignacio , Nacevilla C. , de la Fuente-Morato A.

At present, the handcrafted footwear sector is affected by the high competitiveness due to the increasing automation of companies. In this sense, in order to improve its competitiveness, a system was proposed to predict the failures of a production system and to carry out preventive maintenance actions. Samples were taken from 25 productions and 7 activities were established: cutting, stitching, pre fabrication, final preparation, gluing, assembly and finishing. The company produces batches of 90 pairs per day, with a standard time of 274.53 min and a promised productivity of 1.8. A support vector machine model was developed to predict the possible failures of the process taking as a reference the standard time of each stage. Finally, the results allow predicting the faults to optimise the production process by applying Support Vector Machine (SVM). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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IoT Monitoring to Control a Bicycle Parking Lot

2022 , Ruales, María Belén , Lara-Alvarez P. , Riba C. , Ayala-Chauvin, Manuel Ignacio

In recent years, the development of new technologies has improved the management of resources and services at the urban level. In this sense, several cities worldwide have developed intelligent infrastructures such as Smart Cities in which, through data collection and management, they aim to achieve social, environmental and economic improvements. Innovative bike racks are a promising solution to traffic-related problems in major cities around the world; however, there is a lack of low-cost solutions for controlling and monitoring bike racks and thus boosting the mobility of cyclists. This paper presents a system to monitor and control a bicycle parking lot. In order to achieve this goal, software and hardware specifications were defined and characterised by the control system. The conceptual design and detail of the prototype and the materialisation proceeded, where technology with ESP8266 microcontrollers and Raspberry Pi+Ethernet/WiFi microprocessors was used in the MQTT communication protocol to implement its architecture. The system implements in the bicycle parking lot of the Universidad Tecnológica Indoamérica. The series of data collected allowed for determining the frequency of use. With this, a database creates where the information on the frequency of use of bicycles is stored. Finally, through a mobile application, the availability of parking spaces can be consulted, and bikes in the parking lot can monitor. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Optimizing Natural Language Processing: A Comparative Analysis of GPT-3.5, GPT-4, and GPT-4o

2024 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima

In the last decade, the advancement of artificial intelligence has transformed multiple sectors, with natural language processing standing out as one of the most dynamic and promising areas. This study focused on comparing the GPT-3.5, GPT-4 and GPT-4o language models, evaluating their efficiency and performance in Natural Language Processing tasks such as text generation, machine translation and sentiment analysis. Using a controlled experimental design, the response speed and quality of the outputs generated by each model were measured. The results showed that GPT-4o significantly outperforms GPT-4 in terms of speed, completing tasks 25% faster in text generation and 20% faster in translation. In sentiment analysis, GPT-4o was 30% faster than GPT-4. Additionally, analysis of response quality, assessed using human reviews, showed that while GPT-3.5 delivers fast and consistent responses, GPT-4 and GPT-4o produce higher quality and more de-tailed content. The findings suggest that GPT-4o is ideal for applications that require speed and consistency, while GPT-4, although slower, might be preferred in contexts where text accuracy and quality are important. This study highlights the need to balance efficiency and quality in the selection of language models and suggests implementing additional automatic evaluations in future research to complement the current findings.

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Experimental Validation of a Kinematic Control Strategy for Trajectory Tracking in Quadruped Robots

2025 , Carvajal, Christian , Varela Aldas, José , Víctor H. Andaluz , Chicaiza Claudio, Fernando , Ayala-Chauvin, Manuel Ignacio

This work presents a high-level control architecture for trajectory tracking in quadruped robots. The proposed controller is based on the motion kinematics of the robot's center of mass (CoM). The proposed strategy transforms planned trajectories in Cartesian space into motion velocity commands for the robot, using a differential kinematic model that relates the velocity of the robot's operational point to its velocity in the XY-plane. The control scheme is organized hierarchically, where the kinematic controller operates independently from the system dynamics, which are handled by low-level controllers. The proposed control architecture is experimentally validated using the Unitree Go2 quadruped robot, employing MATLAB and ROS2 tools. The results confirm the feasibility of using purely kinematic models for high-level locomotion task control under real-world operating conditions. © 2025 IEEE.

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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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Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems

2025 , Darío Muyulema-Masaquiza , Ayala-Chauvin, Manuel Ignacio

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R2 of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure.

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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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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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Preface

2022 , Ayala-Chauvin, Manuel Ignacio , Botto-Tobar M. , Cadena Á.D. , León S.M.

[No abstract available]

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