Examinando por Materia "Multivariant analysis"

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    Modeling and control of nonlinear systems using an Adaptive LAMDA approach
    (Elsevier BV, 2020-01-01) Morales L.; Aguilar J.; Rosales A.; Chávez D.; Leica P.; Morales L.; Aguilar J.; Rosales A.; Chávez D.; Leica P.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This paper presents a soft computing technique for modeling and control of nonlinear systems using the online learning criteria. In order to obtain an accurate modeling, and therefore a controller with good performance, a method based on the fundamentals of the artificial intelligence algorithm, called LAMDA (Learning Algorithm for Multivariate Data Analysis), is proposed, with a modification of its structure and learning method that allows the creation of an adaptive approach. The novelty of this proposal is that for the first time LAMDA is used for fuzzy modeling and control of complex systems, which is a great advantage if the mathematical model is not available, partially known, or variable. The adaptive LAMDA consists of a training stage to establish initial parameters for the controller, and the application stage in which the control strategy is computed and updated using an online learning that evaluates the closed-loop system. We validate the method in several control tasks: (1) Regulation of mixing tank with variable dead-time (slow variable dynamics), (2) Regulation of a Heating, Ventilation and Air-Conditioning (HVAC) system (multivariable slow nonlinear dynamics), and (3) trajectory tracking of a mobile robot (multivariable fast nonlinear dynamics). The results of these experiments are analyzed and compared with other soft computing control techniques, demonstrating that the proposed method is able to perform an accurate control through the proposed learning technique. © 2020 Elsevier B.V.
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    Preliminary geochemical study of thermal waters at the Puracé volcano system (South Western Colombia): An approximation for geothermal exploration
    (Universidad Industrial de Santander, 2018-01-01) Gómez-Díaz E.; Marin-Cerón M.I.; Gómez-Díaz E.; Marin-Cerón M.I.; Universidad EAFIT. Departamento de Ciencias; Geología Ambiental y Tectónica
    The Puracé Volcano is located in the Cauca department, SW of Colombia, along the Coconucos volcanic chain. This volcano is an interesting target for geothermal exploration, because it is a young caldera-type volcano, with thermal activity (e.g. hot springs and fumaroles). Using hydro-geochemical analyses of hot springs, we determine the type of water, origin and relation with the geothermal system, reservoir temperature, mixing process and finally the potential areas for future exploration. The analyzed water-types are bicarbonate, dilutechloride, sulphate-chloride, acid-sulphate and heated steam-acid sulfated. The conservative elements, allow to identify the correlation between different springs and to infer commune sources. Moreover, the applied solutes geothermometers for each suitable thermal-water group were used to estimate the reservoir temperature. The Silica geothermometers resulted within a range of 120°C -170°C while those the Cation geothermometers are above these temperatures reflecting values from 160°C to 220°C. However, the Cation geothermometer of low temperature clearly identify another zone of lower temperature. Mixing and recharge processes, were identified through of stable isotopes. Finally, the preliminary geothermal model shows two zones of high enthalpy system ( > 150°C). © 2018, Boletín de Geología.
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    Towards an empirical model of the UX: A factor analysis study
    (Universidad de los Andes, 2014-01-01) Ariza, N.; Maya, J.
    One of the key factors to bear in mind when it comes to developing innovative products is a satisfying and pleasurable user experience (UX). Although there are many models and definitions of UX, most of them have been proposed intuitively. From themes and elements previously identified as essential for an UX model, a study of exploratory factor analysis (EFA ) was performed. We found eight factors in Before-UX-stage, 7 in During-UX and 17 in After-UX stage. Similar UX aspects are expressed in different ways trough the different stages and factors. There are differences in terms of how a factor could weight and relate to others regarding the UX-stage. Moreover, while some factors are consistent with the literature, others are new. Most of the factors give us an idea about the articulation of variables and the way some factors relate to others.