Ing. Lubomír Bakule, CSc. byl jmenován emeritním pracovníkem ÚTIA AV ČR, za jeho přínos k rozvoji ústavu a vědního oboru Decentralizované řízení složitých systémů.
Theses will focus on the parameter and/or state estimation of stochastic models where the noise is described by the probability distribution with a bounded support. These models will be used for prediction and model-based control. The mentioned models are suitable for a description of real systems where some of the involved quantities are physically bounded, eg. inherent nonnegativity, maximal allowed speed etc.
Bibliography:
[1] A. d'Onofrio, Bounded noises in physics, biology, and engineering. New York, Springer, 2013 [2] M. Kárný etal.: Optimized Bayesian Dynamic Advising: Theory and Algorithms, Springer, 2006 [3] M. S. Grewal, A. P. Andrews: Kalman Filtering - Theory and Practice Using MATLAB, 2008, Wiley-IEEE Press [4] G. C. Goodwin, M. M. Seron and J. A. De Dona: Constrained Control and Estimation - An Optimisation Approach, Springer, 2005 [5] S. Kotz, J. R. Van Dorp: Beyond Beta - Other Continuous Families of Distributions with Bounded Support and Applications, World Scientific Publishing, 2004 [6] Další literatura dle konkrétního zaměření práce
Cílem přednášky je předložit matematické principy obecné teorie spolehlivosti systémů a techniky analýzy dat o přežití, spolehlivost komponentních systémů, některé asymptotické výsledky teorie spolehlivosti, koncept cenzorovaných experimentů. Postupy budou ilustrovány na praktických úlohách zpracování dat ze zkoušek životnosti materiálů a z klinického výzkumu.
The course is devoted to extremal events models, it means thus events which occur with very low probability, but with significant influence on behaviour of described model. We deal with fluctuation of random sums and fluctuation of maxima, further distributions for modeling extremal events and various models will be introduced and applied. Theoretical results will be applied on real data.
The main goal of the subject is to provide decision making mathematical principles with random effects, optimal and robust strategies and their mutual links together with computational aspects for the real applications. The techniques are illustrated within practical examples originating from point and interval estimation and statistical hypothesis testing.
This half-course introduces key modelling classifications for analogue electrical signals and systems, leading notably to small-signal analysis of nonlinear components. There is also a focus on amplifiers, notably on operational amplifier configurations.
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