Ústav teorie informace a automatizace

AS

Mathematical Methods in Economics

Oddělení: 
AS
Fakulta: 
Fakulta dopravní ČVUT
Přednášející: 
Vyučován: 
Ano
Typ kurzu: 
magisterský
Semestr: 
zimní

Basics of Bayesian Decision Making

Oddělení: 
AS
Fakulta: 
Fakulta dopravní ČVUT
Přednášející: 
Vyučován: 
Ne
Typ kurzu: 
doktorandský
Semestr: 
zimní

Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions

Vedoucí projektu: Doc. Ing. Václav Šmídl, Ph.D.
Oddělení: AS
Podporováno (ID): GAP102/11/0437
Poskytovatel: Grantová agentura ČR
Trvání: 2011 - 2014
Podrobnosti: zde

Duální řízení: inteligentní řízení systémů s neurčitostí

Název práce v Aj: 
Dual control: intelligent control of uncertain systems
Školitel: 
Typ práce: 
bakalářská
diplomová
Pracoviště/Tel.: 
UTIA, Pod vodárenskou věží 4, Praha
Klíčová slova: 
dualní řízení, inteligentní systémy, systémy s neurčitostí

Naše znalost o skutečném světě není nikdy dokonalá. V reálném světě existují náhodné jevy, poruchy nebo nepředpovídané situace, které nazýváme jednotně neurčitostí. Pokud chceme reálné soustavy ovlivňovat (řídit) je třeba vyřešit dva úkoly: 1) řízený objekt co nejlépe poznat a 2) dosáhnout cíle řízení tj. požadovaného chování.

Úkoly: 
  1. Seznamte se s teorií duálního rízení a s metodami aproximativního dynamického programování. 
  2. Zvolte jednoduchý systém (například inverzní kyvadlo) a metodu duálního řízení. 
  3. Aplikujte zvolenou metodu na zvolený systém.
Literatura: 
  • D.P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific, Nashua, US, 2001. 2nd edition. 
  • J. Si, A.G. Barto, W.B. Powell, and D. Wunsch, editors. Handbook of Learning and Approximate Dynamic Programming, Danvers, May 2004. Wiley-IEEE Press.

Analýza scintigrafických obrazových sekvencí pro lékařskou diagnostiku

Název práce v Aj: 
Analysis of scintigraphic image sequences for medical diagnostics
Školitel: 
Typ práce: 
bakalářská
diplomová
Pracoviště/Tel.: 
UTIA, Pod vodárenskou věží 4, Praha 8
Klíčová slova: 
Analýza hlavních komponent, Bayesovská statistika, matematické modelování, nukleární medicína
Funkce orgánů a tkání se v lékařské zobrazovací diagnostice posuzuje pomocí dynamických sekvencí snímků. Analýza obrazových sekvencí je založena na odhadování neznámých parametrů matematického modelu. Za určitých zjednodušujících předpokladů má model jednoduché řešení. V praktických aplikacích však nelze zaručit platnost předpokladů a tedy ani správnost výsledků analýzy. Bylo publikováno několik rozšíření matematického modelu, která poskytují spočitatelné řešení. Přínos nových metod pro praktické uplatnění v lékařské diagnostice však nebyl dosud studován.
Literatura: 
  1. V. Šmídl, A. Quinn, "The Variational Bayes Method in Signal Processing", Springer 2005. 
  2. M. Šámal, M. Kárný, H. Benali, W. Backfrieder, A. Todd-Pokropek, and H. Bergmann, "Experimental comparison of data transformation procedures for analysis of principal components," Physics in Medicine and Biology, vol. 44, pp. 2821-2834, 1999.

Seminars

News  | Seminars ]

Nadpis Date&Time
CSKI seminar: Inverse modelling for source term reconstruction 20.10.2014 - 14:00
AS seminář: Distribuované dynamické odhadování v difuzních sítích 06.10.2014 - 11:00
AS seminář: Rozdělení časových rozestupů pro systémy interagujících částic 08.09.2014 - 11:00
ČSKI seminář: Částicové stochastické systémy spojované s modelovaním dopravních jevů 17.06.2014 - 14:00
AS seminář: Aproximace plně pravděpodobnostního návrhu pomocí metod lokální regrese 02.06.2014 - 11:00
ČSKI seminář: Jak to vidí počítač 20.05.2014 - 14:00
ČSKI seminář: Stochastické nelineární vlnové rovnice 06.05.2014 - 14:00
AS seminář: Odhad struktury lineárního modelu, jeho rozšíření a aplikace 05.05.2014 - 11:00
AS seminář: Simulační výstupy algoritmu řízení světelné signalizace NOMŘÍZ 07.04.2014 - 11:00
CSKI seminar: Classification of idempotent semiring modules with strongly independent basis 20.03.2014 - 14:00
AS seminář: Znovu k základům plně pravděpodobnostního návrhu 03.03.2014 - 11:00
AS seminář: Supra-Bayesovská kombinace pravděpodobnostních distribucí – pokračování 03.02.2014 - 11:00
AS seminar: Exact-Approximate Bayesian Inference for Gaussian Process Classifiers 09.12.2013 - 14:00
AS seminář: Optimalizace ekologie jízdy na základě průběžně měřených dat 02.12.2013 - 11:00
ČSKI seminář: Proč lidé počítají? Co lidé počítají? 19.11.2013 - 14:00
ČSKI seminář: O původu vnitřního uspořádání v systémech se sociálními interakcemi 05.11.2013 - 14:00
AS seminar: MELT SPINNING PROCESS: ANALYTICAL AND NUMERICAL SOLUTIONS 04.11.2013 - 11:00
ČSKI seminář: Kompoziční modely s nepřesností 22.10.2013 - 14:00
ČSKI seminář: Kauzální kompoziční modely 15.10.2013 - 14:00
CSKI seminar: Analysis of the Solution Map Governed by a Parametrized Differential Inclusion 08.10.2013 - 14:00

AS web summary

AS_

Department of Adaptive Systems

Logo of DAS Head of the Department:
Tatiana Valentine Guy

Deputy head of the Department:
Miroslav Kárný

Secretary:
Věra Králová

phone: +420 286 890 420
www: http://www.utia.cz/AS
staff: people, Ph.D. students, alumni
List of publications, courses, projects

The Department of Adaptive Systems focuses predominantly on the design of decision-making systems, which modify their behavior according to the changing properties of their environment. This essential ability – adaptivity – enhances their efficiency. Decades of research have brought a number of conceptual, theoretical, algorithmic, software and application results. The applicability of adaptive systems is currently being extended toward complex scenarios by improving the classical adaptive systems and by developing their new versions.

The departmental “know-how” serves to resolve national as well as international research projects, running in collaboration with industry and government agencies. The interplay between theory and limited computing power is the common issue linking the various project domains. They include traffic control, management and control of technological systems, radiation protection, nuclear medicine, analysis of financial data, electronic democracy, etc. The increasing complexity of the problems addressed directs the main stream of the research toward decentralized control of large-scale systems and normative decision-making with multiple participants.

