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Dormido Canto, Sebastián

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0000-0001-7652-5338
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Dormido Canto
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Sebastián
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search.filters.applied.f.accessLevel: info:eu-repo/semantics/openAccess × Fecha de finalización: 2023 ×

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Mostrando 1 - 10 de 27
  • Miniatura
    Publicación
    ENTORNO DE SIMULACIÓN INTERACTIVA PARA EL CONTROL
    (2007-11-13) Aranda Almansa, Joaquín; Dormido Canto, Sebastián; Muñoz Mansilla, María del Rocío; Chaos García, Dictino; Díaz Martínez, José Manuel
  • Miniatura
    Publicación
    Data mining technique for fast retrieval of similar waveforms in Fusion massive databases
    (Elsevier, 2008-01-01) Vega, J.; Pereira González, Augusto; Portas, A.; Farias Castro, Gonzalo Alberto; Santos, M.; Sánchez, E.; Pajares Martínsanz, Gonzalo; Dormido Canto, Sebastián; Dormido Canto, Raquel; Sánchez Moreno, José; Duro Carralero, Natividad
    Fusion measurement systems generate similarwaveforms for reproducible behavior.Amajor difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behaviour, i.e. discharges with “similar” waveforms. Here we introduce a new technique for rapid searching and retrieval of “similar” signals. The approach consists of building a classification system that avoids traversing the whole database looking for similarities. The classification system diminishes the problem dimensionality (by means of waveform feature extraction) and reduces the searching space to just the most probable “similar” waveforms (clustering techniques). In the searching procedure, the input waveform is classified in any of the existing clusters. Then, a similarity measure is computed between the input signal and all cluster elements in order to identify the most similar waveforms. The inner product of normalized vectors is used as the similarity measure as it allows the searching process to be independent of signal gain and polarity. This development has been applied recently to TJ-II stellarator databases and has been integrated into its remote participation system.
  • Miniatura
    Publicación
    An analysisof models identification methods for high speed crafts
    (Journal of Maritime Research, 2005-01-01) Dormido Bencomo, Sebastián; Aranda Almansa, Joaquín; Muñoz Mansilla, María del Rocío; Dormido Canto, Sebastián; Díaz Martínez, José Manuel
    Two different approaches of the system identification method have been proposed in order to estimate models for heave, pitch and roll dynamics of a high speed craft. Both of them resolve the identification subject as an optimization problem to fit the best model. The first approach uses genetic algorithms and nonlinear least squares with constraints methods applied in the frequency domain. The second one suggests a new parameterization which facilitates obtaining high quality starting values and avoids non-quadratic functions in the cost function. At last it is shown an example in which the two approximations are applied and compared.
  • Miniatura
    Publicación
    Using Web-based laboratories for control engineering education
    (International Conference on Engineering Education – ICEE 2007, 2007-09-03) Dormido Bencomo, Sebastián; Vargas Oyarzun, Héctor; Esquembre Martínez, Francisco; Sánchez Moreno, José; Duro Carralero, Natividad; Dormido Canto, Raquel; Dormido Canto, Sebastián
  • Miniatura
    Publicación
    Determinación de parámetros de la transfomada Wavelet para la clasificación de señales del diagnóstico scattering Thomson
    (Jornadas de Automática 2004, 2004-01-01) Farias Castro, Gonzalo Alberto; Santos, M.; Fernández Marrón, José Luis; Dormido Canto, Sebastián
  • Miniatura
    Publicación
    Obtaining high preventive and resilience capacities in critical infrastructure by industrial automation cells
    (Elsevier, 2020-06) González, Santiago G.; Dormido Canto, Sebastián; Sánchez Moreno, José
    The advances in Information Technologies (ITs) are providing Industrial Control Systems (ICS) with a great capacity for interconnection and adaptability. However, the use of communication networks makes ICS highly vulnerable. Consequently, it is essential to develop methodologies for the identification and subsequent classification of the ICS that intervene in critical infrastructure assets with any level of complexity, scalability and heterogeneity. The System and Infrastructure of Knowledge for Real Experimentation by means of Cells of Industrial Automation (SIKRECIA), described in this work, provides new capabilities for research, development, simulation and testing of the functioning of these systems, and the ability to foresee the behavior of a specific system in industrial production. The scenarios recreated through SIKRECIA have the ability to anticipate new threats that affect the ICS of critical infrastructures. Using SIKRECIA, a specific vulnerability of a PLC has been verified through the engineering programmed for the management of a traffic light control system. The results obtained demonstrate the high dependence between IT and OT (Operation Technologies) systems and therefore the importance of being able to recreate those environments before entering into operation. As SIKRECIA is an open system, it can use components from different industrial manufacturers to cover the existing architectures in the process industry.
  • Miniatura
    Publicación
    Análisis, desarrollo y publicación de laboratorios vrtuales y remotos para la enseñanza de la automática
    (EIWISA 2007, V Jornadas de enseñanza a través de internet/web de la inginiería de sistemas y automática, 2007-01-01) Dormido Bencomo, Sebastián; Farias Castro, Gonzalo Alberto; Canto Díez, María Antonia; Esquembre Martínez, Francisco; Sánchez Moreno, José; Dormido Canto, Sebastián; Dormido Canto, Raquel; Duro Carralero, Natividad
  • Miniatura
    Publicación
    A Controller design by QFT methodology for dynamic positioning of a moored platform
    (2006-01-01) Muñoz Mansilla, María del Rocío; Aranda Almansa, Joaquín; Díaz Martínez, José Manuel; Dormido Canto, Sebastián; Chaos García, Dictino
  • Miniatura
    Publicación
    Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET
    (IOP Publishing, 2018-03-02) Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Baruzzo, M.; Gelfusa, Michela; Contributors, JET.; Dormido Canto, Sebastián
    Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy 'from scratch' has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.
  • Miniatura
    Publicación
    Assessment of linear disruption predictors using JT-60U data
    (Elsevier, 2019-09) Vega, J.; Hernández, F.; Isayama, A.; Joffrin, E.; Matsunaga, G.; Suzuki, T.; Dormido Canto, Sebastián
    Disruptions are dangerous events in tokamaks that require mitigation methods to alleviate its detrimental effects. A prerequisite to trigger any mitigation action is the existence of a reliable disruption predictor. This article assesses a predictor that relates in a linear way consecutive samples of a single quantity (in particular, the magnetic perturbation time derivative signal has been used). With this kind of predictor, the recognition of disruptions does not depend on how large the signal amplitude is but on how large the signal increments are: small increments mean smooth plasma evolution whereas abrupt increments reflect a non-smooth evolution and potential risk of disruption. Results are presented with data from the JT-60U tokamak and high-beta discharges. Two training methods have been tested: a classical approach in which the more data for training the better and an adaptive method that starts from scratch. In both cases the success rate is about 95%. It should be noted that predictors based on signal increments and their adaptive versions can be of big interest for next devices such as JT-60SA or ITER.