Examinando por Autor "Fidalgo Astorquia, Ignacio"

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  • Miniatura
    Ítem
    Fractional-order system identification: efficient reduced-order modeling with particle swarm optimization and AI-based algorithms for edge computing applications
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-04-16) Fidalgo Astorquia, Ignacio; Gomez Larrakoetxea, Nerea; Gude, Juan José; Pastor López, Iker
    Fractional-order systems capture complex dynamic behaviors more accurately than integer-order models, yet their real-time identification remains challenging, particularly in resource-constrained environments. This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. First, PSO optimizes model parameters by minimizing the discrepancy between the high-order system response and the reduced model output. These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. Experimental validation on a custom-built heating system demonstrates that both PSO and the AI techniques yield precise reduced-order models. While PSO achieves slightly lower error metrics, its iterative nature leads to higher and more variable computation times compared to the deterministic and rapid inference of AI approaches. These findings highlight a trade-off between estimation accuracy and computational efficiency, providing a robust solution for real-time fractional-order system identification on edge devices.
  • Miniatura
    Ítem
    Gesture-based human machine interaction using RCNNs in limited computation power devices
    (NLM (Medline), 2021-12-08) Tellaeche Iglesias, Alberto ; Fidalgo Astorquia, Ignacio ; Vazquez, Juan-Ignacio ; Saikia, Surajit
    The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.