 

Last events:



The project proposal "Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling" was ranked as the second best of 389 proposals submitted within Czech-Norwegian Research Programme and awarded the grant. Congratulations!
88

Dear Colleagues,

you are cordially invited to the summer Cajovy dychanek (The tea seminar) with the topic "Dynamics of quasi-stable systems".

Date & Time: Wednesday, July 16, 14:00

Place: 477 or 474

Our guest is Prof.Igor Chueshov from Kharkov University, Ukraine.

Abstract: In this talk we explain main ideas of the recently developed approach in the study infinite dimensional dissipative systems. This approach covers wide classes of systems and allows to describe long-time dynamics in the terms of finite-dimensional attractors.

110
Gratulujeme Karelu Macekovi, členu týmu výzkumníků, který získal několik US patentů: Identifying Models of Dynamic Systems, Decision Support System Based on Energy Markets, and HVAC Controller with Regression Model to Help Reduce Energy Consumption

-------------------------------------------------------------------------------------------------------------------------------

110
Odpovědnost za obsah: AS
Poslední změny: 16.05.2014

AS

Oddělení adaptivních systémů

Logo of DAS Vedoucí oddělení:
Tatiana Valentine Guy

Zástupce vedoucí oddělení:
Miroslav Kárný

Sekretářka:
Věra Králová

telefon: +420 286 890 420
www: http://www.utia.cas.cz/cs/AS
lidé: lidé, doktorandi
Seznam publikací, přednášek, projektů

Oddělení Adaptivních systémů se zaměřuje na výzkum a návrh rozhodovacích systémů, které mění své chování v reakci na chování okolního prostředí. Tato zásadní schopnost adaptovat se zvyšuje efektivnost těchto systémů. Desetiletí výzkumu přinesla řadu koncepčních, teoretických, algoritmických, softwarových a aplikovaných výsledků. Cílem nových výzkumných projektů je rozšíření použitelnosti klasických metod na složitější problémy, případně vývoj zcela nových postupů.

Zkušenosti oddělení AS jsou využívány při řešení národních i mezinárodních výzkumných projektů podporovaných grantovými agenturami i soukromými zdroji. Společným jmenovatelem projektů je vytváření teorie respektující omezenost dostupných výpočetních zdrojů. Tento koncepční problém složitosti je společný pro všechny sledované aplikační oblasti, které zahrnují řízení dopravy pomocí signalizace, řízení technologických procesů, radiační ochranu, nukleární medicínu, analýzu finančních dat, elektronickou demokracii, atd. Zvyšující se složitost řešených problémů je hlavní motivací pro výzkum decentralizovaného řízení rozsáhlých systémů a normativní rozhodování s mnoha účastníky.

 

Poslední události:



The project proposal "Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling" was ranked as the second best of 389 proposals submitted within Czech-Norwegian Research Programme and awarded the grant. Congratulations!
88

Dear Colleagues,

you are cordially invited to the summer Cajovy dychanek (The tea seminar) with the topic "Dynamics of quasi-stable systems".

Date & Time: Wednesday, July 16, 14:00

Place: 477 or 474

Our guest is Prof.Igor Chueshov from Kharkov University, Ukraine.

Abstract: In this talk we explain main ideas of the recently developed approach in the study infinite dimensional dissipative systems. This approach covers wide classes of systems and allows to describe long-time dynamics in the terms of finite-dimensional attractors.

110
Gratulujeme Karelu Macekovi, členu týmu výzkumníků, který získal několik US patentů: Identifying Models of Dynamic Systems, Decision Support System Based on Energy Markets, and HVAC Controller with Regression Model to Help Reduce Energy Consumption

-------------------------------------------------------------------------------------------------------------------------------

110
Odpovědnost za obsah: AS
Poslední změny: 16.05.2014

AS/about/main

Homepage of AS department

 

Department characteristics

Adaptive systems (AS) are systems making decisions or selecting control actions and concurrently improving themselves. They work under incomplete knowledge in uncertain, stochastic and dynamically changing environment. Traditionally, AS comprise adaptive estimators, detectors, predictors, controllers, etc. Design and application of AS represent long-term challenge that can be addressed only when using variety of disciplines labeled as cybernetics.

The list of people in our department indicates that the group is well balanced, covering the art of adaptive systems from theoretical, algorithmic and software aspects up to real-life applications. The group has been dealing with adaptive (control) systems and related problems more than 40 years. Through these years it has created a unified, theoretically and algorithmically well grounded approach to solving problems met in the area. The approach which can be labeled as Bayesian dynamic decision making is now perceived as Prague school of adaptive systems.

Theory and algorithms

The distinguished features of the department are:

  • the research activities aim at creating a unified theory;
  • the decision-making problem is solved as the technical one in its maximal possible completeness;
  • the constructive approach dominates the work (the best possible solution is searched for: improvements are rarely started from the analytical side);

Applications

Applications the Department is dealing with are a source of vital feedback that directs us to real, not just 'academical' questions. They ranges from adaptive control of technological processing up to advising to human beings managing complex process in industry, economy and medicine. The energy spent on gradual building of generic algorithmic and software tools starts to pay back so that we are able to enter new application domains very efficiently.

Odpovědnost za obsah: AS
Poslední změny: 02.03.2012

AS/research/main

Research

Adaptive systems are dynamic units that learn their environment while make their decisions. Within this broad framework, the main research areas of the department are:

 

Probabilistic Design: Fully Probabilistic Design of Dynamic Decision Strategies

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims under given constraints. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used.

Advanced Control: Adaptive LQ Controllers and Predictive Controllers

Advanced control strategies based on LQ and predictive algorithms are significant for different industrial applications. The aim of the research is a fixing of control theory in this area and developing of complete computer-aided design of adaptive controllers. The design arises from raw data, measured on a real controlled system; user's knowledge; and user demands and it results into a completely pre-tuned and verified controller.

Stochastic Sampling: Sequential Sampling Methods for Identification and Control

Sampling methods are known for being computationally expensive, however recent research and increasing performance of computers improved applicability of these methods in such a way that they represent a strong alternative to traditional approximation methods.

Linear Systems: Advanced Theory of Linear Systems

Linear systems form a well-developed core of advanced controllers. Consequently, their understanding and even minor improvements have a deep impacts on the field. 

Traffic Control: Urban Traffic Feedback Control

The general objective of the project is the enrichment of the complete design line of LQG controllers so that it will cover steps related to state estimation, ideally with mixed-type (continuos and discrete) states.

Bayesian Decision Making: Models with strictly bounded noise

This research deals with Bayesian learning using models with bounded noise and physically constrained quantities.

