Browsing by Author "Delpiano, José"

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    (33, 33) Verdejo, Humberto; Pino, Victor; Kliemann, Wolfgang; Becker, Cristhian; Delpiano, José; 33
    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.
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    (33, 33) Maldonado, Sebastián; Saltos, Ramiro; Vairetti, Carla; Delpiano, José; 33
    Dataset shift is a relevant topic in unsupervised learning since many applications face evolving environments, causing an important loss of generalization and performance. Most techniques that deal with this issue are designed for data stream clustering, whose goal is to process sequences of data efficiently under Big Data. In this study, we claim dataset shift is an issue for static clustering tasks in which data is collected over a long period. To mitigate it, we propose Time-weighted kernel k-means, a k-means variant that includes a time-dependent weighting process. We do this via the induced ordered weighted average (IOWA) operator. The weighting process acts as a gradual forgetting mechanism, prioritizing recent examples over outdated ones in the clustering algorithm. The computational experiments show the potential Time-weighted kernel k-means has in evolving environments.
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    (33, 33) Paul, Alvaro; Murgadas, Sofía; Delpiano, José; Moreno-Casas, Patricio A.; Walczak, Magdalena; Lopez, Mauricio; 33
    Internal curing (IC) of concrete by pre-wetted lightweight aggregate (LWA) is an established technology to assist cement hydration and reduce shrinkage and cracking in concrete. However, the current understanding in what makes a certain LWA effective for IC gives opportunities to improve the technique. The aim of this article is to identify the moisture transport mechanisms within an LWA that govern IC performance. Results on LWA of different internal structures (natural, manufactured), and different size distributions (fine, coarse), pre-soaked with either pure water or water containing shrinkage reducing admixtures (SRA), indicate that there are different mechanisms involved in water uptake and release: one controlled by capillary action, and one controlled by air diffusion into the pore water. It is concluded that it is the internal structure, geometry, and particle size distribution of the LWA that determine the effect of SRA and the overall LWA impact on the IC performance. By using 3D micro-CT images LWAs are studied in order to determine which characteristics (pore size, pore connectivity, pore distribution) are better suited for improving IC. This contribution to understanding water transport in LWAs may help to engineer the characteristics of LWA optimized for IC applications.
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    An analytical model for small signal stability analysis in unbalanced electrical power systems
    Verdejo, Humberto; Moreira, Pablo; Kliemann, Wolfgang; Becker, Cristhian; Delpiano, José
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    Apple orchard production estimation using deep learning strategies: A comparison of tracking-by-detection algorithms
    Villacrés, Juan; Viscaino, Michelle; Delpiano, José; Vougioukas, Stavros; Auat Cheein, Fernando
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    Challenges for computer vision as a tool for screening urban trees through street-view images
    Arevalo-Ramirez, Tito; Alfaro, Anali; Figueroa, José; Ponce-Donoso, Mauricio; Saavedra, Jose M.; Recabarren, Matías; Delpiano, José
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    Computational tomography and CFD simulation of a biofilter treating a toluene, formaldehyde and benzo[?]pyrene vapor mixture.
    Moreno-Casas, Patricio A.; Scott, Felipe; Delpiano, José; Vergara-Fernández, Alberto
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    Multiple object tracking for robust quantitative analysis of passenger motion while boarding and alighting a metropolitan train
    Gómez Meza, José Sebastián; Delpiano, José; Velastin, Sergio A.; Fernández, Rodrigo; Awad, Sebastián Seriani
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    A two-stage deep learning strategy for weed identification in grassfields
    Calderara-Cea, Felipe; Torres-Torriti, Miguel; Auat Cheein, Fernando; Delpiano, José
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    Using Image Analysis Techniques for Dust Detection Over Photovoltaic Panels
    Funes, Gustavo; Peters, Eduardo; Delpiano, José