Examinando por Materia "Root mean square errors"

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    Ítem
    Design of computer experiments applied to modeling of compliant mechanisms for real-time control
    (SPRINGER, 2013-07-01) Acosta, Diego A.; Restrepo, David; Durango, Sebastian; Ruiz, Oscar E.; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de Procesos
    This article discusses the use of design of computer experiments (DOCE) (i.e., experiments run with a computer model to find how a set of inputs affects a set of outputs) to obtain a force-displacement meta-model (i.e., a mathematical equation that summarizes and aids in analyzing the input-output data of a DOCE) of compliant mechanisms (CMs). The procedure discussed produces a force-displacement meta-model, or closed analytic vector function, that aims to control CMs in real-time. In our work, the factorial and space-filling DOCE meta-model of CMs is supported by finite element analysis (FEA). The protocol discussed is used to model the HexFlex mechanism functioning under quasi-static conditions. The HexFlex is a parallel CM for nano-manipulation that allows six degrees of freedom (x, y, z, ? x, ? y, ? z ) of its moving platform. In the multi-linear model fit of the HexFlex, the products or interactions proved to be negligible, yielding a linear model (i.e., linear in the inputs) for the operating range. The accuracy of the meta-model was calculated by conducting a set of computer experiments with random uniform distribution of the input forces. Three error criteria were recorded comparing the meta-model prediction with respect to the results of the FEA experiments by determining: (1) maximum of the absolute value of the error, (2) relative error, and (3) root mean square error. The maximum errors of our model are lower than high-precision manufacturing tolerances and are also lower than those reported by other researchers who have tried to fit meta-models to the HexFlex mechanism. © 2012 Springer-Verlag London Limited.
  • No hay miniatura disponible
    Ítem
    Design of computer experiments applied to modeling of compliant mechanisms for real-time control
    (SPRINGER, 2013-07-01) Acosta, Diego A.; Restrepo, David; Durango, Sebastian; Ruiz, Oscar E.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    This article discusses the use of design of computer experiments (DOCE) (i.e., experiments run with a computer model to find how a set of inputs affects a set of outputs) to obtain a force-displacement meta-model (i.e., a mathematical equation that summarizes and aids in analyzing the input-output data of a DOCE) of compliant mechanisms (CMs). The procedure discussed produces a force-displacement meta-model, or closed analytic vector function, that aims to control CMs in real-time. In our work, the factorial and space-filling DOCE meta-model of CMs is supported by finite element analysis (FEA). The protocol discussed is used to model the HexFlex mechanism functioning under quasi-static conditions. The HexFlex is a parallel CM for nano-manipulation that allows six degrees of freedom (x, y, z, ? x, ? y, ? z ) of its moving platform. In the multi-linear model fit of the HexFlex, the products or interactions proved to be negligible, yielding a linear model (i.e., linear in the inputs) for the operating range. The accuracy of the meta-model was calculated by conducting a set of computer experiments with random uniform distribution of the input forces. Three error criteria were recorded comparing the meta-model prediction with respect to the results of the FEA experiments by determining: (1) maximum of the absolute value of the error, (2) relative error, and (3) root mean square error. The maximum errors of our model are lower than high-precision manufacturing tolerances and are also lower than those reported by other researchers who have tried to fit meta-models to the HexFlex mechanism. © 2012 Springer-Verlag London Limited.
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    Ítem
    Improvement of the signal-to-noise ratio in interferometry using multi-frame high-dynamic-range and normalization algorithms
    (ELSEVIER SCIENCE BV, 2012-01-01) Restrepo, R.; Uribe-Patarroyo, N.; Belenguer, T.; Universidad EAFIT. Departamento de Ciencias Básicas; Óptica Aplicada
    Using both high dynamic range (HDR) and normalization methodologies, we show a method to improve the fringe pattern contrast in interferometric measurements normally used for phase recovering. In a simulated interferogram that mimics the main effects that can be found in an interferometric process (stray-light, photon noise, electronic noise, scattering phenomena, etc.) it was possible to improve the contrast of the fringes and to decrease the root mean square error by more than 35%. The method proposed is applied to experimental interferograms to measure wavefront error and retardance changes on liquid crystal (LC) devices. It is done by using a Mach-Zehnder set-up in which we used different polarization areas. The proposed method increases the quality of the phase recovered and decreases the root mean square error by 50%. © 2011 Elsevier B.V. All rights reserved.
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    Ítem
    Spatiotemporal Modeling of Urban Growth Using Machine Learning
    (MDPI AG, 2019-12-28) Duque, J.; Jorge E. Patino; Gomez, J.; Passos, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson's correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities. © 2019 by the authors.