Browsing by Subject "CNN"

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    A Transfer Learning Framework for predictive energy-related scenarios in Smart Buildings
    (IEEE Transactions on Industry Applications, 2023-02-01) González Vidal, Aurora; Niu, Shuteng; Song, Houbing; Skarmeta Gómez, Antonio; Mendoza Bernal, José; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Human activities and city routines follow patterns. Transfer learning can help achieve scalable solutions towards the realisation of smart cities accounting for similarities between regions, domains, and activities. In this study, we propose a Transfer Learning-based framework for smart buildings to test this hypothesis in energy-related problems. Our framework has two major components: the network creation and the transferable predictive model. In order to create the network that groups buildings sharing characteristics, we evaluated two strategies: a novel clustering algorithm for mixed data, k-prod, and clustering the image-based representation of time series. Then, a combination of Long Short Term Memory and Convolutional Neural Network was trained on the centroids of the clusters for energy consumption prediction. The Coefficient of Variation of the Root Mean Squared Error (CVRMSE) of the predictions in such clusters vary between 3.85 and 58.85 %. The obtained parameters were transferred to the rest of the buildings for predictive purposes, finding accurate results in buildings with little data. Our framework deals with insufficient training data since parameters from scenarios with more sensors can be received. It also carries out state-of-the-art performance on 3 datasets from different sources having in total 533 rooms/buildings and two energy efficiency domains: consumption prediction reducing the CVRMSE in a 21.6 %, and air conditioning usage prediction moving from a 4.18 % to a 0.28% CVRMSE. Our framework extracts more knowledge from available IoT deployments, so that smartness could be spread between environments at a fewer cost given that less individual effort will be needed.
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    Visual monitoring of landing gear in fighters using deep learning
    (Springer, 2025) Latre-Campo, Jesús; Bueno-Crespo, Andrés; Rodríguez-Bermúdez, Germán; Pereñíguez García, Fernando; Ingeniería de la Información y las Comunicaciones
    The analysis of images using deep learning techniques makes it possible to detect anomalous or dangerous situations in different fields of application. This work aims to ensure the correct configuration of landing gear during aircraft landings. In contrast with other works, the small object detection problem is solved using background subtraction technique, and subsequently feeding it to our proposed convolutional neural network to automatically classify the position of the landing gear. This work also develops a new database that combines synthetic and real images, generated from exclusive fighter landing manoeuvres performed by a real test pilot. The obtained model, trained with synthetic data and tested with real images, presents a 0.9981 of accuracy. The result is a functional system, tested against real images and endowed with “early warning” capability as it is able to detect the position of an aircraft’s landing gear in advance and prevent catastrophic accidents.