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123 results on '"Molina-Solana, Miguel"'

1. An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

Author

Manjavacas, Antonio, Campoy-Nieves, Alejandro, Jiménez-Raboso, Javier, Molina-Solana, Miguel, and Gómez-Romero, Juan

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, I.2.8, J.2
Abstract

Heating, Ventilation, and Air Conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can outperform traditional reactive controllers. However, DRL-based solutions are generally designed for ad hoc setups and lack standardization for comparison. To fill this gap, this paper provides a critical and reproducible evaluation, in terms of comfort and energy consumption, of several state-of-the-art DRL algorithms for HVAC control. The study examines the controllers' robustness, adaptability, and trade-off between optimization goals by using the Sinergym framework. The results obtained confirm the potential of DRL algorithms, such as SAC and TD3, in complex scenarios and reveal several challenges related to generalization and incremental learning.

Published
2024
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2. Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification

Author

Gijón, Alfonso, Pujana-Goitia, Ainhoa, Perea, Eugenio, Molina-Solana, Miguel, and Gómez-Romero, Juan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The ever-growing use of wind energy makes necessary the optimization of turbine operations through pitch angle controllers and their maintenance with early fault detection. It is crucial to have accurate and robust models imitating the behavior of wind turbines, especially to predict the generated power as a function of the wind speed. Existing empirical and physics-based models have limitations in capturing the complex relations between the input variables and the power, aggravated by wind variability. Data-driven methods offer new opportunities to enhance wind turbine modeling of large datasets by improving accuracy and efficiency. In this study, we used physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm, while imposing certain physical constraints to the model. The developed models for regression of the power, torque, and power coefficient as output variables showed great accuracy for both real data and physical equations governing the system. Lastly, introducing an efficient evidential layer provided uncertainty estimations of the predictions, proved to be consistent with the absolute error, and made possible the definition of a confidence interval in the power curve.

Published
2023

3. Exploring Deep Reinforcement Learning Algorithms for Enhanced HVAC Control

Author

Manjavacas, Antonio, Campoy-Nieves, Alejandro, Molina-Solana, Miguel, Gómez-Romero, Juan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ansari, Jonathan, editor, Fuchs, Sebastian, editor, Trutschnig, Wolfgang, editor, Lubiano, María Asunción, editor, Gil, María Ángeles, editor, Grzegorzewski, Przemyslaw, editor, and Hryniewicz, Olgierd, editor

Published
2024
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4. Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines

Author

Gijón, Alfonso, Eiraudo, Simone, Manjavacas, Antonio, Bottaccioli, Lorenzo, Lanzini, Andrea, Molina-Solana, Miguel, Gómez-Romero, Juan, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Franco, Leonardo, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M. A., editor

Published
2024
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7. Graph Neural Network Potentials for Molecular Dynamics Simulations of Water Cluster Anions

Author

Gijón, Alfonso, Molina-Solana, Miguel, Gómez-Romero, Juan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mikyška, Jiří, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2023
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8. Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation

Author

Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, and Guo, Yi-Ke

Subjects
Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science
Abstract

We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previous methods but has significantly lower computational complexity; in other words, we reduce the computational cost without affecting the data assimilation accuracy. We tested the new method with data from a real-world application: a pollution model of a site in Elephant and Castle, London and found that we could reduce the size of the background covariance matrix representation by O(10^3) and, at the same time, increase our data assimilation accuracy with respect to existing reduced space methods., Comment: Published in Computer Methods in Applied Mechanics and Engineering in Dec 2020

Published
2021
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10. A Game Based e-Learning System to Teach Artificial Intelligence in the Computer Sciences Degree

Author

International Association for Development of the Information Society (IADIS), de Castro-Santos, Amable, Fajardo, Waldo, and Molina-Solana, Miguel

Abstract

Our students taking the Artificial Intelligence and Knowledge Engineering courses often encounter a large number of problems to solve which are not directly related to the subject to be learned. To solve this problem, we have developed a game based e-learning system. The elected game, that has been implemented as an e-learning system, allows to develop Artificial Intelligence Decision Making Systems of very diverse complexity level. The e-learning system discharges the students of doing work not directly related with the Artificial Intelligence and Knowledge Engineering problems. This way, students can try their development and self-evaluate their progression level. The results obtained after using this e-learning system with the students (during the Artificial Intelligence and Knowledge Engineering course) show a substantial improvement in students' learning outcomes. [The authors thank the Secretariat for Teaching Innovation of the University of Granada for the funding provided for the development of this project. For the complete proceedings, see ED579335.]

