Examinando por Autor "Herrera Caro, Pedro Javier"

Mostrando 1 - 6 de 6
  • Miniatura
    Publicación
    A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting
    (MDPI, 2023-01-02) Lazcano, Ana; Herrera Caro, Pedro Javier; Monge, Manuel
    Accurate and real-time forecasting of the price of oil plays an important role in the world economy. Research interest in forecasting this type of time series has increased considerably in recent decades, since, due to the characteristics of the time series, it was a complicated task with inaccurate results. Concretely, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have appeared in this field with promising results compared to traditional approaches. To improve the performance of existing networks in time series forecasting, in this work two types of neural networks are brought together, combining the characteristics of a Graph Convolutional Network (GCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network. This is a novel evolution that improves existing results in the literature and provides new possibilities in the analysis of time series. The results confirm a better performance of the combined BiLSTM-GCN approach compared to the BiLSTM and GCN models separately, as well as to the traditional models, with a lower error in all the error metrics used: the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the R-squared (R2). These results represent a smaller difference between the result returned by the model and the real value and, therefore, a greater precision in the predictions of this model.
  • Miniatura
    Publicación
    Modelo combinado basado en redes neuronales recurrentes y redes convolucionales de grafos para la predicción de series temporales económicas
    (Universidad Nacional de Educación a Distancia (España). Escuela Internacional de Doctorado. Programa de Doctorado en Ingeniería de Sistemas y Control, 2023) Lazcano de Rojas, Ana; Herrera Caro, Pedro Javier; Monge Moreno, Manuel Ángel
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    On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-12) Hernández González, Jerónimo; Herrera Caro, Pedro Javier
    In peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction, one of the most appealing features of peer assessment for instructors. Our proposal uses a probabilistic model to estimate a grade for each test, accounting for the degree of precision and bias of grading peers. The grade that the instructor would assign to a test can help enhance the model. Our main hypothesis is that guiding the instructor through supervision of a peer-assessed task by pointing out to them which test to evaluate next can lead to improvement in the validity of the model-estimated grades at an early stage. Moreover, the instructor can decide how many tests to grade based on their own criteria of tolerable uncertainty, as measured by the model. We validate the method using both synthetically generated data and real data collected in an actual class. Models that link the roles of the student as grading peer and as test-taker appear to better exploit available information, although simpler models are more appropriate in specific conditions. The best performing technique for guiding the instructor is that which selects the test with the highest expected entropy reduction. In general, empirical results are in line with the hypothesis of this study.
  • Miniatura
    Publicación
    Recognition of Egyptian hieroglyphic texts through focused generic segmentation and cross-validation voting
    (ELSEVIER, 2025-03) Fuentes Ferrer, Raúl; Duque Domingo, Jaime; Herrera Caro, Pedro Javier
    Ancient Egyptian hieroglyphs form part of a complex language that has attracted the attention of Egyptologists, historians, and amateurs for centuries. In use for more than 3000 years, they consist of hundreds of symbols that can be transcribed into their Latin phonemes. Although there have been some previous works on the recognition of hieroglyphs through computer vision, this is a study of unprecedented depths and presents several unique contributions. On the one hand, we have created the largest and most complete dataset of existing Egyptian hieroglyphs to date, covering all the main symbols used on stelae. On the other, we have carried out a systematic analysis of detection, segmentation, and classification methods, focusing our research on a composite method of focused generic segmentation and classification with an ensemble model of ConvNeXt backbones using Cross-Validation Voting (CVV). Our trained model has been evaluated against several carved or painted stone stelae, obtaining excellent results. To the best of our knowledge, there is currently no other methodology capable of obtaining the classification results presented in this paper, and the method and the dataset presented represent a very significant advancement in the development of automated methods for reading Egyptian hieroglyphic texts.
