Person:
Auquilla Sangolquí, Andrés Vinicio

Birth Date

1980-08-25

ORCID

0000-0002-3754-041X

Scopus Author ID

56294185800

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ingeniería, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ciencias de la Computación, Cuenca, Ecuador

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ingeniería

La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

Job Title

Profesor (T)

Last Name

Auquilla Sangolquí

First Name

Andrés Vinicio

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Now showing 1 - 10 of 10

Combining occupancy user profiles in a multi-user environment: An academic office case study
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-09-14) Auquilla Sangolquí, Andrés Vinicio
In a worldwide context, space heating is the largest energy consumer in commercial buildings, it accounts for 35% of the total energy consumed in the US. Energy efficient thermostats, that learn occupancy patterns and user preferences, haven been studied in literature. However, they are oriented to single-user environments, therefore, they are not applicable in offices where several users interact, i.e. multi-user environments. To expand the single-user techniques in order to cope with multi-user environments, two methods are proposed to derive the user's expected temperatures demands based on their occupancy profiles and individual preferences in terms of desired temperature and tolerance. This paper presents the implications of the implementation of such techniques by means of a case study of two users in an academic office. We observed that the proposed methods reduced the operational time up to 33% compared to a reference fixed schedule of 12 hours while maintaining user comfort. In conclusion, smart thermostats can also reduce energy consumption in multi-user environments while guaranteeing individual user expectations.
A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index
(SPRINGER VERLAG, 2014-06-30) Auquilla Sangolquí, Andrés Vinicio; Vanegas Peralta, Pablo Fernando
In an Object Based Image Analysis Classification (OBIA) process, the quality of the classification results are highly dependent on segmentation. However, a high number of the studies that make use of an OBIA process find the segmentation parameters by making use of trial-and-error methods. It is clear that a lack of a structured procedure to determine the segmentation parameters produces unquantified errors in the classification. This paper aims to quantify the effects of using a semi-automatic approach to determine optimal segmentation parameters. To this end, an OBIA process is performed to classify land cover types produced by both a manual and an automatic segmentation. Even though the classification using the manual segmentation outperforms the automatic segmentation, the difference is only 2%. Since the automatic segmentation is performed with optimal parameters, a procedure to accurately determine those parameters must be performed to minimize the error produced by a misjudgment in the segmentation step. © 2014 Springer International Publishing.
Using Time-Driven Activity-Based Costing to Identify Best Practices in Academic Libraries
(Elsevier, 2016-05-01) Sigüenza Guzmán, Lorena Catalina; Auquilla Sangolquí, Andrés Vinicio
In the current competitive and dynamic environment, libraries must remain agile and flexible, as well as open to new ideas and ways of working. Based on a comparative case study of two academic libraries in Belgium, this research study investigates the opportunities of using Time-Driven Activity-Based Costing (TDABC) to benchmark library processes. To this end, two major research questions are addressed: 1) Can TDABC be used to enhance process benchmarking in libraries? 2) Do results at activity level provide additional insights compared to macro results in a process benchmarking? We first start by describing the TDABC implementation. Then, we discuss and compare the workflow of 10 library processes covering the four principal library functions: acquisition, cataloging, circulation and document delivery. Next, based on the benchmarking exercise, we report and discuss potential processes and performance improvements that can be realized from using library time and costs information, in particular concerning the two libraries analyzed. We conclude this article by discussing the advantages of using TDABC as a tool to enhance process benchmarking in libraries.
Impact reduction potential by usage anticipation under comfort trade-off conditions
(ELSEVIER USA, 2016-01-01) Auquilla Sangolquí, Andrés Vinicio
Well-optimized intelligent control of products and systems with a substantial energy and/or consumables demand can allow to reduce the use phase impact of these devices and systems significantly. However, depending on the usage patterns and their variability, the system efficiency and tardiness, as well as comfort-impact avoidance trade-off considerations, the effectiveness of such strategies can greatly differ. This contribution describes models for and analyses the sensitivity of the achievable impact reduction with respect to these factors, thus facilitating use phase oriented eco-design decision making. The observations are illustrated by means of a zone heating and a laser cutting machine case study.
Intelligent occupancy-driven thermostat by dynamic user profiling
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-09-06) Auquilla Sangolquí, Andrés Vinicio
Matching system functionality and user needs by learning from user behaviour enables a significant reduction in energy consumption. Habits and routine behaviour are exploited and captured in user profiles to automatically create customized heating schedules. However, over time the user conduct can change either gradually or abruptly and old occupancy patterns could become obsolete. Hence, a self-learning system should be able to cope with these changes and adapt the identified user profiles accordingly. An approach to track changing behaviour and update the corresponding user profiles, and hence heating schedules, is presented. The proposed strategy is evaluated by comparing prediction accuracy and potential energy savings to the case where learning is static and to incremental learning strategies. The results are illustrated by means of a real-life dataset of a single-user office.
Improving cluster-based methods for usage anticipation by the application of data transformations
(Elsevier BV, 2018) Auquilla Sangolquí, Andrés Vinicio; De bock, Yannick; Duflou, Joost r
