Examinando por Autor "Sanchez-Oro, Jesús"

Mostrando 1 - 2 de 2

A fast variable neighborhood search approach for multi-objective community detection
(Applied Soft Computing (Elsevier), 2021-11) Perez-Pelo, Sergio; Sanchez-Oro, Jesús; Gonzalez-Pardo, Antonio; Duarte, Abraham
Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or criminology (fraud detection). This paper presents a metaheuristic approach based on Variable Neighborhood Search (VNS) which leverages the combination of quality and diversity of a constructive procedure inspired in Greedy Randomized Adaptative Search Procedure (GRASP) for detecting communities in social networks. In this work, the community detection problem is modeled as a bi-objective optimization problem, where the two objective functions to be optimized are the Negative Ratio Association (NRA) and Ratio Cut (RC), two objectives that have already been proven to be in conflict. To evaluate the quality of the obtained solutions, we use the Normalized Mutual Information (NMI) metric for the instances under evaluation whose optimal solution is known, and modularity for those in which the optimal solution is unknown. Furthermore, we use metrics widely used in multiobjective optimization community to evaluate solutions, such as coverage, ϵ-indicator, hypervolume, and inverted generational distance. The obtained results outperform the state-of-the-art method for community detection over a set of real-life instances in both, quality and computing time.
Monitoring Volcanic and Tectonic Sandbox Analogue Models Using the Kinect v2 sensor
(Earth and Space Science, 2022-06-01) Rincón, Marta; Marquez, A; Herrera, R; Galland, O; Sanchez-Oro, Jesús; Concha, David; Sanz, Antonio
The measurement of surface deformation in analogue models of volcanic and tectonic processes is an area in continuous development. Properly quantifying topography change in analogue models is key for a useful comparison between experiment results and nature. The aim of this work is to evaluate the capabilities of the simple and cheap Microsoft® Kinect v2 sensor for monitoring analogue models made of granular materials. Microsoft® Kinect v2 is a video-gaming RedGreenBlue-Depth device combining an optical camera and an infrared distance measurement sensor. The precision of the device for model topography measurements has been quantified using 64 experiments, with variable granular materials materials and distance to the model. Additionally, we tested the capabilities of averaging several distance images to increase the precision. We have developed a specific software to facilitate the acquisition and processing of the Kinect v2 data in experiment monitoring. Our results show that measurement precision is material dependent: with clear-colored and fine-grained materials, a precision ∼1.0 mm for digital elevation models with a 1.6 mm pixel size can be obtained. We show that by averaging ≥5 consecutive images the distance precision can reach values as low as 0.5 mm. To show the Kinect v2 capabilities, we present monitoring results from case study experiments modeling tectonics and volcano deformation. The Kinect v2 achieves lower spatial resolutions and precision than more sophisticated techniques such as photogrammetry. However, Kinect v2 provides a cheap, straightforward and powerful tool for monitoring the topography changes in sandbox analogue models.