Institute of Information Theory and Automation

Pro všechny

Call for Contributions

The workshop will include talks with extensive discussions and poster sessions. We invite participants to submit draft papers describing the technical content of proposed contribution. We especially encourage submissions that directly address any of the topics, but related papers are also welcomed. The selected submission may be accepted either as an oral presentation or as a poster presentation.

Important Dates

 

Submission of papers due:

 

June 28, 2013 JULY 7, 2013

 

Notification of acceptance for the workshop and decision on selection of contributions for the book due:

 

July 19, 2013  SENT

 

Format

This is a one-day workshop in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (

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Výsledky Soutěže o nejlepší publikaci a aplikaci ÚTIA 2012

KATEGORIE: Publikace všeobecné

1. cena

Bartels S., Kružík M.: An efficient approach to the numerical solution of rate-independent problems with nonconvex energies. In: Multiscale Model. Simul., vol. 9, No. 3, pp. 1276-1300, 2011

Brzeźniak, Z., Ondreját, M.: Weak Solutions to Stochastic Wave Equations with Values in Riemannian Manifolds. In: Communications in Partial Differetial Equations, 36: 1624-1653, 2011

Práce přihlášené do Soutěže o nejlepší publikaci a aplikaci ÚTIA 2012

(v abecedním pořadí podle prvního autora)

Historical publications of AS department

V. Peterka, J. Krýže, and A. Fořtová. Numerical solution of Wiener-Hopf equation in statistical identification of linear dynamic systems. Kybernetika, 2:331-346, 1966. Download.

Seminars MTR department

Title Date&Time
Factors associated with subjective well-being in Czech Republic: material situation and material deprivation. 13.02.2017 - 14:00
Everything You Always Wanted to Know about Copulas (but Were Afraid to Ask) 16.01.2017 - 14:00
Variational tools in analysis of multifunctions 05.12.2016 - 14:00
Tropical Limits of Probability Spaces. Entropy and Beyond 24.10.2016 - 14:00
Hidden Conflict of Belief Functions / Skrytý konflikt domněnkových funkcí 17.10.2016 - 14:00
Handling multidimensional probability tables by means of Kruskal-form tensors 07.12.2015 - 14:00
Some Results and Problems in the Theory of Fisher Information 23.11.2015 - 14:00
Nash Equilibrium in Pay-as-bid Electricity Market 02.11.2015 - 14:00
Lattices of functional dependences 31.08.2015 - 14:00
Multilinear Secret-Sharing Scheme 18.08.2015 - 14:00
Reasoning about uncertain conditionals 10.08.2015 - 14:00
Consonant Conflicts between Belief Functions 01.06.2015 - 14:00
Shepley's and Partially-Shapley's Axiomatics with Restricted Symmetry 20.10.2014 - 14:00
Algorithmic game theory and games with a low Price of Anarchy 29.09.2014 - 14:00
Solving Two-Player Extensive-Form Games: Algorithms and Compact Strategy Representation 22.09.2014 - 14:00
Points and Lines in Metric Spaces 12.05.2014 - 14:00
Graphical modeling of biological pathways 23.09.2013 - 14:00
Integer Programming for Bayesian Network Structure Learning 02.09.2013 - 14:00
Analysis of DNA Mixtures with Artefacts 18.06.2013 - 14:00
Estimation and tests under L-moment condition models and applications to radar detection 08.04.2013 - 14:00

Bayesian soft sensor: a tool for on-line estimation of the key process variable in cold rolling mills

One of the key objectives of any rolling mill control system is to keep the thickness of the processed material within the prescribed tolerance band, which can be as low as +-10 micrometers for thin strips. Failure to comply with the tolerances results in losses which, according to experts estimate, might go up to 10% of the profit for poorly equipped rolling mills. Unfortunately, no practical direct measurement of the gauge within the rolling gap is possible. Strip thickness can be measured 50--100cm after the rolling gap with a high transport delay (20--120 samples).

Models with strictly bounded noise

A state space model is frequently used for a description of real systems. Usually, some state variables are hidden and cannot be measured directly and some model parameters are unknown. Then, the need for learning, i.e., the state filtering and parameter estimation, arises. Probabilistic models provide a suitable description of the always uncertain reality and call for such approaches as Bayesian learning. Uncertainties are standardly modelled by the Gaussian distribution. This leads to Kalman-filter-based algorithms.

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Institute of Information Theory and Automation