Persona: Dormido Canto, Sebastián
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Dormido Canto
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Sebastián
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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, JoséThe 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.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, 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 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; 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.Publicación A linear equation based on signal increments to predict disruptive behaviours and the time to disruption on JET(IOP Publishing, 2019-12-13) Vega, J.; Murari, A.; Hernández, F.; Cruz, T.; Gadariya, Dhaval; Rattá, Giuseppe A.; Contributors, JET.; Dormido Canto, SebastiánThis article describes the development of a generic disruption predictor that is also used as basic system to provide an estimation of the time to disruption at the alarm times. The mode lock signal normalised to the plasma current is used as input feature. The recognition of disruptive/non-disruptive behaviours is not based on a simple threshold of this quantity but on the evolution of the amplitudes between consecutive samples taken periodically. The separation frontier between plasma behaviours (disruptive/non-disruptive) is linear in such parameter space. The percentages of recognised and false alarms are 98% and 4%, respectively. The recognised alarms can be split into valid alarms (90%) and late detections (8%). The experimental distribution of warning times follows an exponential model with average warning time of 443 ms. On the other hand, the prediction of the time to the disruption has been fitted to a Weibull model that relates this predicted time to the distance of the points to the diagonal in the parameter space of consecutive samples. The model shows a very good agreement between predicted times and warning times in narrow time intervals (between 0.01 s and 0.06 s) before the disruption.Publicación Disruption prediction with artificial intelligence techniques in tokamak plasmas(Springer Nature, 2022-06-06) Vega, J.; Murari, A.; Rattá, Giuseppe A.; Gelfusa, Michela; Contributors, JET.; Dormido Canto, SebastiánIn nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.Publicación A novel feature engineering approach for high-frequency financial data(Elsevier, 2023-10) Mantilla, Pablo; Dormido Canto, SebastiánFeature engineering for high-frequency financial data based on constructing dynamic data subsets, defined by time intervals in which high-frequency trends occur, is proposed. These intervals are obtained through time series segmentation. This methodology allows us to extract and analyze variables by intraday trends as well as to feed artificial intelligence models to forecast response variables in future trends. Furthermore, to show how to use this feature engineering, this methodology is applied to estimate high-frequency volatility, duration and direction linked to future intraday trends, developing multiclass classification models based on the machine learning method extreme gradient boosting. Experimentation was conducted using high-frequency financial data from the Brazil Stock Exchange, corresponding to 206 trading days related to 20 listed assets from this financial market.