Institute of Information Theory and Automation

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Doc. Ing. Václav Šmídl, Ph.D.

Position: 
research fellow
Department: 
Department of Adaptive Systems
Mail: 
Web: 
http://staff.utia.cz/smidl
Room: 
381
Phone: 
266052420
Research interests: 
multivariate analysis, Bayesian filtering, recursive estimation, adaptive control
Publications ÚTIA: 
list

Education:

2001-2004  PhD student  in Trinity College Dublin, department of Electronics and electrical engineering. Defended thesis: The Variational Bayes approach in Signal Processing.

Teaching:

Czech Technical University: Hierarchical Bayesian Models

Employment:

2004-now  ÚTIA, department of adaptive systems, research fellow

2010-now  UWB, Regional innovation center for electrical engineering, senior researcher

2018-now  CTU, Faculty of Electrical Engineering, senior researcher

2001-2004 Trinity College Dublin, PhD by research, EU projects MOUMIR and ProDacTool.

1999-2000 Sidat Praha, implementation of industrial control systems

Other sources:

scholar.google.com
ResearchGate

2019-04-02 22:10

Person detail

Hierarchical models fordetection and description of anomalies
Duration: 2018 - 2020
Anomaly detection, which aims to identity samples very different from majority, is an important tool of unsupervised data analysis. Currently, most methods for anomaly detection use relatively simple shallow models without any complex layers and hierarchies.
Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)
Duration: 2014 - 2017
This proposal brings together experts in information theory with experts in atmospheric dispersion modelling, to tackle a particularly difficult and highly relevant scientific problem.
Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions
Duration: 2011 - 2014
The aim of this project is to explore new directions in diagnostics, control and parameter identification strategies of ac electric drives under critical operating conditions. Main attention will be paid to sensorless drive control and estimation in standstill and low speeds. We propose to explore suitability of methods from Bayesian identification and stochastic control in this area.
Stochastic sequential sampling for identification and control of distributed systems
Duration: 2008 - 2010
Stochastic decentralized control of distributed systems is studied from theoretical and algorithmic point of view. Decentralization is formalized by imposing conditional independence assumptions in the centralized control problem. However, local models and aims are in general incompatible with this structure and a suitable projections must be found.

Current

Graduates

Ing. Alkomiet Belal
Mgr. Kateřina Henclová (Márová)
Ing. Václav Mácha
Ing. Vít Škvára
Ing. Lukáš Ulrych
Ing. Ondřej Tichý Ph.D.
Institute of Information Theory and Automation