Investigated Research Topics:

Decision-Making: Adaptive Decision-Making under Informationaly Demanding Conditions

Knowledge extraction maps extensive data sets on lower dimensional objects. Its results always serve to a subsequent, often dynamic, decision making. Decision-making quality is substantially influenced by the mapping used. This simple fact is relatively rarely respected by many elements in the overwhelming arsenal of existing mappings. A complete solution of decision making problems that includes explicitly the discussed mapping are severely limited by computational complexity.

Advising: Optimized Bayesian Dynamic Advising

Complex technical and societal systems are often managed by human beings (operators, managers, medical doctors ...) who badly need help to reach high standards of their acting. Conceptual solution, formalization, algorithmization and implementation of such advising systems have been addressed. The resulting system is able to cope with dynamically changing incompletely known multi-attribute environment, to learn and optimize dynamic decision-making strategy realized either by human being or automatically.

Multi-Participant DM: Theory of Multi-Participant Bayesian Decision Making

Single decision-making unit like  the advising system or non-linear adaptive controller reach relatively soon its applicability barrier, mostly caused by computationally complexity and limited reliability. Then, a distributed solution is needed. The theory and algorithms covering design and cooperation  Bayesian decision-making units (participants) are inspected. They respect limited abilities of such units, incomplete knowledge and random nature of the surrounding environment. The problem is scientifically challenging with an extreme applicability width.
Odpovědnost za obsah: AS
Poslední změny: 26.03.2012

AS/about/people

Members of Adaptive Systems

[ Active members  | Alumni ]

Jméno Surnameikona řazení Position Room 26605-
Lubomír Bakule research fellow 74 2214
Květoslav Belda research assistant 468 2310
Josef Böhm Emeritus staffer 476 2337
Jindřich Bůcha Emeritus staffer 2061
Kamil Dedecius research associate 480 2570
Tatiana Valentine Guy head of the department 481 2254
Kateřina Hlaváčková-Schindler research associate 479 2061
Radek Hofman postdoc 380 2442
Jitka Homolová research associate 362 2347
Pavel Hrabák Ph.D student 479 2307
Ladislav Jirsa research fellow 463 2302
Miroslav Kárný deputy head of the department 477 2274
Věra Králová secretary 478 2061
Ivan Nagy research fellow 483 2251
Petr Nedoma Emeritus staffer 479 2307
Lenka Pavelková research associate 476 2337
Petr Pecha research fellow 365 2009
Pavla Pecherková postdoc 369 2358
Jan Přikryl research assistant 369 2358
Oleksandr Rezunenko research associate 384 2267
Vladimíra Sečkárová Ph.D. student 469 2817
Evgenia Suzdaleva research assistant 482 2280
Václav Šmídl research associate 381 2420
Ondřej Tichý Ph.D. student 480 2570
Petr Zagalak research associate 377 2367
Odpovědnost za obsah: AS
Poslední změny: 04.01.2011

AS/partners

Partners

Research projects and applied projects are solved in cooperation with our department partners:

    International partners:

 

    Czech partners:

 

 

Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/news

News

[ News  | Seminars ]

The project proposal "Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling" was ranked as the second best of 389 proposals submitted within Czech-Norwegian Research Programme and awarded the grant. Congratulations!
88

Dear Colleagues,

you are cordially invited to the summer Cajovy dychanek (The tea seminar) with the topic "Dynamics of quasi-stable systems".

Date & Time: Wednesday, July 16, 14:00

Place: 477 or 474

Our guest is Prof.Igor Chueshov from Kharkov University, Ukraine.

Abstract: In this talk we explain main ideas of the recently developed approach in the study infinite dimensional dissipative systems. This approach covers wide classes of systems and allows to describe long-time dynamics in the terms of finite-dimensional attractors.

110
Gratulujeme Karelu Macekovi, členu týmu výzkumníků, který získal několik US patentů: Identifying Models of Dynamic Systems, Decision Support System Based on Energy Markets, and HVAC Controller with Regression Model to Help Reduce Energy Consumption

-------------------------------------------------------------------------------------------------------------------------------

110
Odpovědnost za obsah: AS
Poslední změny: 05.06.2014

AS/applications/main

Applications

Advisory System: Optimizing Support Tool for Decision Makers

Theory of dynamic advising has been converted into universal tool based on dynamic normal mixtures used as environment model and on fully probabilistic design used as constructor of advising strategies. The resulting system has been applied in such diverse field as operating of rolling mills and supporting of medical doctors curing cancer of thyroid gland. Other applications are addressed, too.

Urban Traffic Control: Feedback Control of Traffic in Cities

The only way to solve the problem of bad traffic situation in cities is the application of signal traffic control. Daily traffic course and the requirements of public transport preferences strongly need the application of some intelligent traffic system. Such a system should provide some efficient traffic control using on-line adaptive methods.

Nuclear Safety: Modeling of Consequences of Nuclear Accidents

Complex physical modeling, need for fast on-line computations on a large modeled time-spatial domain and sparse data are dominant characteristics of this important applied project.

Medicine: Support of Diagnostics and Treatment

These applications while being important on their own help us to deal with problems relying on prior information, see lymphoscintigraphy, and careful modeling, see nuclear. All of them including internal test modeling capabilities of mixtures especially in cases when the number of learning data is small.

Electrical drives: sensorless control

Control of electric drives is a well studied area and many techniques are available for normal opration regimes. However, even the best the state of the art methods fail under critical operating conditions such as broken sensors or extremely low speed. Application of Bayesian decision making algorithms helps to improve this situation and provide more reliable and robust control strategies.

Common R&D projects with Technical Development of ŠKODA AUTO a.s.

Solving common projects is based on effort to utilize knowledge and experience academic/scientific potential ÚTIA AV ČR, v.v.i. and experimental background of car maker ŠKODA AUTO a.s. The aim is not only to set up knowledge base for research projects like EKODRIVE and DAR but also to specificate shared outputs in the form of vehicle units, simulation tools and mathematical models as the source for further development advanced assistant driver systems.

 

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.
Odpovědnost za obsah: AS
Poslední změny: 03.05.2012

AS/softwaretools/main

Software tools

Development of software tools was never primary aim of our research, however development of methodologies and algorithms is impossible without proper software support. At present, we are dealing with increasingly more complex systems, hence requirement on reliability and flexiblity of software tools are growing. The following projects are actively developped and maintained:

Mixtools: Toolbox for Decision Making with Mixtures

Mixtools is a toolbox designed for learning, prediction and control design with probability mixtures. It makes the firm basis of the other software products.

BDM Library: C++ library for Bayesian Decision Making

This project is a successor of Object-Oriented toolbox Mixtools 3000. Decision making is interpreted as a method of dedicated objects: decision makers. The library contains a range of common decision-makers, such as estimators, Bayesian filters (including particle filters, Kalman filters, etc.) Decision makers for control, (i.e. LQG controller) are under development.

