Person:
Peña Ortega, Mario Patricio

Birth Date

1987-12-26

ORCID

0000-0002-3986-7707

Scopus Author ID

57202190504

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ciencias Químicas, Cuenca, Ecuador

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ciencias Químicas

Fundada en 1955 como la Escuela de Química Industrial, la facultad ha sido un pilar fundamental en la formación de profesionales altamente capacitados, comprometidos con el desarrollo de la ciencia, la educación y el bienestar social. La Facultad de Ciencias Químicas pone a consideración su trabajo académico, investigativo y de vinculación con la sociedad, desarrollado a través de la práctica de una docencia de calidad, investigación e innovación en su área de estudio. Desde su oficio de conocimiento se permite contribuir a la sociedad con cuatro carreras: Bioquímica y Farmacia, Ingeniería Química, Ingeniería Ambiental e Ingeniería Industrial. Su carta de presentación en la Academia, la coloca como una dependencia dinámica, donde confluye la solidez de una trayectoria de más de sesenta años. Aquí se trabaja en una continua formación de pregrado y posgrado de la más alta calidad, mediante la mejora continua con la innovación y a la vanguardia de las ciencias químicas.

Job Title

Profesor (T)

Last Name

Peña Ortega

First Name

Mario Patricio

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Now showing 1 - 10 of 28

Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
(2018) Peña Ortega, Mario Patricio; Cerrada, Mariela; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo M; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Sánchez, René Vinicio
The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of data. First, the framework uses ANOVA combined with Tukey's test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. © 2018 - IOS Press and the authors. All rights reserved.
Gráficas de trayectorias escolares de estudiantes de bachillerato
(2021) Illescas Peña, Lourdes Eugenia; Bravo Guerrero, Fabián Eugenio; Peña Ortega, Mario Patricio; Bojorque Pazmiño, Miriam Eliana
For school authorities it is important to know how the students of the institution are progressing in their studies, because in this way they can detect any type of difficulties that arise, however, that information is not always known in time. The objective of this research is to propose a methodology that allows to visualize school trajectories of the students and in this way identify the difficulties that students have in mathematics. To show the potential of the methodology, high school students from an Ecuadorian educational institution were investigated between 2015 and 2019, their grades were processed and graphs of school trajectories were generated, which present the evolution of their grades during the school period. Through this methodology, the identification of individual trajectories of low performance is facilitated, in addition, it was found that collaborative activities have higher scores than individual ones, performance differences between courses were also evidenced. The presentation of grades through graphs is a powerful tool for academic management since it facilitates the detection of difficulties and the making of timely decisions.
Fast feature selection based on cluster validity index applied on data-driven bearing fault detection
(Institute of Electrical and Electronics Engineers Inc., 2020) Cabrera, Diego; Sánchez, René Vinicio; Peña Ortega, Mario Patricio; Cerrada, Mariela
The Prognostics and Health Management (PHM) approach aims to reduce potential failures or machine downtime by determining the system state through the identification of the signals changes produced by the system's faults. Machine learning (ML) approaches for fault diagnosis usually have high-dimensional feature space that can be obtained from signal processing. Nevertheless, as more features are included in the ML algorithms the processing time increases, there is a tendency for overfitting, and the performance may even decrease. Feature selection has multiple goals including building more simple and comprehensible models, improving the performance on ML algorithms, and preparing clean and understandable data. This paper proposes a methodological framework based on a cluster validity index (CVI) and Sequential Forward Search (SFS) to select the best subset of features applied on the problem of fault severity classification in rolling bearing. The results show that a perfect classification can be obtained with KNN with at least six selected features.
A hybrid algorithm for supply chain optimization of assembly companies
(IEEE, 2019) Cevallos Tapia, Carlos Patricio; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Peña Ortega, Mario Patricio
