Browsing by Title Facultad de Ciencias Físicas y Matemáticas

Now showing items 4100-4119 of 20545

    • Decomposition of 2D polygons and its effect in hydrological models 
      Sanzana, P.; Gironás, J.; Braud, I.; Hitschfeld, N.; Branger, F.; Rodriguez, F.; Fuamba, M.; Romero, J.; Vargas, X.; Muñoz, J. F.; Vicuña, S.; Mejía, A. (IWA Publishing, 2019)
      © IWA Publishing 2019.2D non-uniform polygonal meshes allow representation of the impact of landscape elements and small infrastructures on water flows. The initial vectorial mesh, derived from the intersection of several ...
    • Deconstructing the Leaching Ratio 
      Estay, Humberto; Díaz Quezada, Simón (Springer, 2020)
      The heap leaching process has been widely used for recovering different metals since its first application at the end of the 1960s. In Chile, copper production via heap leaching has accounted for between 30 and 40% of ...
    • Deconstruction of the green alga Ulva rigida in ionic liquids: Closing the mass balance 
      Pezoa Conte, R.; Leyton, A.; Anugwom, I.; Schoultz, S. von; Paranko, J.; Mäki Arvela, P.; Willför, S.; Muszyński, M.; Nowicki, J.; Lienqueo Contreras, María Elena; Mikkola, J. P. (Elsevier, 2015)
      Algae are known to grow at high rates compared to terrestrial plants that contain comparable amounts of carbohydrates by weight. Therefore, this renders them attractive in terms of any biorefinery concept. In this work the ...
    • Deconvolución en audio utilizando modelos basados en Machine Deep Learning 
      León González, Diego Javier (Universidad de Chile, 2021)
      El problema de dereverberación es un problema con gran potencial de investigación en ciencias e ingeniería. El problema puede modelarse como uno de deconvolución y existe una vasta cantidad de modelos que buscan abordarlo. ...
    • Deep Exploration of Eridani with Keck Ms-band Vortex Coronagraphy and Radial Velocities: Mass and Orbital ... 
      Mawet, Dimitri; Hirsch, Lea; Lee, Eve J.; Ruffio, Jean Baptiste; Bottom, Michael; Fulton, Benjamin J.; Absil, Olivier; Beichman, Charles; Bowler, Brendan; Bryan, Marta; Choquet, Elodie; Ciardi, David; Christiaens, Valentin; Defrère, Denis; Gómez González, Carlos (Institute of Physics Publishing, 2019)
      We present the most sensitive direct imaging and radial velocity (RV) exploration of Eridani to date. Eridani is an adolescent planetary system, reminiscent of the early solar system. It is surrounded by a prominent and ...
    • Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings 
      Verstraete, David; Ferrada, Andrés; López Droguett, Enrique; Meruane Naranjo, Viviana; Modarres, Mohammad (Hindawi Ltd., 2017)
      Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty ...
    • Deep learning health state prognostics of physical assets in the Oil and Gas industry 
      Figueroa Barraza, Joaquín Eduardo; Guarda Brauning, Luis; Benites Pérez, Ruben; Bittencourt Morais, Carlos; Ramos Martins, Marcelo; López Droguett, Enrique (Sage, 2020)
      Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies ...
    • Deep Learning HUB: a novel graphical programming platform for big machinery data analytics in reliability ... 
      Pishahang, Mohammad Hossein (Universidad de Chile, 2020)
      La tendencia global hacia la Industria 4.0 y el Internet de las cosas industrial (IoT), además de la proliferación de tecnología de detección de bajo costo, ha empujado al panorama de la Gestión de Activos Físicos más que ...
    • Deep learning para identificación de núcleos activos de galaxias por variabilidad 
      Miranda Castillo, Nicolás Martín (Universidad de Chile, 2018)
      En la presente era de datos masivos, la astronomía requiere de herramientas automatizadas para el análisis de información asociada al comportamiento de objetos a lo largo del tiempo. El desarrollo de proyectos de observación ...
    • Deep learning-based classification of high intensity light patterns in photorefractive crystals 
      Ivanovic, Marija; Mancic, Ana; Hermann Avigliano, Carla; Hadzievski, Ljupco; Maluckov, Aleksandra (IOP Publishing, 2020)
      In this paper, we establish a new scheme for identification and classification of high intensity events generated by the propagation of light through a photorefractive SBN crystal. Among these events, which are the inevitable ...
    • Deep local directional embeddings para detección de objetos 
      Souto Herrera, Fabián Eduardo (Universidad de Chile, 2020)
      En el contexto de aprendizaje de máquinas, el presente trabajo aborda cómo una nueva arquitectura de redes neuronales profundas, llamada DLDENet propuesta por el autor, permite obtener mejores resultados en detección de ...