GPC Toolbox

GPC toolbox is a toolbox for MATLAB and Simulink. It  serves for obtaining the basic knowledge about the Generalized Predictive Control (GPC) applied to linear single-input single-output systems, which are described by input-output differential equation or state-space form.

 

Image Sequence Analysis Toolbox

A set of Matlab routines developed for analysis of medical image sequences. Many methods are general purpose blind source separation tools and can be used with any data. Other are specific to medical image sequences.

 

Developed Software:

Jobcontrol: User Interface for Routine Use of Mixtools

Jobcontrol should help the user to focuse on the application problem at hand without spending too much energy on proper combination of tools from Mixtools into the problem solver.  

Designer: Computer-Aided Design of Adaptive Controllers

Designer is a label for a complete chain of algorithms that serve for a complete computerized design of adaptive controllers. They start from raw data measured on a real system, user's knowledge and wishes and result into a completely pre-tuned aand verified controller.

LQ Toolbox

LQ toolbox is a MATLAB - Simulink based toolbox for  design of  LQ controllers based on input-output models and simulation of examples with single input and single output systems. Fixed model and adaptive versions are supported.
Odpovědnost za obsah: AS
Poslední změny: 22.04.2013

AS/publications/main

Publications

Basic Publications

Basic publications of AS Department related to Bayesian theory are:
      - Bayesian approach to system identification by V. Peterka 
      - Recursive Nonlinear Estimation. A Geometric Approach, Springer (London 1996) by R. Kulhavý 
      - Dealing with Complexity. A Neural Networks Approach, Springer, (Berlin 1997) by M. Kárný, K. Warwick K., V. Kůrková
      - Digital Self-tuning Controllers by V. Bobál, J. Böhm, J. Macháček, J. Fessl
      - Optimized Bayesian Dynamic Advising: Theory and Algorithms by M. Karny et al.
      - The Variational Bayes Method in Signal Processing by V. Šmídl and A. Quinn
      - Decision Making with Imperfect Decision Makers, by T.V. Guy, M. Kárný, D.H. Wolpert, Springer, 2012.

Historical papers

Scanned versions of documents that are hard to find but well worth reading.

Presentations

Presentations in *.ppt and *.pdf formats from AS seminars, CSKI seminars and other events.

Academic Papers of the Department

Personal publications of members of the department can be found on their personal webpages.
Odpovědnost za obsah: AS
Poslední změny: 10.10.2012

AS/education/main

Education

The AS Department ensures, organizes and produces amount of lectures, educational materials, seminars, conferences and workshops within the domain of decision making, advanced control and related areas. The department produces a significant amount of educational material on Bayesian Decision Making. This page summarizes the information about the main educational activities held in the department.

Educational materials

The educational materials on Bayesian decision making produced in AS Department are presented at this page. The material is organized so that to be potentially useful for different target groups of users: from students and PhD students to engineers solving practical problems.

Topics for students

The AS Department offers the students the wide range of research directions both of theoretical and applied nature. They can be taken by students as the topics for their diploma projects, Bachelor or Master work or Ph.D. study.

Seminars

The AS Department holds regularly the seminars on Monday at 10.30 in room 474. Furthermore, it manages the seminars of the Czech Society for Cybernetics and Informatics (CSKI), which usually held on every odd Tuesday of a month at 14.00 in the lecture room No. 474.

Workshops

The AS Department organizes conferences and workshops, which aim at helping their active participants to cope with the tasks of up-to-date Information Technologies and Control and, in a wider context, at helping them to come in useful in a changing, multicultural and multinational but gradually integrating Europe.

University courses

The teaching activities of the AS members, being held at the universities and the educational institutions, are presented here along with a brief description and a time-table.
Odpovědnost za obsah: AS
Poslední změny: 02.03.2012

AS/contact

Contact

Full name:
Czech:English:
oddělení Adaptivních systémů (AS)Department of Adaptive Systems
Ústav teorie informace a automatizace AV ČR, v.v.i.  (ÚTIA) 

Institute of Information Theory and Automation of the ASCR

Mailing address:
Department of Adaptive Systems
Institute of Information Theory and Automation 
P.O. Box 18
182 08 Prague 8
Czech Republic 

Visiting address:
Pod Vodárenskou věží 4
Prague 8 - Libeň
Czech Republic

Tel: +420 286890420
E-mail: school@utia.cas.cz

Detailed info how to reach the building can be found here.  

Odpovědnost za obsah: AS
Poslední změny: 15.10.2012

AS/about/alumni

Alumni


Jméno Surnameikona řazení Position
Josef Andrýsek after defending PhD in AS he became an analytic at a leading software firm
Luděk Berec after PhD and a research period in AS he became researcher at Institute of Entomology, Biology Centre of the AS CR
Pavel Dohnal after a research period in AS he joined FEL ČVUT
Martin Dungl Ph.D student
Pavel Ettler
Hong Gao after PhD and a research period in AS she became researcher at USA and Canada
Petr Gebouský after PhD and a research period in AS he left us to a private company
Alena Halousková after a long research period in AS she joined the firm Merit
Li He after PhD and a research period in AS she became researcher at ABB
Luboš Housa research assistant
Petya Ivanova after a research period in AS she returned back to Bulgary
Vladimír Kafka research assistant
Evgeny Kalenkovich PhD. student
Nathalia Khailova after PhD and a research period in AS she became researcher at Mayo Clinique, USA
Zuzana Knejflová Ph.D student
Tetiana Korotka Ph.D. student
Jan Kracík after PhD and a research period in AS he left us to a private company
Lenka Kulhavá after a research period in AS she joined the firm 3M
Rudolf Kulhavý after PhD and a research period in AS he became researcher at IBM
Ladislav Lhotka
Kamil Mrázek Ph.D. student
Václav Müller Ph.D. student
Miroslav Novák after PhD and a research period in AS he left us to a private company
Pavel Novotný
Adrian E. Raftery after a sabbatical year he returned back to University Washington
Jiří Rojíček after PhD and a research period in AS he became researcher at a large automation firm
Josep-Maria Rossell
Jan Šindelář postdoc
Ludvík Tesař research associate
Lukáš Trejra Ph.D. student
Christopher Tucker after a sabbatical year he returned back to University Washington
Markéta Valečková after a research period in As she became analytic of a health insurance company
Ferdinand Varga Ph.D. student
Jan Zeman Ph.D. student
Kateřina Zemánková Ph.D. student
Odpovědnost za obsah: AS
Poslední změny: 04.01.2011

AS/research/bayes

Adaptive Decision-Making under Informationally Demanding Conditions

Knowledge extraction maps extensive data sets on lower dimensional objects. Its results always serve to a subsequent, often dynamic, decision making. Decision-making quality is substantially influenced by the mapping used. This simple fact is relatively rarely respected by many elements in the overwhelming arsenal of existing mappings. A complete solution of decision making problems that includes explicitly the discussed mapping are severely limited by computational complexity (labelled as curse of dimensionality).  The project contributes to an improvement of this state via

i)   solving general dynamic-decision tasks within a specific Bayesian methodology that uses probabilistic tools  both for describing the object and strategies of decision making but also its aims and constraints;

ii)  developing methodology approximating the optimal solution obtained;

iii) verifying the developed methodological and algorithmic tools on non-trivial, practically significant, decision-making problems in medicine (diagnostics of secondary lymphedema) and economy (trading with futures).

