Persona: 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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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::virtual::3655::600; Dormido Canto, Raquel::virtual::3656::600; Sánchez Moreno, José::virtual::3657::600; Duro Carralero, Natividad::virtual::3658::600; Dormido Canto, Sebastián; Dormido Canto, Raquel; Sánchez Moreno, José; Duro Carralero, Natividad; Dormido Canto, Sebastián; Dormido Canto, Raquel; Sánchez Moreno, José; Duro Carralero, Natividad; Dormido Canto, Sebastián; Dormido Canto, Raquel; Sánchez Moreno, José; Duro Carralero, NatividadFusion 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.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 ManuelTwo 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.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 ManuelPublicación 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::virtual::4131::600; Dormido Canto, Sebastián; Dormido Canto, Sebastián; Dormido Canto, SebastiánDetecting 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.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, SebastiánDisruptions 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.Publicación Heatflow: un laboratorio basado en web usando Easy JAva simulations y labview para el entrenamiento en técnicas de automatización(XII Latin-American Congress on Automatic Control , CD-ROM del Congreso, Salvador de Bahía (Brasil), october 2006, 2006-10-01) Vargas Oyarzun, Héctor; Farias Castro, Gonzalo Alberto; Dormido Bencomo, Sebastián; Canto Díez, María Antonia; Esquembre Martínez, Francisco; Dormido Canto, Raquel; Duro Carralero, Natividad; Sánchez Moreno, José; Dormido Canto, SebastiánPublicación 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, DictinoPublicación The Three-Tank System: A Remote and Virtual Control Laboratory using Easy Java Simulations(2005-01-01) Vargas Oyarzun, Héctor; Dormido Bencomo, Sebastián; Duro Carralero, Natividad; Dormido Canto, Raquel; Sánchez Moreno, José; Pastor Vargas, Rafael; Dormido Canto, SebastiánPublicación Control problems in marine vehicles: Some experiences in stabilization and tracking control(2006-01-01) Cruz García, Jesús Manuel de la; Riola Rodríguez, José María; Aranda Almansa, Joaquín; Muñoz Mansilla, María del Rocío; Chaos García, Dictino; Díaz Martínez, José Manuel; Dormido Canto, SebastiánPublicación Simulation and Experimental Results of a New Control Strategy For Point Stabilization of Nonholonomic Mobile Robots(IEEE, 2019-08-22) Farias, Gonzalo; Garcia, Gonzalo; Dormido Bencomo, Sebastián; Fábregas Acosta, Ernesto::virtual::4114::600; Aranda Escolástico, Ernesto::virtual::4115::600; Chaos García, Dictino::virtual::4116::600; Dormido Canto, Sebastián::virtual::4117::600; Fábregas Acosta, Ernesto; Aranda Escolástico, Ernesto; Chaos García, Dictino; Dormido Canto, Sebastián; Fábregas Acosta, Ernesto; Aranda Escolástico, Ernesto; Chaos García, Dictino; Dormido Canto, Sebastián; Fábregas Acosta, Ernesto; Aranda Escolástico, Ernesto; Chaos García, Dictino; Dormido Canto, SebastiánThis article presents a closed-loop position control of a mobile robot, which is capable of moving from its current position to a target point by manipulating its linear and angular velocities. The main objective of this article is to modify an existing control law based on the kinematic model to improve the response when the robot is backwards oriented and to reach the destination point in less time and with a shorter trajectory. Stability of the proposed control law is validated by Lyapunov Criterion. Some procedures are implemented to test this approach both in simulation with MATLAB, and experimentally with the Khepera IV robot.