Artículos (GIDITIC)

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https://hdl.handle.net/10784/7942

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Examinando Artículos (GIDITIC) por Autor "Aguilar, Jose"

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    Ítem
    Adaptive System for the Generation of Emerging Behaviors in Serious Emerging Games
    (Instituto Politecnico Nacional, 2020-01-01) Aguilar, Jose; Altamiranda, Junior; Diaz, Francisco; Aguilar, Jose; Altamiranda, Junior; Diaz, Francisco; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    A video game adaptation system (SAV) for serious emergent games (JSE), allows emergent behaviors in the game, such as the appearance of environments, events, narratives and characters, among others, in order to adapt to the context in the one that is developing. In previous articles the architecture of a JSE engine has been proposed. Furthermore, a first subsystem has been proposed that allows the emergence of a JSE according to the objectives of the environment, based on the ant colony optimization algorithm (ACO). In the present work, the second component of said architecture is specified, the SAV, which allows its dynamic adaptation (during the JSE). The SAV is made up of the sub-layers of strategies, sequences and properties, which manage each of these types of possible emergencies in a JSE, with the intention of dynamically adapting it to the context-domain where the game is being played. Furthermore, in this work the behavior of these sublayers is analyzed in a specific case study, showing very encouraging results of SAV in the educational context of an intelligent classroom (SaCI).
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    Ítem
    An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA
    (Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Morales, Luis; Aguilar, Jose; Morales, Luis; Aguilar, Jose; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) >98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.
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    Ítem
    Recognition of the Driving Style in Vehicle Drivers
    (MDPI AG, 2020-05-01) Cordero, Jorge; Aguilar, Jose; Aguilar, Kristell; Chavez, Danilo; Puerto, Eduard; Cordero, Jorge; Aguilar, Jose; Aguilar, Kristell; Chavez, Danilo; Puerto, Eduard; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.