Contact:

M. Kárný

Odpovědnost za obsah: AS
Poslední změny: 22.07.2010

AS/research/fpd

Fully Probabilistic Design of Dynamic Decision Strategies

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims under given constraints. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used. Existing limitations of the paradigm impede its applicability to complex DM as:

  1. Complexity of the information processing often crosses resources accessible.
  2. Quantification of domain-specific knowledge, aims and constraints is weakly supported. It concerns mapping of domain-specific elements on probabilistic distributions (pd).
  3. Methodology of the DM with multiple aims is incomplete.

The research aims to overcome these problems. It relies on distributed DM and fully probabilistic design (FPD) of strategies. The goal is to build a firm theoretical background of FPD of distributed DM strategies. Besides, it will enrich available results and unify them into internally consistent theory suitable for a flat cooperation structure.

This aim implies the main tasks:

  1. Inspection of conditions leading to FPD
  2. Extension of FPD to design with sets of ideal pds
  3. Design of computerized conversion of knowledge and aims into environment-describing and ideal pds
  4. Elaboration of theoretical framework for selecting cooperation tools

 

Contact:

M. Kárný

Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/research/sampling

Stochastic Sequential Sampling for Identification and Control of Distributed Systems

This research direction is concerned with identification and control of uncertain systems using Bayesian decision-making theory. The main advantage of this theory is consistency of the generated decision (i.e. estimates and control actions). However, solution of the implied recursive Bayesian relations is often available only approximately. 

Sampling methods provide a traditional approximation methodology for Bayesian statistics. Any complex probability density function can be approximated by a set of samples generated from it. This method is computationally expensive, however research effort to increase efficiency of sampling methods and increasing performance of computers improved applicability of these methods in such a way that they bring significant improvement in many application areas and represent a strong alternative to traditional approximation methods.

Sequential Monte Carlo is a way to apply sampling methods for on-line estimation and filtering. It is an established methodology with many practical applications. The advanatage of the methodology is it universality with a possibility to tailor the algorithm for a particular problem via proposal density or Rao-Blackwellization. 

A new research direction is based on application of particle filtering methods in control. This is based on duality between estimation and control. Particle filters can be applied to both dynamic programming and model predictive control formalization of the control task.

Various specific features of the approch are being elaborated under nationally funded projects listed below.

Contact:

Projects:

Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions
2011-2014
Stochastic sequential sampling for identification and control of distributed systems2008-2010
Odpovědnost za obsah: AS
Poslední změny: 20.03.2013

AS/research/linear

Linear Systems

Despite of huge progress in intelligent control, neural networks, fuzzy and nonlinear control, and other parts of control theory, the methods of linear control still remain a basis the other theories are compared with. Linear models have proved to be a relatively easy but powerful tool that has been successfully applied to many problems at work, and which has reached a high level of development during the last decades.

The lectures on linear systems and control also form a core of university courses devoted to systems theory and control. Nevertheless, the existing open problems show that there is still room for further growth and improvement of existing methods and inventing new approaches and methods.

The main goal of these studies is to contribute to the development of new methods and algorithms for the analysis and synthesis of linear control systems (with constant parameters and with or without time delays). An important vehicle for meeting the goal is the exploitation of numerous theoretical works of the recent period, for example our own contributions pertaining to the problems of matrix completions of polynomial matrices.

Contact:

P. Zagalak
Odpovědnost za obsah: AS
Poslední změny: 22.07.2010

AS/research/urban

Urban Traffic Feedback Control

The research deals with urban traffic feedback control systems. The general objective of the project is the enrichment of the complete design line of LQG controllers so that it will cover steps related to state estimation, ideally with mixed-type (continuos and discrete) states. The most important problems expected to be solved during the project are (i) general solution of state estimation in factorized form and its specialization to linear Gaussian state-space models (ii) translation of the users knowledge into optional parameters of the resulting factorized filter (iii) design of filters estimating mixed-type states and (iv) implementation and testing of controllers with state estimation on realistic simulation of traffic control problem.

Contact:

E. Suzdaleva

Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/research/advcontrol

Advanced Control

 

The main direction of the research is a design and investigation of model-based control approaches and methodologies for their real implementation and self-tuning of their parameters.

The research is focused predominantly on:

The issue of the model-based approaches is

  1. A choice and composition of suitable model for given control strategy.
    The models taking into consideration are:
    • Input Output regressive models
    • State-Space models
    The models are obtained either by some identification procedure (least squares, probabilistic theory) or they are determined on the basis of mathematical-physical analysis.
  2. Tuning of the controllers.
    The tuning is investigated from two sides:
    • aimed advices for manual tuning based on mathematical-physical relations
    • automated tuning based on probabilistic theory
Individual investigated control approaches are characterized in the following pages:
Odpovědnost za obsah: AS
Poslední změny: 09.03.2012

AS/about/history

History

History of AS department

AS department was created in middle of sixties of the past century. Control applications based on physical modeling reached soon barrier that stems from complexity of the constructed models and impossibility to find feasible controllers to them. It was found that simple black-box models are often sufficient for design of efficient controllers. The need to learn model structure and its parameters stimulated interest in so called experimental identification. Search for an adequate methodology gradually singled out Bayesian methodology as the only known systematic tools suitable for solving the addressed class of problems. Gradually, following the improvements of the theoretical, algorithmic and evaluation tools, the interests have shifted to multivariate, non-linear and non-Gaussian cases. Also, control of basic level of technological processes has been gradually substituted by  higher level control and other application domains (physics, medicine, economy, societal decision making etc.). Attempt to created applicable generic tools and struggle with curse of dimensionality has become the main driving forces of the research we perform.

During decades of research a lot of people and partners contributed to our current know how, see the alumni list and list of honorary members. It is also worthwhile  to scan workshops and seminars we organized: they clearly demonstrate both paradigm shift we underwent including circles we return back to old ideas and old problems.  The  list of people actively working within the department, the recent seminars and addressed research as well as application topics indicate that the department is flourishing and contributes to progress of the field.

Alumni

Previous grants and projects

Odpovědnost za obsah: AS
Poslední změny: 28.02.2012

AS/applications/advisory

Advisory system: optimizing support tool for decision-makers

The algorithmic and software implementation of theory of optimized Bayesian dynamic advising served as a basis for construction of advisory system intended to support the decision-maker.