Abstract The wide adoption of Internet of Things (IoT) infrastructure in recent years has allowed capturing data from systems that make intensive use of electrical power or consumables typically aiming to create predictive models to anticipate a system’s demand and to optimize system control, assuring the service while minimizing the overall consumption. Several methods have been presented to perform usage anticipation; one promising approach involves a two step procedure: profiling, which discovers typical usage profiles; and, prediction that detects the most likely profile given the current information. However, depending on the problem at hand, the number of observations to characterize a profile can increase greatly, causing high dimensionality, thus complicating the profiling step as the amount of noise and correlated features increase. In addition, the profile detection uncertainty increases, as the cluster intra-variability becomes larger and the distances between the centroids become similar. To overcome the difficulties that a usage profile with high dimensionality poses, we developed a methodology that finds the intrinsic dimensionality of a dataset, containing binary historical usage data, by performing dimensionality reductions to improve the profiling step. Then, the profile detection step makes use of the transformed actual data to accurately detect the current profile. This paper describes the implementation details of the application of such techniques by the analysis of two use cases: (1) usage prediction of a laser cutter machine; and, (2) occupancy prediction in an office environment. We observed that the dataset dimensionality and the cluster intra-variability was greatly reduced, making the profile detection less prone to errors. In conclusion, the implementation of methodologies to enhance the separability of the original data by dimensionality transformations improves the profile discovery and the subsequent actual profile detection.
Nonparametric user activity modelling and prediction
(2020) Nowé, Ann; De Bock, Yannick; Duflou, Joost R; Auquilla Sangolquí, Andrés Vinicio
Modelling the occupancy of buildings, rooms or the usage of machines has many applications in varying fields, exemplified by the fairly recent emergence of smart, self-learning thermostats. Typically, the aim of such systems is to provide insight into user behaviour and incentivise energy savings or to automatically reduce consumption while maintaining user comfort. This paper presents a nonparametric user activity modelling algorithm, i.e. a Dirichlet process mixture model implemented by Gibbs sampling and the stick-breaking process, to infer the underlying patterns in user behaviour from the data. The technique deals with multiple activities, such as , of multiple users. Furthermore, it can also be used for modelling and predicting appliance usage (e.g. ). The algorithm is evaluated, both on cluster validity and predictive performance, using three case studies of varying complexity. The obtained results indicate that the method is able to properly assign the activity data into well-defined clusters. Moreover, the high prediction accuracy demonstrates that these clusters can be exploited to anticipate future behaviour, facilitating the development of intelligent building management systems. © 2020, Springer Nature B.V.
The energy saving potential of retrofitting a smart heating system: a residence hall pilot study
(2021) Duflou, Joost R.; De Bock, Yannick; Auquilla Sangolquí, Andrés Vinicio; Bracquené, Ellen; Nowé, Ann
Energy conservation is of increasing importance in contemporary society. A large fraction of energy end-use can be attributed to space conditioning. Therefore, intelligent control systems were devised and commercialised in the form of smart thermostats. Hereto, the availability of occupancy information is essential such that heating and/or cooling schedules can be tailored to user needs. This way energy savings can be obtained without jeopardising user satisfaction. However, preceding studies generally rely on simulations to estimate the potential reduction in energy consumption. This work aims at quantifying the potential based on a real life experiment. The development of a smart heating system is presented along with the results of an actual field test of retrofitting this system in 14 single-user student rooms of a university residence hall. An experiment was conducted in which the heating was automatically steered for 1 week (26 March 2018–01 April 2018). Total energy savings range between 26.9% and 59.5% and calculated thermal comfort was not significantly affected by the autonomous control. Furthermore, an environmental impact reduction of 3.2 to 12.9 EcoPoints is estimated for the controlled week, resulting in a reduction of 37.5 to 150.2
Plataforma para análisis de mercado a través de datos de redes sociales
(2021) Fajardo Cárdenas, Ángel Patricio; Bravo Chuqui, Néstor Ariel; Auquilla Sangolquí, Andrés Vinicio; Vanegas Peña, Paúl Fernando
Nowadays, and more with the pandemic, e-commerce is becoming the predominant way of marketing products and services in the world. A study conducted by the Ecuadorian Chamber of Electronic Commerce in 2020 shows that purchases and sales through digital channels has increased at least 15 times since the beginning of the pandemic. Therefore, to conduct market research companies must seek new ways to extract information and then then carry out its analysis and thus obtain competitive advantage. Data extraction is a complex and not very scalable process; therefore, this research presents a methodology for the extraction of information from a given industrial sector. The methodology consists of two fundamental steps, first, a ranking of the main sources of information available and most used in the country in a given industry sector is made, several characteristics and expert opinion are considered. Second, a platform is proposed that integrates the best ranked information sources and performs data extraction. Finally, these data are presented in a Dashboard with the availability to be downloaded and used in subsequent studies It is concluded that the 4 platforms that offer the greatest benefit for this research are: Google Trends, Facebook, YouTube and Twitter. There are also sources of information that have a high rating when applying the proposed analysis, however, data extraction is difficult due to their security policies.
A Methodological Framework for Creating Large-Scale Corpus for Natural Language Processing Models
(Springer, 2021) Santos Leon, David Enrique; Auquilla Sangolquí, Andrés Vinicio; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Santos Leon, David Enrique
Currently, there is a boom in introducing Machine Learning models to various aspects of everyday life. A relevant field consists of Natural Language Processing (NLP) that seeks to model human language. A key and basic component for these models to learn properly consists of the data. This article proposes a methodological framework for constructing a large-scale corpus to feed NLP models. The development of this framework emerges from the problem of finding inputs in languages other than English to feed NLP models. With an approach focused on producing a high-quality resource, the construction phases were designed along with the considerations that must be taken. The stages implemented consist of the corpus characterization to be obtained, collecting documents, cleaning, translation, storage, and evaluation. The proposed approach implemented automatic translators to take advantage of the vast amount of English literature and implemented through non-cost libraries. Finally, a case study was developed, resulting in a corpus in Spanish with more than 170,000 documents within a specific domain, i.e., opinions on textile products. Through the evaluations carried out, it is established that the proposed framework can build a large-scale and high-quality corpus.