José Delpiano

Clínica Las Condes

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Fernando Auat Cheein

Harper Adams University

Stavros Vougioukas

University of California, Davis

José M. Saavedra

Universidad de Los Andes, Chile

Humberto Verdejo

Universidad de Santiago de Chile

Michelle Viscaíno

Federico Santa María Technical University

Cristhian Becker

Universidad de Santiago de Chile

Javier Ruíz-del-Solar

University of Chile

Juan Villacrés

University of California, Davis

Tito Arevalo-Ramirez

Pontificia Universidad Católica de Chile

Mauricio Ponce-Donoso

Sociedad de Biología de Chile

Cooperative Institutions

University of Chile

Universidad de Los Andes, Chile

Pontificia Universidad Católica de Valparaíso

Pontificia Universidad Católica de Chile

University of Talca

Austral University of Chile

Universidad Andrés Bello

Universidad de Santiago de Chile

Software (Germany)

Metropolitan University of Technology

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Mechanistic Description of Convective Gas–Liquid Mass Transfer in Biotrickling Filters Using CFD Modeling

Environmental Science & Technology (2019)

Patricio A. Moreno‐Casas Felipe Scott José Delpiano José A. Abell Francisco Caicedo

The gas–liquid mass transfer coefficient is a key parameter to the design and operation of biotrickling filters that governs the transport rate of contaminants and oxygen from the gas phase to the liquid phase, where pollutant biodegradation occurs. Mass transfer coefficients are typically estimated via experimental procedures to produce empirical correlations, which are only valid for the bioreactor configuration and range of operational conditions under investigation. In this work, a new method for the estimation of the gas–liquid mass transfer coefficient in biotrickling filters is presented. This novel methodology couples a realistic description of the packing media (polyurethane foam without a biofilm) obtained using microtomography with computational fluid dynamics. The two-dimensional analysis reported in this study allowed capturing the mechanisms of the complex processes involved in the creeping porous air and water flow in the presence of capillary effects in biotrickling filters. Model predictions matched the experimental mass transfer coefficients (±30%) under a wide range of operational conditions.

10.1021/acs.est.9b02662

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Citations (13)

A two-stage deep learning strategy for weed identification in grassfields

Computers and Electronics in Agriculture (2024)

Felipe Calderara-Cea Miguel Torres‐Torriti Fernando Auat Cheein José Delpiano

Identification

10.1016/j.compag.2024.109300

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Citations (2)

Self-modeling in humanoid soccer robots

Robotics and Autonomous Systems (2009)

Juan Cristóbal Zagal José Delpiano Javier Ruíz-del-Solar

10.1016/j.robot.2009.03.010

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Citations (15)

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

Energies (2020)

Humberto Verdejo Victor Pino Wolfgang Kliemann Cristhian Becker José Delpiano

The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

10.3390/en13082093

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Citations (51)

Evolutionary Optimization Applied for Fine-Tuning Parameter Estimation in Optical Flow-Based Environments

Danillo Roberto Pereira José Delpiano João Paulo Papa

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Harmony search

Optical Flow

10.1109/sibgrapi.2014.22

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Citations (3)

Multi-objective optimization for parameter selection and characterization of optical flow methods

Applied Soft Computing (2016)

José Delpiano Luis Pizarro Rodrigo Verschae Javier Ruíz-del-Solar

Characterization

Optical Flow

10.1016/j.asoc.2016.01.037

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Citations (2)

Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - SensitivityAnalysis

Zenodo (CERN European Organization for Nuclear Research) (2022)

Juan Villacrés Michelle Viscaíno José Delpiano Stavros Vougioukas Fernando Auat Cheein

The dataset "Sensitivity Analysis" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper: Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. Computers and Electronics in Agriculture. The article is currently accepted. For a better reference format, please refer to the journal's official website. If you have used the material presented in this data set, please cite the previous article. For more information regarding the dataset, please refer to the paper mentioned below.

Orchard

Tracking (education)

10.5281/zenodo.7383337

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Citations (0)

Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - SensitivityAnalysis

Zenodo (CERN European Organization for Nuclear Research) (2022)

Juan Villacrés Michelle Viscaíno José Delpiano Stavros Vougioukas Fernando Auat Cheein

Orchard

Tracking (education)

10.5281/zenodo.7383338

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Method for measurement of collagen monomer orientation in fluorescence microscopy

Journal of Biomedical Optics (2021)

Rodrigo P. Alzola Seyed Mohammad Siadat Anuj Gajjar Rickard Stureborg Jeffrey W. Ruberti

Significance: Collagen is the most abundant protein in vertebrates and is found in tissues that regularly experience tension, compression, and shear forces. However, the underlying mechanism of collagen fibril formation and remodeling is poorly understood. Aim: We explore how a collagen monomer is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the monomer has a weak contrast and is relatively small. It is possible to attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy. Approach: We present two methods for detecting a monomer and classifying its orientation. A modified Gabor filter set and an automatic classifier trained by convolutional neural network based on a synthetic dataset were used. Results: By evaluating the performance of these two approaches with synthetic and experimental data, our results show that it is possible to determine the location and orientation with an error of ∼37 deg of a single monomer with fluorescence microscopy. Conclusions: These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling.

Collagen fibril

10.1117/1.jbo.26.7.076501

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Citations (3)

The role of moisture transport mechanisms on the performance of lightweight aggregates in internal curing

Construction and Building Materials (2020)

Álvaro Paúl Sofía Murgadas José Delpiano Patricio A. Moreno‐Casas Magdalena Walczak

Shrinkage

10.1016/j.conbuildmat.2020.121191

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Citations (33)