    • Deep oxidation of aluminum by a DC oxygen plasma 
      Baier Saip, J. A.; Avila, Jonathan I.; Tarrach, G.; Cabrera, A. L.; Fuenzalida Escobar, Víctor; Zárate Aliaga, Ramón; Schuller, I. K. (ELSEVIER SCIENCE SA, 2005-05-31)
      A novel way of oxidising aluminum using a DC oxygen plasma is described. The oxidation is carried out with a pressure of similar to 0.1 bar, an electrical current lower than 3 mA, and a working distance between the electrodes ...
    • Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings 
      Verstraete, David Benjamin; López Droguett, Enrique; Meruane, Viviana; Modarres, Mohammad; Ferrada, Andrés (SAGE, 2020)
      With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational ...
    • Deep shear wave velocity profiles of the Santiago Basin for evaluation of seismic response 
      Pastén Puchi, César; Salomón, José; Sáez, Miguel; Ruiz Tapia, Sergio; Leyton, Felipe (ICSMGE, 2017)
      Evaluation of the seismic response of the Santiago Basin requires a shear wave velocity model with vertical and lateral variations that may explain the site effects reported during the 2010 Maule Earthquake. In order to ...
    • Deep Subaru Hyper Suprime-Cam Observations of Milky Way Satellites Columba I and Triangulum II 
      Carlin, Jeffrey L; Sand, David J.; Muñoz Vidal, Ricardo Rodrigo; Spekkens, Kristine; Willman, Beth; Crnojevic, Denija; Forbes, Duncan A.; Hargis, ,Jonathan; Kirby, Evan; Peter, Annika H. G.; Romanowsky, Aaron J.; Strader, Jay (IOP Publishing, 2017)
      We present deep, wide-field Subaru Hyper Suprime-Cam photometry of two recently discovered satellites of the Milky Way (MW): Columba. I (Col I) and Triangulum. II (Tri II). The color-magnitude diagrams of both objects point ...
    • Deep time evidence for climate sensitivity increase with warming 
      Shaffer, Gary; Huber, Matthew; Rondanelli Rojas, Roberto; Pedersen, Jens Olaf Pepke (Amer Geophysical Union, 2016)
      Future global warming from anthropogenic greenhouse gas emissions will depend on climate feedbacks, the effect of which is expressed by climate sensitivity, the warming for a doubling of atmospheric CO2 content. It is not ...
    • Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey 
      Wei, Wei; Huerta, E. A.; Whitmore, Bradley; Lee, Janice; Hannon, Stephen; Chandar, Rupali; Dale, Daniel; Larson, Kirsten; Thilker, David; Ubeda, Leonardo; Boquien, Mederic; Chevance, Melanie; Kruijssen, J. M. Diederik; Schruba, Andreas; Blanc Mendiberri, Guillermo; Congiu, Enrico (Oxford University Press, 2020)
      We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope (HST) ...
    • Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements ... 
      San Martin, Gabriel; López Droguett, Enrique; Meruane Naranjo, Viviana; das Chagas Moura, Márcio (SAGE Publications Ltd, 2019)
      © The Author(s) 2018.One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such ...
    • A deep WISE search for very late type objects and the discovery of two halo/thick-disc T dwarfs: WISE ... 
      Pinfield, D. J.; Gomes, J.; Day Jones, A. C.; Leggett, S. K.; Gromadzki, M.; Burningham, B.; Ruiz González, María Teresa; Kurtev, R.; Cattermole, T.; Cardoso, C.; Lodieu, N.; Faherty, J.; Littlefair, S.; Smart, R.; Irwin, M.; Clarke, J. R. A.; Smith, L.; Lucas, P. W.; Gálvez Ortiz, M. C.; Jenkins, James Stewart; Jones, H. R. A.; Rebolo, L.; Béjar, V. J. S.; Gauza, B. (Oxford University Press, 2014)
      A method is defined for identifying late-T and Y dwarfs in WISE down to low values of signalto- noise. This requires a WISE detection only in theW2-band and uses the statistical properties of the WISE multiframe measurements ...
    • Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection 
      Cabrera Vives, Guillermo; Reyes, Ignacio; Förster, Francisco; Estévez Valencia, Pablo; Maureira, Juan Carlos (Institute of Physics, 2017)
      We introduce Deep-HiTS, a rotation invariant convolutional neuralnetwork (CNN) model for clas-sifying images of transients candidates into artifacts or real sources for the High cadence TransientSurvey (HiTS). CNNs have ...