To customise a particular advisory system, a large sample of historical data taken from managed process is analysed and processed offline. The obtained results are complemented by information about the expected advisory levels and decision-making aims.

A core of the advisory system forms Mixtools package, which has been implemented both: as a toolbox within MATLAB environment and as MATLAB-independent code. The MATLAB-like implementation is intended to serve to research and simulation purposes. Another implementation can be integrated with an existing control and/or monitoring system of the process managed and, thus, can serve to real-time, full-scale application.

The advisory system was implemented and extensively tested on several different case studies: prediction of urban traffic, treatment of thyroid gland carcinoma and fault detection and isolation problem. A real-time, full-scale industrial implementation of the advisory system on cold rolling mills confirmed the generic nature of the tool and illustrated the following key features of the solution:

  • ability to tailor to specific system managed
  • ability to support permanent adaptation
  • ability to follow the best practice available
  • ability to provide the strategy leading to the desired behaviour of system managed
  • ability to cope with multiple decision-making aims
  • ability to control the information load on the decision-maker (the techniques forming the core of advisory system are hidden from the decision-maker and advices are presented in easy-to-understand graphical form)

The system and its core Mixtools package are permanently innovated and improved. For the latest version, please, contact M.Kárný.

Contact:

M.Kárný 




Support of grants

  • Dynamic clustering: theory, algorithms and software, Grant Agency of the Czech Republic CR No. 102/03/0049, 2002-2005
  • Dynamic clustering for control of complex processes, Academy of Sciences of the Czech Republic No. S1075351, 2002-2005

Local SVN repository (accessible with password only):

http://mys.utia.cas.cz:1800/svn/mixtools



Odpovědnost za obsah: AS
Poslední změny: 28.02.2012

AS/applications/traffic

Urban Traffic Control: Hierarchical control of transportation nets

The task aims at building a multi-level control of the traffic in large urban transportation nets. The basic unit we operate with is the traffic microregion. It is a logically delimited collection of crossroads and the communications joining the crossroads. We suppose, some of the crossroads are controlled by signal lights and the arms of the controlled crossroads are equipped by detectors -- measuring devices, providing us with transportation data (intensities and densities of the traffic flow).

The basic variable we model and control is a vector of queue lengths that are being formed in the arms of the controlled intersection. The queues are approximated by the number of vehicles, using the physical principle "the increment of the queue is given by the difference in the amount of incoming and outgoing cars". In addition to this, a linear dependence of the car density measured on the remote detector on the column length is considered. In this way a state space model for queue lengths and density in the microregion is constructed.

The controller built on the basic of the presented model has three levels:

  1. Local control which cares of a single microregion, only. It predicts column lengths of the microregion (which are no measurable) and computes ratios of the greens for signal lights, so that the columns would be minimal. The estimation is performed by Kalman filtering, for the optimization is used linear programming.
  2. Coordinating control cares about the "good relations" between the microregion. With no coordination, the microregions incline to overload their neighbors, if convenient for them. The coordination prevents this phenomenon. Both the model and the optimization are just slight modifications of those from the local control.
  3. Exceptional states control cares about the states of the traffic, which do not belong to the common transportation. They are e.g. accidents, closures, big over-saturations etc. They are supposed to be described by discrete model and through them, mostly control using the collected expert knowledge will be realized.

The traffic control algorithm based on these principles has been developed in cooperation with ELTODO EG and finalised and practically tested in cooperation with ELTODO dopravní systémy.

Currently we are in the process of extending the model and improving the controller in the scope of a project NOMŘÍZ, a joint effort of ÚTIA, ELTODO dopravní systémy and Czech Technical University, Faculty of Transportation Sciences.

Contact:

Projects

  • Ministry of Transportation of the Czech Republic, No. 1F43A/003/120,
  • Ministry of Education, Youth and Sports of the Czech Republic, No. 1M0572 (Centre of applied research DAR ),
  • Technology Agency of the Czech Republic, No. TA01030603 (NOMŘÍZ).

Local SVN repository (accessible with password only):

http://mys.utia.cas.cz:1800/svn/doprava



Odpovědnost za obsah: AS
Poslední změny: 26.10.2012

AS/applications/nuclearsafe

Nuclear Safety

Assessment of radiological impact of accidental and normal radioactive releases on population. Application of multi-pathway transport model for regulation of normal atmospheric radioactive discharges from nuclear facilities. Advance from deterministic assessment of radiological consequences of radioactive releases into atmosphere toward the probabilistic approach.

Adaptation of techniques enables progress from former deterministic calculations towards the generation of probabilistic answers on assessment questions. Uncertainties of input parameters are taking into account and their propagation through the mathematical model is treated. Adopted scheme of Monte Carlo modeling uses stratified sampling procedure LHS. Uncertainty analysis and sensitivity analysis techniques are used to classify the extent of the uncertainty on predicted consequences and rank of particular input parameters according to their influence on radiological endpoint values.

Development of the proper sequential data assimilation techniques for corrections of model predictions on basis of observed (measured) values in terrain. Verification of various minimization algorithms with regard to complex task of radionuclide propagation into the living environment. Cooperation on development of interactive user friendly program tool customized for conditions of nuclear facilities in the Czech Republic for support of decision making during nuclear emergencies.

Contact:


Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/applications/medicine/main

Medical Applications

In this domain we are oriented towards computerized support of difficult diagnostic and treatment problems. The particular important applications serve us as test-bed of our generic methodology, algorithms and software tools. The specific problem of nuclear medicine topics is a lack of data for processing which points out the advantage of prior information.

Nuclear medicine - thyroid cancer

  • Modelling of activity-time course in thyroid gland after administration of 131I to a patient,
  • Advisory system for patient-individual recommendation of therapeutic activity of 131I.

Nuclear medicine - lymphoscintigraphy

A system for early diagnosis and treatment of upper limb lymphedema using quantitative lymphoscintigraphy

Internal medicine

Medical Imaging methods used for diagnostics in internal medicine.

Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/applications/medicine/nuclear

Nuclear medicine - thyroid cancer

Model of activity kinetics

After oral administration of radioactive 131I to a patient, iodine is accummulated in thyroid gland. Its activity rapidly increases and then slowly decreases. Model of activity time course is useful for (i) prediction of thyroid activity in a near future, (ii) time integral of activity is proportional to a dose (i.e. energy of the radiation) absorbed in the tissue. Thyroid activity is measured once or twice a day and usually not much more than 3 such measurements are available. Furthermore, these data contain random and potentially other measurement errors. Because small amount of uncertain data, prior information has been balanced to decrease uncertainty of estimated model parameters and, on the other hand, not to overweight information carried by the data.

The time integral of thyroid activity has been estimated as a random quantity. Its distribution is used for dosimetric and radio-hygienic purposes and as an input quantity for statistical analyses as well.

