Facultad de Ingeniería - CampGDL
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Item type:Publication, Multimodal Biometric Framework for Evaluating Emotional Impact of Chromatic Manipulation in Cinematic Content(MDPI AG, 2026-05-25); ; 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hypnogram-Driven Automatic Sleep Staging and a Quality-Index Assessment Through a Two-Stage LSTM-DNN Ensemble Learning Approach Using Multi-Biosignal Features for Sleep Disorder Detection(MDPI AG, 2026-06-27); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning Automated Measurements of Expanded Polystyrene Beads Size Using Low‐Resolution Micrography<jats:title>ABSTRACT</jats:title><jats:p>The analysis of microscopic characteristics of closed‐cell polymeric foams, particularly bead size, is relevant for understanding properties such as thermal insulation, energy absorption, and compressive structural strength of these materials. This study presents an automated method based on Deep Learning models to measure the bead size of Expanded Polystyrene foams in low‐resolution micrographs. The results of this approach were compared with manual measurements at two expanded polystyrene foam densities: 8.5 and 24 kg/m<jats:sup>3</jats:sup>. Hypothesis tests, including Student's <jats:italic>t</jats:italic>‐test, Levene's test, and Mann–Whitney <jats:italic>U</jats:italic> test, were conducted and showed no significant differences between manual and automatic measurements. Student's <jats:italic>t</jats:italic>‐test and Levene's test indicated that both methods have comparable means and variances, while the Two One‐Sided Test confirmed that they were equivalent for bead size measurement. Additionally, the Mann–Whitney <jats:italic>U</jats:italic> test revealed no differences in medians, and Bland–Altman plot analyses demonstrated no systematic bias between the methods. Taken together, these results suggest that the proposed Deep Learning‐based method is a reliable and precise substitute for the manual method in measuring the bead size of expanded polystyrene, making it suitable for practical use in the bead microstructural analysis of expanded polystyrene material.</jats:p>36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic Balancing of a Slider-Crank Mechanism Using Equimomental System of Point Masses(Springer Nature Switzerland, 2025-11-18); Daniel Martins16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Does the use of dedicated mobile devices in magnetism classes improve student learning?(Frontiers Media SA, 2026-04-21); Palacios-Navarro, Guillermo8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-Driven Based Control Applied to DC Network Converters for Voltage Bus Stabilization(2020); 1 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stability-Aware Security–Performance Trade-Off Analysis in Resource-Constrained IoT Systems: A Time-Series and Bootstrap-Based Evaluation of TLS and Hybrid ECC–AES Mechanisms(MDPI AG, 2026-05-02); ; 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automating BIM Change Certification with Blockchain: A Case Study in Residential Building Design(International Association for Automation and Robotics in Construction (IAARC), 2026-06-22) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Uncertainty quantification of compressive stress response in expanded polystyrene foams using evidential neural networks<jats:p>This study investigates the application of Deep Evidential Regression in shallow feed-forward neural networks to model and quantify the compressive stress response of expanded polystyrene foam. This foam material, widely utilized for impact protection and packaging, exhibits distinct mechanical behavior characterized by elasticity, plateau, and densification stages during compressive loading. This research adopts a data-driven approach, leveraging artificial neural networks enhanced with evidential learning to predict the distribution of stress responses, thereby addressing both aleatoric and epistemic uncertainties. The methodology involves organizing stress-strain data into training, validation, and test sets, adding noise to simulate real-world conditions, and training models with evidential layers. Results demonstrate that the proposed models maintain high predictive accuracy, with coefficients of determination exceeding 0.90 for noisy test data and above 0.99 for noise-free data. The evidential regression models also provide robust uncertainty quantification, essential for applications where data quality varies. This study’s findings highlight the efficiency and effectiveness of Deep Evidential Regression in enhancing the reliability of stress-strain predictions for EPS foam, offering significant potential for broader application to similar foam materials.</jats:p>31