Published
2017

11. Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

Author

G. Uceda, Rafael, primary, Gijón, Alfonso, additional, Míguez-Lago, Sandra, additional, Moreno-Cruz, Carlos, additional, Blanco, Víctor, additional, Fernández-Álvarez, Fátima, additional, Álvarez de Cienfuegos, Luis, additional, Molina-Solana, Miguel, additional, Gómez-Romero, Juan, additional, Miguel, Delia, additional, J. Mota, Antonio, additional, and M. Cuerva, Juan, additional

Published
2024
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12. Characterizing Political Fake News in Twitter by its Meta-Data

Author

Amador, Julio, Oehmichen, Axel, and Molina-Solana, Miguel

Subjects
Computer Science - Computation and Language, Computer Science - Social and Information Networks, Statistics - Machine Learning
Abstract

This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users.

Published
2017

14. Towards Data-Driven Simulation Models for Building Energy Management

Author

Gómez-Romero, Juan, Molina-Solana, Miguel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Paszynski, Maciej, editor, Kranzlmüller, Dieter, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2021
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16. IBERIFIER Reports: Analysis of the Impact of Disinformation on Political, Economic, Social and Security Issues, Governance Models and Good Practices: The cases of Spain and Portugal

Author

Badillo Matos, Ángel, primary, Arteaga, Félix, additional, Baldi, Vania, additional, Paisana, Miguel, additional, Crespo, Miguel, additional, Cardoso, Gustavo, additional, Rementería, María José, additional, Philippe, Olivier, additional, Calvo, Blanca, additional, Buslón, Nataly, additional, Hernández Escayola, Pablo, additional, Gómez Romero, Juan, additional, and Molina-Solana, Miguel, additional

Published
2023
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17. IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Author

Montoro Montarroso, Andrés, primary, Camacho, David, additional, Martín, Alejandro, additional, Torregrosa, Javier, additional, Rosso, Paolo, additional, Chulvi, Berta, additional, Rementería, María J., additional, Calvo Figueras, Blanca, additional, Philippe, Olivier, additional, Molina Solana, Miguel, additional, Cantón Correa, Javier, additional, and Gómez Romero, Juan, additional

Published
2023
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24. LaANIL: ANIL with Look-Ahead Meta-Optimization and Data Parallelism.

Author

Tammisetti, Vasu, Bierzynski, Kay, Stettinger, Georg, Morales-Santos, Diego P., Cuellar, Manuel Pegalajar, and Molina-Solana, Miguel

Subjects
MACHINE learning, SIMPLE machines, PARALLEL programming, ARTIFICIAL neural networks
Abstract

Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of the neural network, ANIL leads to simpler computations and reduces the complexity of MAML. Despite its benefits, ANIL suffers from issues like accuracy variance, slow initial learning, and overfitting, hardening its adaptation and generalization. This work proposes "Look-Ahead ANIL" (LaANIL), an enhancement to ANIL for better learning. LaANIL reorganizes ANIL's internal architecture, integrating parallel computing techniques (to process multiple training examples simultaneously across computing units) and incorporating Nesterov momentum (which accelerates convergence by adjusting the learning rate based on past gradient information and extracting informative features for look-ahead gradient computation). These additional features make our model more state-of-the-art capable and better edge-compatible and thus improve few-short learning by enabling models to quickly adapt to new information and tasks. LaANIL's effectiveness is validated on established meta-few-shot learning datasets, including FC100, CIFAR-FS, Mini-ImageNet, CUBirds-200-2011, and Tiered-ImageNet. The proposed model achieved an increased validation accuracy by 7 ± 0.7% and a variance reduction by 44 ± 4% in two-way two-shot classification as well as increased validation by 5 ± 0.4% and a variance reduction by 18 ± 2% in five-way five-shot classification on the FC100 dataset and similarly performed well on other datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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26. GraphDL: An Ontology for Linked Data Visualization

Author

Gómez-Romero, Juan, Molina-Solana, Miguel, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Herrera, Francisco, editor, Damas, Sergio, editor, Montes, Rosana, editor, Alonso, Sergio, editor, Cordón, Óscar, editor, González, Antonio, editor, and Troncoso, Alicia, editor