Advisory system

Radiodestruction of thyroid tumour is achieved by administration of 131I with high activity. The aim is to destroy the target tumour but, on the other hand, to minimize secondary radiation risks. As the response of patients' organism to administered activity is individual, therapeutic activity must be administered individually as well.

The advisory system is based on probabilistic mixture describing a selected multidimensional subset of characteristic patients' data. Then the advisory mixture model is designed, reflecting the user request to minimize the administered activity with successful result of therapy. Advice for a specific patient is conditioned by his actual data obtained in diagnostic examination before the planned therapy.

The results demonstrate that it is crucial to collect wide enough data set for description by the probabilistic mixture. The advices corresponded to medical decisions in one category of the disease that was sufficiently described by the available data used for mixture estimation.

Next effort is focused on

  • collection of more data (variables and records),
  • inclusion of incomplete data records into mixture estimation,
  • dealing with mixtures of both continuous and discrete variables.

 


Contact:

L. Jirsa

Support of grants

  • Intelligent decision support of diagnosis and therapy in nuclear medicine by Bayesian processing of uncertain data and probabilistic mixtures, Academy of Sciences of the Czech Republic, 1ET1 007 50404, 2004-2007
  • Centre of applied research DAR, Ministry of Education, Youth and Sports of the Czech Republic, 1M6 798 555 601
  • Dynamic clustering: theory, algorithms and software, Grant Agency of the Czech Republic, 102/03/0049, 2002-2005
Odpovědnost za obsah: AS
Poslední změny: 26.10.2012

AS/applications/medicine/lympho

Nuclear medicine - lymphoscintigraphy

Quantitative lymphoscintigraphy for diagnostics and therapy of upper limb lymphedema

Inspection by lymphoscintigraphy is potentially the method searched for. Its potential for examination of upper limbs is, however, inhibited by the lack of a reliable quantitative evaluation when upper limbs are examined. The main reason is number of measurements limited both by the time-capacity of the gamma camera and by the ability of a patient to undergo a series of measurement in long time intervals.

The general aim of this project is to develop new automated lymphedema diagnostics based on combination of scintigraphy quantification and other indicators available. This task can be divided into two tasks:

  1. To develop routinely applicable quantitative evaluation of the state of upper limbs. The Bayesian paradigm, in conjunction with a simplified model of the diffusion dynamics, is used to obtain reliable quantitative evaluations for the first time. Among the resulting benefits are:
    1. a procedure for tuning the measurement conditions,
    2. an assessment of deviations between limbs,
    3. the possibility of lymphedema staging assessment.
  2. To combine developed scintigraphy quantification with the rest clinical and qualitative scintigraphy findings into one global automated lymphedema stage classifier in order to increase the accuracy of diagnostics. The advantages of such global classifier are:
    1. a maximum information available for the decision,
    2. a minimized impact of skill of inspecting expert on the disease assessment,
    3. the possibility to evaluate the relevance of subset of features on the disease assessment; The relevance of the developed quantification in the combined diagnostics can be verified in this way.

Diagnostic value of the developed combined lymphedema diagnostics will be tested on the real data within the full diagnostic-therapeutic cycle and compared with conclusions of alternative diagnostic evaluations.

Contact:

M. Kárný

Support of grants

  • IGA MZČR-NC7601-3/2003.
Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/applications/medicine/internal

Internal Medicine Image Diagnosing

Application in Internal Medicine - Computer Image Diagnosing

Methods of medical image diagnosis are developed in AV CR project 1ET101050403. These methods are based on modelling of healthy and unhealthy tissue image pattern features using Gaussian mixture models. Decision making is based on Bayesian framework.

Internal medicine diseases are diagnosed.

  • Successful application in Endocrinology: diagnosing thyroid gland disease (Hashimoto's Lymphocytic Thyroiditis) with ultrasonography
  • Planned application: diagnosing liver tumor with computer tomography

Contact

M.Kárný

Support of grants

  • AV CR 1ET101050403
  • Centre of applied research DAR, Ministry of Education, Youth and Sports of the Czech Republic, 1M6 798 555 601
Odpovědnost za obsah: AS
Poslední změny: 26.10.2012

AS/softwaretools/mixtools

Mixtools: Toolbox for Decision Making with Mixtures

Mixtools is a toolbox designed for learning, prediction and control design with probability mixtures with a stress on fully probabilistic of strategies. The toolbox functions cover:

  • Initialization of mixture estimation.
  • Approximate mixture parameter estimation.
  • Prediction with mixtures.
  • Design of adaptive decision making strategies
  • Design of advisory systems
  • Visualization.

The toolbox functions offers the possibility of processing with high-dimensional data records, dynamic mixture components and extensive sets of learning data.

Contact:

Support of grants

  • Fully probabilistic design of dynamic decision strategies, Grant Agency of the Czech Republic, No. 102/08/0567, 2008-2011
  • Research center DAR, MŠMT 1M0572, 2005-2011
  • Dynamic clustering: theory, algorithms and software, Grant Agency of the Czech Republic No. 102/03/0049, 2002-2005
  • Dynamic clustering for control of complex processes, Academy of Sciences of the Czech Republic No. S1075351, 2002-2005

SVN repository (with password only):


Odpovědnost za obsah: AS
Poslední změny: 26.10.2012

AS/softwaretools/job

JobControl: User Interface for Routine Use of Mixtools

Jobcontrol is a user friendly interface for Mixtools and Designer toolboxes. The Mixtools toolbox is a powerful set of utilities for system identification employing mixture models and the corresponding control design. It is implemented as set of M-scripts and MEX-binary exacutables for the Matlab computing environment. It suits to the goal of finding suitable structure for given data. The Designer toolbox then serves for finding optimal controller parameters, constructing ideal controller and testing the controller found.

As an expert tools, Mixtools and Designer fullfils end user's needs, but are not totally suited for direct usage of the end user. In other words, they are not very user-friendly. It is why, we are developing, environment, which integrates all the tasks, that are connected with system identification and controller design and helps to collec all the user's knowledge of data and the real-world system where data come from. The Jobcontrol package, therefore, integrates endless expertise that is otherwise available only through study of the theoretical books - Optimized Bayesian Dynamic Advising: Theory and Algorithms by M. Karny et al. and [P. Nedoma, M. Karny, T.V. Guy, I. Nagy, and L. Tesar. Learning and prediction with normal mixtures. Technical Report 2045, UTIA AV CR, 2002], Mixtools toolbox documentation Bayesian approach to system identification by V. Peterka and experience contained in many experiments.

 

The Jobcontrol package help to solve user's problem in terms of the experiment (or job). Every experiment consists of description of user's data, and description of the way how the mixture is estimated and how the control is performed and what tests are to be done. Jobcontrol offers the user environment for interactive input of the description of experiment as well as lucid way of configuring experiment using one cnfiguration file. Integral part of Jobcontrol package is the protocol generator, which automatically creates a very convenient LaTeX document, which shows all the aspects of system identification, control and user's data description.

