Browsing by Subject "Deep learning"

Now showing items 1-13 of 13

    • An LSTM Approach for SAG Mill Operational Relative-Hardness Prediction 
      Ávalos, Sebastián; Kracht Gajardo, Willy; Ortiz, Julian (MDPI, 2020)
      Ore hardness plays a critical role in comminution circuits. Ore hardness is usually characterized at sample support in order to populate geometallurgical block models. However, the required attributes are not always available ...
    • Assessment of deep learning techniques for prognosis of solar thermal systems 
      Correa Jullian, Camila; Cardemil Iglesias, José Miguel; López Droguett, Enrique; Behzad, Masoud (Elsevier, 2020)
      Solar Hot Water (SHW) systems are a sustainable and renewable alternative for domestic and low- temperature industrial applications. As solar energy is a variable resource, performance prediction methods are useful tools ...
    • 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 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 ...
    • Detection of COVID-19 from CT scan images: a spiking neural networkbased approach 
      Garain, Avishek; Basu, Arpan; Giampaolo, Fabio; Velásquez Silva, Juan Domingo; Sarkar, Ram (Springer, 2021)
      The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for ...
    • Dyspnea severity assessment based on vocalization behavior with deep learning on the telephone 
      Alvarado Gutiérrez, Eduardo Alexis; Grageda Ushak, Nicolas Eduardo; Luzanto Tapia, Alejandro Pablo; Mahu Sinclair, Rodrigo Manuel; Wuth Sepúlveda, Jorge Iván; Mendoza Inzunza, Laura Hispania; Becerra Yoma, Néstor Jorge (MDPI, 2023)
      In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. ...
    • Echo state network and variational autoencoder for efficient one-class learning on dynamical systems 
      Cabrera, Diego; Sancho, Fernando; Cerrada, Mariela; Sánchez, René-Vinicio; Tobar, Felipe (IOS Press, 2018)
      Usually, time series acquired from some measurement in a dynamical system are the main source of information about its internal structure and complex behavior. In this situation, trying to predict a future state or to ...
    • Going deep into schizophrenia with artificial intelligence 
      Cortés Briones, Jose A.; Tapia Rivas, Nicolás Igor; D'Souza, Deepak Cyril; Estévez Valencia, Pablo Antonio (Elsevier, 2022)
      Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity ...
    • Hydrological early warning system based on a deep learning runoff model coupled with a meteorological forecast 
      de la Fuente, Alberto; Meruane Naranjo, Viviana; Meruane, Carolina (MDPI AG, 2019)
      © 2019 by the authors.The intensification of the hydrological cycle because of global warming raises concerns about future floods and their impact on large cities where exposure to these events has also increased. The ...
    • Semantic segmentation model for crack images from concrete bridges for mobile devices 
      López Droguett, Enrique; Tapia, Juan; Yáñez, Claudio; Boroschek Krauskopf, Rubén (Sage, 2020)
      Computer vision algorithms are powerful techniques that can be used for remotely monitoring and inspecting civil structures. Detecting and segmenting cracks in images of concrete bridges can provide useful information ...
    • System-level prognostics and health management: A graph convolutional network-based framework 
      RuÍz-Tagle Palazuelos, Andrés; López Droguett, Enrique (SAGE, 2020)
      Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, ...
    • Two-level genetic algorithm for evolving convolutional neural networks for pattern recognition 
      Montecino, Daniel A.; Pérez, Claudio A.; Bowyer, Kevin W. (IEEE-Inst Electrical Electronics Engineers, 2021)
      The aim of Neuroevolution is to nd neural networks and convolutional neural network (CNN) architectures automatically through evolutionary algorithms. A crucial problem in neuroevolution is search time, since multiple ...
    • Using convolutional neural networks in robots with limited computational resources: Detecting NAO robots ... 
      Cruz, Nicolás; Lobos-Tsunekawa, Kenzo; Ruiz del Solar, Javier (Springer Verlag, 2018)
      The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use. In ...