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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2020 - 20222
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search.filters.applied.f.accessLevel: info:eu-repo/semantics/openAccess × Fecha de inicio: 2020 ×

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Mostrando 1 - 2 de 2
  • 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
    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, Sebastián
    In 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.