Published
2018
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27. A Software Tool for Categorizing Violin Student Renditions by Comparison

Author

Delgado, Miguel, Fajardo, Waldo, Molina-Solana, Miguel, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Herrera, Francisco, editor, Damas, Sergio, editor, Montes, Rosana, editor, Alonso, Sergio, editor, Cordón, Óscar, editor, González, Antonio, editor, and Troncoso, Alicia, editor

Published
2018
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30. Intelligent Tutoring System, Based on Video E-learning, for Teaching Artificial Intelligence

Author

Bailón, Antonio, Fajardo, Waldo, Molina-Solana, Miguel, Kacprzyk, Janusz, Series editor, Bajo, Javier, editor, Hernández, Josefa Z., editor, Mathieu, Philippe, editor, Campbell, Andrew, editor, Fernández-Caballero, Antonio, editor, Moreno, María N., editor, Julián, Vicente, editor, Alonso-Betanzos, Amparo, editor, Jiménez-López, María Dolores, editor, and Botti, Vicente, editor

Published
2015
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36. Correlated Trends: A New Representation for Imperfect and Large Dataseries

Author

Delgado, Miguel, Fajardo, Waldo, Molina-Solana, Miguel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Larsen, Henrik Legind, editor, Martin-Bautista, Maria J., editor, Vila, María Amparo, editor, Andreasen, Troels, editor, and Christiansen, Henning, editor

Published
2013
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37. Has FQAS Something to Say on Taking Good Care of Our Elders?

Author

Ros, María, Molina-Solana, Miguel, Delgado, Miguel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Larsen, Henrik Legind, editor, Martin-Bautista, Maria J., editor, Vila, María Amparo, editor, Andreasen, Troels, editor, and Christiansen, Henning, editor

Published
2013
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39. Mining Musical Patterns: Identification of Transposed Motives

Author

Berzal, Fernando, Fajardo, Waldo, Jiménez, Aída, Molina-Solana, Miguel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Rauch, Jan, editor, Raś, Zbigniew W., editor, Berka, Petr, editor, and Elomaa, Tapio, editor

Published
2009
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40. Uso de repositorios de código y herramientas de gestión de proyectos en prácticas de asignaturas del Grado en Informática

Author

Molina Solana, Miguel José

Abstract

El proyecto perseguía abordar una de las mayores fricciones existentes en las prácticas de las asignaturas del grado en Informática: el compartir código y software, y actualizaciones de ambos, con el estudiantado de forma ágil y cómoda. Para ello, se propuso como innovación el incorporar herramientas propias de la gestión de proyectos informáticos, y en particular repositorios de código, para mejorar dicha gestión. Estas herramientas (concretamente, git y Github) se encuentran plenamente integradas en el día a día de las empresas de desarrollo de software, por lo que su utilidad y adecuación están plenamente contrastadas. Aparte de facilitar la experiencia docente de compartir código entre profesorado y estudiantado, el proyecto perseguía dar al estudiantado la oportunidad de usar herramientas de gestión de código del ámbito profesional, así como conocer y experimentar de forma práctica su uso. El proyecto pretendía pues aplicar buenas prácticas docentes que ya se aplican en la industria informática y en otras universidades de prestigio, desarrollando prácticas innovadoras, e integrándolas en la docencia habitual como un proceso continuado de mejora. Todo el equipo docente de la parte práctica de la asignatura ha estado involucrado en el proyecto de innovación (8 profesores) y ha contribuido a su ejecución. El proyecto de innovación ha resultado un éxito en tanto y en cuanto se ha podido validar que la integración y uso de la herramienta github en la docencia práctica de la asignatura es posible y positiva. Toda una promoción de Ingeniería Informática (y sus dobles grados asociados), entorno a 400 estudiantes, ha participado de la experiencia. No ha existido ningún problema de implementación ni de adopción por parte del profesorado ni del estudiantado. Por todo ello, el próximo curso se seguirá usando la herramienta.

Published
2022

42. Sinergym

Author

Jiménez-Raboso, Javier, primary, Campoy-Nieves, Alejandro, additional, Manjavacas-Lucas, Antonio, additional, Gómez-Romero, Juan, additional, and Molina-Solana, Miguel, additional

Published
2021
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