Šmídl | Institute of Information Theory and Automation

Institute of Information Theory and Automation

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.

1994-1999 University of West Bohemia, department of cybernetics. Defended Thesis: The balance models of thermodynamical systems.

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

2010-now UWB, Regional innovation center for 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

2016-03-29 18:31

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.