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    Publicación
    Robust and adaptive discrete-time control for autonomous off-road vehicles
    (Universidad Nacional de Educación a Distancia (España). Escuela Internacional de Doctorado. Programa de Doctorado en Ingeniería de Sistemas y Control, 2018-12-18) Fernández Guzmán, Fernando Benjamín; Cerrada Somolinos, José Antonio; Herrera Caro, Pedro Javier
    A la hora de diseñar un controlador para vehículos todo terreno no tripulados existen dos aspectos a tener en cuenta, además de la propia dinámica del vehículo: la velocidad y las irregularidades del terreno. Estos aspectos hacen que el comportamiento del sistema varíe en el tiempo y, mientras que el primer aspecto puede ser medible, determinar cómo afectan las irregularidades del terreno a la dinámica del vehículo puede ser extremadamente difícil. Las soluciones que cubren esta problemática pueden ser muy complejas y requerir gran cantidad de recursos tanto de tiempo como de conocimientos, haciendo que su implementación en sistemas embebidos sea poco práctica. La literatura propone soluciones basadas en la implementación de controladores adaptativos con modelo de referencia (MRAC, por sus siglas en inglés). Sin embargo, aun existen aspectos a mejorar en términos de robustez, alcance del controlador y complejidad de diseño e implementación. El trabajo de investigación desarrollado en esta tesis consiste en el análisis e implementación de dos tipos de controladores: un regulador auto ajustable en cascada y un regulador auto ajustable robusto. El primero consiste en dos lazos de control anidados, donde el lazo interno contiene el regulador auto ajustable para la velocidad de viraje y el lazo externo contiene un controlador tipo PID para la posición lateral del vehículo, el cual es gestionado por un planificador de ganancias para ajustar los parámetros del controlador según la velocidad del vehículo. El segundo controlador es un regulador auto ajustable robusto que controla directamente la posición lateral del vehículo. Ambos reguladores usan un controlador digital tipo RST, en el primero el algoritmo usado se basa en posicionamiento de polos de grado mínimo (MDPP, por sus siglas en inglés), mientras que en el segundo se basa en un posicionamiento de polos robusto basado en un modelo de regulación de segundo orden. Adicionalmente, este último contiene un planificador de ganancias que ajusta los parámetros del modelo de regulación según la velocidad medida del vehículo. Para la identificación en tiempo real de la dinámica de viraje, en ambos controladores se han propuesto dos tipos de algoritmos: Recursividad con mínimos-cuadrados y Gradiente en tiempo real. Las controladores propuestos en esta tesis fueron validados en entornos simulados y reales, y comparados favorablemente frente a un controlador de tipo óptimo ampliamente conocido. Además de utilizarse en robots agrícolas con dirección deslizante, pueden aplicarse en vehículos con diferente tipo de sistema de dirección, al ser los controladores lineales y "ligeros" en términos de programación, lo que favorece su implementación en sistemas embebidos pequeños, reduciendo así la brecha de transición de prototipos de investigación a vehículos comerciales.
  • Miniatura
    Publicación
    A vision-based strategy to segment and localize ancient symbols written in stone
    (Springer, 2017-12-21) Duque Domingo, Jaime; Herrera Caro, Pedro Javier; Cerrada Somolinos, Carlos; Cerrada Somolinos, José Antonio; https://orcid.org/0000-0001-6649-5550; https://orcid.org/0000-0001-8679-6617; https://orcid.org/0000-0002-8591-6581; https://orcid.org/0000-0001-5492-5293
    This work proposes an automatic method to detect ancient symbols written in stone. The proposed method takes into account well-known techniques used in computer vision to identify the contour of the symbols in the image. The two-stage method consists of segmentation and localization processes. Segmentation process includes a pre-processing step, edge detection and thresholding. Localization process is based on two conditions that take into account several parameters, like the distance between points, and the orientation and the continuity of the edges. This proposal has been applied to localize Egyptian cartouches (borders enclosing the name of a king) and stonemason’s marks from images obtained under varying lighting conditions (controlled and natural lighting). The proposed method is compared favorably against other methods based on chain coding, neural networks and statistical correlation. The promising results give new possibilities to identify and recognize complex symbols and ancient texts.