A fundamental goal of any system is to get an optimal state. These optimal states can be found in different areas, such as medicine, engineering, or architecture. In the field of industrial engineering, one of its objectives is improving or optimizing company processes in order to increase benefits while reducing costs. In this context, an essential component is the supply chain, which is a network in that different entities, such as manufacturers, suppliers, distributors, retailers, transporters, and customers or end-users, are associated. Several optimization algorithms with different approaches have been developed to optimize the supply chain. Nevertheless, they still have problems to fulfill some requirements at once. This research aims to develop a hybrid optimization algorithm that leverages the capabilities of different approaches. This algorithm, which presents a multi-objective optimization schema, meets a tradeoff between the optimization results quality and the runtime. To this end, a manufacturing and assembly company is used as a case study to prove the algorithm. The results are also compared with other state-of-the-art algorithms using the same execution environment and general settings. Findings indicate that the hybrid algorithm converges in less time and in most cases, it could reach the global optimal.
Assessment of supply chain performance in an assembly company: evaluation of evolutionary algorithms
(Springer, Singapore, 2020) Orellana Ordoñez, Josselin Jimena; Peña Ortega, Mario Patricio; Llivisaca Villazhañay, Juan Carlos
In current globalized markets, companies no longer compete with each other. They now compete with the supply chains (SC) to which they belong. SC optimization allows efficient and effective management of resources. In many cases, optimization goals can conflict with one another. Therefore, the purpose of this work was to evaluate SC performance by comparing three optimization algorithms in a case study with multiple objectives. Two objectives are maximizing profit and maximizing the level of customer service. Also, the modeled problem considers multiple products and periods for two security inventory scenarios (maximum and minimum inventory levels). Evolutionary algorithms were compared: NSGA-II, MOPSO, and MOMA. The NSGA-II algorithm obtained the best result. With a minimum inventory level, NSGA-II presented 97.87% service level and the best benefit. Results show the importance of SC management and its optimization as well as some relevant variables to be considered.
Inventory management for retail companies: a literature review and current trends
(IEEE Xplorer, 2021) Muñoz Macas, Cinthya Vanessa; Espinoza Aguirre, Jorge Andres; Arcentales Carrión, Rodrigo Nicanor; Peña Ortega, Mario Patricio
In recent years, the correct management of inventories has become a fundamental pillar for achieving success in enterprises. Unfortunately, studies suggesting the investment and adoption of advanced inventory management and control systems are not easy to find. In this context, this article aims to analyze and present an extensive literature concerning inventory management, containing multiple definitions and fundamental concepts for the retail sector. A systematic literature review was carried out to determine the main trends and indicators of inventory management in Small and Medium-sized Enterprises (SMEs). This research covers five years, between 2015 and 2019, focusing specifically on the retail sector. The primary outcomes of this study are the leading inventory management systems and models, the Key Performance Indicators (KPIs) for their correct management, and the benefits and challenges for choosing or adopting an efficient inventory control and management system. Findings indicate that SMEs do not invest resources in sophisticated systems; instead, a simple Enterprise Resource Planning (ERP) system or even programs such as Excel or manual inventories are mainly used.
Demand forecasting for textile products using machine learning methods
(Springer International Publishing, 2022) Medina Samaniego, Héctor Wilfrido; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Guamán Guachichullca, Noé Rodrigo
Due to its close relationship with various operational decisions, market demand forecasting has been considered one of the essential activities in all organizations. Unfortunately, the textile industry has been the most difficulty generating forecasts, mainly due to the volatility caused in the market by short product life cycles, special events, and competitions. From the beginning, forecasting has been using traditional statistical methods. However, the increasing use of artificial intelligence has opened a new catalog of prediction methods currently being studied for their high precision. This study explores machine learning (ML) as a tool to generate forecasts for the textile industry, applying regression-focused algorithms such as Linear Regression, Ridge, Lasso, K-nearest neighbor, Support Vector Regression (SVR), and Random Forest (RF). To this end, time series were used as inputs for the models, supported by external variables such as Google Trends and special events. The results show that ML as a prediction method has higher precision than purely statistical basic prediction models
