A5051181098- Magnetic confinement fusion research
- Control Systems and Identification
- Fault Detection and Control Systems
- Fusion materials and technologies
- Advanced Control Systems Optimization
- Olfactory and Sensory Function Studies
- Advanced Chemical Sensor Technologies
- Model Reduction and Neural Networks
- Biochemical Analysis and Sensing Techniques
- Superconducting Materials and Applications
- Real-time simulation and control systems
- Photoacoustic and Ultrasonic Imaging
- Enzyme Structure and Function
- Reinforcement Learning in Robotics
- Advanced machining processes and optimization
- Advanced Algorithms and Applications
- Neural Networks and Applications
- Advanced Sensor and Control Systems
- Hydraulic flow and structures
- Irrigation Practices and Water Management
- Control and Stability of Dynamical Systems
- Iterative Learning Control Systems
- Stability and Controllability of Differential Equations
- Advanced Fluorescence Microscopy Techniques
- Hydraulic and Pneumatic Systems
Advanced Manufacturing Research Centre
2024
University of Sheffield
2024
The University of Melbourne
2018-2024
Centre National de la Recherche Scientifique
2017-2024
Grenoble Images Parole Signal Automatique
2016-2024
Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis
2023
Université Grenoble Alpes
2013-2022
University of Applied Sciences and Arts of Southern Switzerland
2020-2021
Dalle Molle Institute for Artificial Intelligence Research
2020-2021
National Centre for Nuclear Research
2021
The perception of taste is a prime example complex signal transduction at the subcellular level, involving an intricate network molecular machinery, which can be investigated to great extent by tools provided Computational Molecular Modelling. present review summarises current knowledge on mechanisms root transduction, in particular receptors, highly specialised proteins driving activation/deactivation specific cell signalling pathways and ultimately leading five principal tastes: sweet,...
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing test environment. Stable high-performance tokamak operation hybrid advanced plasma scenarios requires control over safety factor (q-profile) kinetic parameters such as beta. This demands to establish reliable routines presently operational tokamaks.
In this paper model-based closed-loop algorithms are derived for distributed control of the inverse safety factor profile and plasma pressure parameter β TCV tokamak.The simultaneous two quantities is performed by combining different methods.The design based on an infinite-dimensional setting using Lyapunov analysis partial differential equations, while designed techniques singleinput single-output systems.The performance robustness proposed controller analyzed in simulations fast transport...
In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed can provide binding site identification and pose prediction at same time, by systematical exploration of protein volume performed with several preliminary calculations. our opinion, this be successfully applied during first steps virtual screening pipeline, because it provides time without visual evaluation site. After prediction, MM/GBSA re-scoring rescoring procedures has been to improve...
This letter introduces a novel neural network architecture, called Integrated Neural Network (INN), for direct identification of nonlinear continuous-time dynamical models in state-space representation. The proposed INN is used to approximate the state map, and it consists feed-forward followed by an integral block. unknown parameters are estimated minimizing properly constructed dual-objective criterion. effectiveness methodology assessed against Cascaded Tanks System benchmark.
In this paper, we present a block-structured architecture for direct identification of continuous-time Linear Parameter-Varying (LPV) state-space models. The proposed consists an LPV model followed by integral block. This structure is used to approximate the system dynamics. unknown matrices are estimated along with state sequence minimizing properly constructed dual-objective criterion. A coordinate-descent algorithm employed optimize desired objective, which alternates between computing...
This article proposes novel distributed control methods for the coupled dynamics of safety factor and electron temperature profiles in tokamaks. The feedback design is based on an infinite-dimensional setting using Lyapunov analysis partial differential equations. modeled by two 1-D linearized resistive diffusion We first propose a combined both stability analysis. A composite then synthesized singular perturbation theory where fast component decoupled from slow induced magnetic field...
A model-based approach to control system design is developed for regulating the discharge flows at outlets of a pipeline network supplied by an irrigation channel. The open channel also controlled automatically regulate supply-point water level. hydraulic pressure source therefore dynamic when flow load varies. Regulation piped achieved adjusting outlet valves on basis specified demand and sensor measurements. blend feedforward feedback proposed. steady-state behaviour nonlinear...
This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to near-optimal controller without requiring any prior knowledge about the environment. We start carefully tuned Proportional Integrator (PI) and exploit recent advances in Reinforcement Learning (RL) Guides improve closed-loop behavior by learning from additional interactions valve. test proposed method various scenarios on three different valves, all highlighting...
Wire-cut Electrical Discharge Machining (Wire EDM) is a machining technique widely used to cut high-precision punch tools and highly value added precision components. With increasing resource efficiency requirements zero-defect manufacturing trend, pushing the limits of reliability, even at cutting speed next technical limits, becoming imperative. Predicting position sparks along wire thus needed develop more efficient EDM processes, thanks suppression discharges which are expected happen in...
This paper presents an integral architecture for direct identification of continuous-time linear parameter-varying (LPV) state-space models. The main building block the proposed consist LPV model followed by block, which is used to approximate state map representation. unknown matrices are estimated along with sequence minimizing a properly constructed dual-objective criterion. A coordinate descent algorithm employed optimize desired objective, alternates between computing and estimating...