Persona: Herrera Caro, Pedro Javier
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pjherrera@issi.uned.es
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0000-0001-8679-6617
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Herrera Caro
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Pedro Javier
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Publicación Self-tuning Regulator for a Tractor with Varying Speed and Hitch Forces(Elsevier, 2018-02) Fernandez, Benjamin; Herrera Caro, Pedro Javier; Cerrada Somolinos, José AntonioDue to the changes in the soil, speed and hitch forces, the dynamics of a farm tractor are constantly changing making the design of an autonomous lane-tracking controller a very complex task. To be able to react to those changes, this paper presents a new adaptive system based on a self-tuning regulator made up of a recursive least-squares parameter identification algorithm for the plant combined with a minimum-degree pole placement (MDPP) method for changing the parameters of a digital RST controller in real time. The MDPP is computed by solving the Diophantine equation for the desired closed-loop reference model. The results presented show how the system is able to adapt the control parameters for different speeds and changes in the hitch cornering stiffness. As future work, this method could also be applied and assessed as a general controller, covering different sizes and different types of steering systems for off-road vehicles.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, ManuelAccurate 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.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 JavierAncient 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.Publicación A Simplified Optimal Path Following Controller for an Agricultural Skid-Steering Robot(Institute of Electrical and Electronics Engineers, 2019-08-02) Fernandez, Benjamin; Herrera Caro, Pedro Javier; Cerrada Somolinos, José AntonioThe dynamics of a skid-steering robot present intrinsic non-linearities that make the design and implementation of a controller a very complex task, time-consuming, and difficult to implement into an embedded system with limited resources. This paper presents a simplified first order digital model approximation and an optimal observer-based control approach for the tracking of the lateral position of such robots. In order to verify the validity of this proposal, 3D real-time interactive simulations and real validations with an agricultural skid-steering robot were performed with satisfactory results.Publicación 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 JavierIn 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.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/0000-0001-5492-5293This 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.Publicación Robust digital control for autonomous skid-steered agricultural robots(Elsevier, 2018-10) Fernández, Benjamín; Herrera Caro, Pedro Javier; Cerrada Somolinos, José AntonioThere are two main issues to consider when designing a controller for autonomous off-road vehicles: velocity and terrain irregularities. Whereas the first one is measurable, the second one is very difficult to determine. Solutions to cover these issues could be very complex and difficult to implement in an embedded system with limited resources. The results obtained in our previous research for an adaptive approach implemented in a tractor with varying hitch forces, lead to the improvements presented here. This paper proposes a robust digital RST pole placement controller design for the lateral position, with sensitivity functions tuned to cover uncertainties and non-linearities not considered in the model. Simulations were implemented to assess the performance of the system and the controller implementation was applied to a skid-steered agricultural robot with limited computational resources and a state-of-the-art navigation system, which delivered satisfactory results.