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Siegfried Mercelis

  • Research Expertise  (University of Antwerp):Expertise in distributed artificial intelligence in the context of smart mobility (e.g. automotive, smart shipping, smart traffic). Expertise in simulation based testing and model predictive control using AI. Expertise in real-time distributed systems and performance analysis using both classical methods (static/dynamic) and predictive methods using AI.
  • Keywords  (University of Antwerp):DISTRIBUTED COMPUTING, INTELLIGENCE (ARTIFICIAL), Electronics and electrical engineering
  • Disciplines  (Interuniversity Microelectronics Centre):Display technology , Antennas and propagation, Automation and control systems, Analogue, RF and mixed signal integrated circuits, Neuromorphic computing, Audio and speech processing, Environmental safety and health of nanotechnology, Battery technology, Biomaterials, Ceramic matrix composites, Hybrid composites, Computational materials science, Metals and alloy materials, Polymer processing, Nanomaterials, Functional materials, Biomedical image processing, Biochemical engineering, Biofluid mechanics, Cell, tissue and organ engineering, Arithmetic and logic structures, Automation, feedback control and robotics , Bio-informatics, Analogue and digital signal processing, Engineering instrumentation, Environmental health and safety, Biostatistics, Care for disabled, Bioethics
  • Disciplines  (University of Antwerp):Adaptive agents and intelligent robotics, Machine learning and decision making, Embedded and real-time systems, Performance modelling, System software and middleware, Distributed systems, Parallel computing , Computer vision, Intelligent transportation systems, Intelligent vehicles, Operational traffic control and traffic management, Performance evaluation, testing and simulation of reliability, Modelling and simulation
  • Research techniques  (University of Antwerp):WCET and WCRC analysis (performance and resource consumption) using static and dynamic methods. Surrogate modeling of complex systems using hybrid AI. Incorporating AI components in Model Predictive Control systems to enhance accuracy and/or reduce computational complexity. Data driven control and optimization with techniques such as reinforcement learning.
  • Users of research expertise  (University of Antwerp):Companies that want to deploy advanced intelligence in large-scale IoT systems. Companies that what to generate an added value using their data (e.g. automation, integration of intelligence in data systems). Companies active in smart mobility (e.g. traffic management, smart vehicles, autonomous shipping)