Learning analytics, dashboard for academic trajectory
(CEUR-WSceurws@sunsite.informatik.rwth-aachen.de, 2019) Peña Ortega, Mario Patricio; Illescas Peña, Lourdes Eugenia; Bravo Guerrero, Fabián Eugenio
© 2019 CEUR-WS. All rights reserved. In the context of university academic management, several proposals have been developed for the study of analysis and visualization of learning trajectories. Bearing in mind that the educational trajectory is the trajectory of the student traveled at a given time from entry to the end of the stay, it can be considered that the use of technology could extract or highlight relevant information that is not seen directly with the tools traditional The visualization of data in educational environments has become a challenge due to the large amounts of information available. The responsibilities of educational administrators require a clear visual proposal adapted to queries based on an academic context. Therefore, it was proposed to generate a tool that generates dashboard based on relevant variables of the students. To do this, the proposal began with a review of the literature that helped analyze the different ways of visualizing the data of academic trajectories. Subsequently, a dynamic visualization was formulated to explain the teachers and authorities through the learning analysis panel, based on the use of parallel coordinates that present multidimensional data over time. The sample was constituted by records of 1975 students of an Ecuadorian university, of the cohort that began in March 2013, distributed by faculties and careers. The technique used allowed us to discover trends and relationships between dimensions, improving the understanding of the trajectory patterns of students, the trends of school dropout, either increase or decrease performance, among other relationships. The consultations allowed to filter data by variables such as: faculties, careers, students and intervals of scores. Finally, the validation of the proposal was made based on the relevance of the dashboard, in response to the inquiries of the academic manager.
Optimization models used in the textile sector: a systematic review
(Springer Science and Business Media Deutschland GmbH, 2022) Llivisaca Villazhañay, Juan Carlos; Veintimilla Reyes, Jaime Eduardo; Sigüenza Guzmán, Lorena Catalina; Torres Torres, Christian Marcelo; Toledo Illescas, María Belén; Peña Ortega, Mario Patricio
In recent years, several works have been published dedicated to obtaining optimization models. Many of them have been applied in the textile sector because they are part of the economic development areas of a country. This article’s main objective is to review the literature published on optimization models and understand what methods their authors used to solve the optimization problems in the textile sector. A systematic methodology was applied to select research questions, digital databases, and search terms to utilize practical and methodological filters later to carry out this systematic review. This procedure allowed performing a review and synthesis of the results obtained on the optimization models. It was found that the models resulting from the systematic review vary depending on the areas to be optimized. The most frequent applications were logistics and production, followed by cost minimization. They were optimized mainly with linear programming, integer programming, Markov chains, genetic algorithms, and multi-objective programming
Red neuronal para clasificación de riesgo en cooperativas de ahorro y crédito
(UNIVERSIDAD DE LAS FUERZAS ARMADAS ESPE, 2018) Peña Ortega, Mario Patricio; Orellana Parapi, Jose Miguel; Peña Ortega, Mario Patricio
In Ecuador exists a great number of credit unions (COAC) specifically 852, which are divided into 5 segments depending on their amount of assets. Nowadays, 66% of the microcredit obtained within the country corresponds to the cooperative system. Nevertheless, just 35 of 58 COAC (segments 1 and 2) present risk rating histories. The purpose of this research is create a neural network that achieves an acceptable percentage of accuracy, to classify a COAC within a scale of risk based on the value of its financial indexes; in order to helping the early detection of future problems. The artificial neural network (ANN) was fitted from data obtained through the Public and solidary economy Superintendence for COAC that presented a high index of assets. In addition, the history of quarterly risk ratings generated by rating agencies in the same period was used: January 2015 - September 2017. An ANN with a classification accuracy of 79.59% was obtained, percentage that is within the range of precision obtained by studies reviewed for classification activities in financial entities. The classification results could be further improved with the use of a hierarchical classification structure.