Examinando por Materia "pattern recognition"
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Ítem Analysis of periodic structures with Fourier description and neuronal network(IMPRENTA UNIV ANTIOQUIA, 2007-06-01) DIAZ, ADALBERTO GABRIEL; Gabriel Diaz, Adalberto; Universidad EAFIT. Departamento de Ingeniería de Producción; Ingeniería, Energía, Exergía y Sostenibilidad (IEXS)This work is developed in a project of textile lattices inspection. The structure of a superficial texture is manifested with a behavior on the base of a model known as pattern which, is associated with a group of characteristics that define it as such. The identification process and classification of shortcomings in the texture consists on establishing a region of conformity in the coordinated space defined by the pattern's characteristics. The reduction process of this m-dimensional space, helps to its identification in an n-dimensional space, such that m > n, where the classification system actually depends on the characteristics of the new space, where the new characteristics truly contain the classification information. The space characteristics allow for the identification of the pattern as such in the place that is explored. The characteristic frequency corresponds to a reduction of the classification space, making it more generic in the area over the image. The classification system is modeled with neuronal networks algorithms and the complexity of the surfaces of decision is solved starting from the architecture and the algorithms of training of the neuronal net.Ítem ¿Es posible tener vehículos más automatizados y seguros?(Universidad EAFIT, 2020-12-01) Martinez Guerrero, Christian Alexander; Martinez-Guerrero, Christian Alexander; Cordero, Jorge; Aguilar, Jose; Aguilar, Kristell; Chavez, Danilo; Puerto, E; GIDITICÍtem 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 ComunicacionesThis 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.