Contact:


SVN repository (password only):


Odpovědnost za obsah: AS
Poslední změny: 12.01.2012

AS/softwaretools/designer

Designer: Computer-Aided Design of Adaptive Controllers

Controller tuning is a basic step in any control application. This tuning is a complex process composed of several steps starting with the plant analysis and ending with the verification of the designed controller. There exist various tools that help in particular steps of the design but the complete path of the design is not supported. This work makes an attempt to offer a procedure of "complete" controller design where all necessary steps follow automatically one after another. The idea is applied here to the LQG controller design. The whole procedure is described and demonstrated on an example with the emphasis on the tuning of the LQG criterion to respect the given constraints.

The steps of the Designer are:

  1. Data preprocessing
  2. Structure estimation
  3. Parameter estimation
  4. Closed-loop optimization
  5. Verification

Currently, the Designer toolbox is merged with the Mixtools where it can be accessed for example using the Jobcontrol interface.

Contact:

SVN repository (password only):

http://mys.utia.cas.cz:1800/svn/mixtools/trunk/unsorted/designer/
  • For documentation about Jobcontrol and its use with Designer see directory mixtools/jobcontrol/doc
  • for speedup computation using mexes read mixtools/!Readme.txt file
Odpovědnost za obsah: AS
Poslední změny: 12.01.2012

AS/softwaretools/bdm

Bayesian Decision Making Library BDM

The library is designed using object oriented approach where decision-making is implemneted as a method of dedicated object: decision-maker. The library contains many commonly known decision-makers such as estimators and Bayesian filters. Support for control-oriented decision-makers (LQG control) is under development.

Design philosophy of the toolbox is tocreate a close image of the underlying theory. The library is build from objects representing random variables, probability density functions (pdfs) and Bayesian models. Calculus with probability density functions is implemented eiter as:

  • methods of classes representing pdfs: marginalization and conditioning
  • special types of pdf classes: the chain rule
  • and operation of class Bayesian model: the Bayes rule.

The library also contain supporting classes for running experiments with Bayesian decision makers, such as:

  • loggers: classes for recording results and intermediate results of the experiement in various formats.
  • user info: classess for loading and storing experiment setup (parameters, conditions, etc.) in XML for easy editation by the user.

For more information see project page: http://mys.utia.cas.cz:1800/trac/bdm

Contact:

Support of grants

  • Centre of applied research DAR, Ministry of Education, Youth and Sports of the Czech Republic, 1M6 798 555 601
  •  GA ČR 102/08/P250, 2008-2010

 

Odpovědnost za obsah: AS
Poslední změny: 26.10.2012

AS/softwaretools/lq

LQ Toolbox

LQ toolbox was created out of the need to demonstrate the characteristics and application of

  • LQ controllers, which require knowledge of the discrete tranfer function of the controlled system
  • Adaptive LQ controllers.
The current toolbox provide:
  • Simulink schemes for
    • a fixed model controller ( various schemes to demonstrate various characteristics)
    • an adaptive version of LQ controller for several types of models : standard regression model, incremental model (with integrator factor), both versions with or without measurable external disturbance and the possibility to consider pre-programming ( use future values of reference signal)
  • Simulink blocks representing above mentioned types of LQ adptive controller
  • m-files for LQ optimization and identification

 

Author:

J. Böhm

Contact (info):

FTP:

 

Odpovědnost za obsah: AS
Poslední změny: 04.08.2010

AS/softwaretools/gpc

GPC toolbox

GPC toolbox serves for obtaining the basic knowledge about the Generalized Predictive Control (GPC). It is prepared for control experiments of Linear Single-Input Single-Output systems with Time-Invariant parameters (LTI SISO systems) described by Input Output differential equation or state-space form.

The GPC toolbox enables user to study the properties of the basic algorithm, generating full control actions and incremental predictive algorithm. The toolbox is prepared in two identical versions:

  • version based only on MATLAB scripts
  • version combining Simulink schemes and MATLAB scripts

MATLAB scripts and Simulink schemes make possible to select and to change

  • input parameters of the controller
  • trajectory (desired reference)
  • controlled system
  • magnitude of immeasurable disturbance of the output

The both versions offer a lot of different possibilities of diagnostics of the control process.

Contact:

Link:

 

Odpovědnost za obsah: AS
Poslední změny: 11.01.2011

AS/publications/basic

Basic Publications

 
 
In this chapter the identification problems are approached via Bayesian statistics. In Bayesian view the concept of probability is not interpreted in terms of limits of relative frequencies but more generally as a subjective measure of belief of a rationally and consistently reasoning person (here called  the statistician) which is used to describe quantitatively the uncertain relationship between the statistician and the external world.
 
Content:
- Underlying Philosophy  and Basic Relations
- System Model, Reexamined from Bayesian Viewpoint
- Parameter Estimation and Output Prediction
- Time-Varying Parameters and Adaptivity
- System Classification

 

Optimized Bayesian Dynamic Advising: Theory and Algorithms by M. Karny et al. (Springer, London, 2005).

The book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization.

Decision Making with Imperfect Decision Makers by T.V. Guy, M. Kárný, D.H. Wolpert, Springer, 2012

Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies.

To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research.

Some of the particular topics addressed include:

• How should we formalise rational decision making of a single imperfect decision maker?

• Does the answer change for a system of imperfect decision makers?

• Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones?

• How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making?

• What can we learn from natural, engineered, and social systems to help us address these issues?

 

Odpovědnost za obsah: AS
Poslední změny: 22.03.2012

AS/education/e-materials/main

Educational materials

The educational materials on Bayesian decision making produced in AS Department are presented at this page. The material is organized so that to be potentially useful for different target groups of users: from students and PhD students to engineers solving practical problems. Textbooks are listed in the basic publications of the AS Department.

Dynamic Decision Making: Fully Probabilistic Design

The presented lecture in form of slides is the most up to date material. It provides a unified basis of dynamic decision making under uncertainty and incomplete knowledge. A package of examples to this lecture will be developed soon.

Bayesian Decision Making: Theory and Examples

This part of educational materials provides Bayesian decision making theory for beginners. It includes basic theoretical materials and examples available to download.

 

Examples on Bayesian Decision Making

This part of educational materials offers Bayesian decision making for experienced researchers and engineers. The provided examples deal with the research carried out in AS Department. Most of them are implemented in toolbox Mixtools.

Odpovědnost za obsah: AS
Poslední změny: 07.09.2011

AS/education/e-materials/start

Bayesian Decision Making: Theory and Examples

The introductory part to Bayesian Decision Making deals with four basic tasks:

  • modelling (simulation),
  • estimation,
  • prediction
  • and control.

These tasks, used for single input - single output cases, are simple enough to demonstrate clearly the basis of the whole theory and, on the other hande, they are mostly needed and used in the practice. The basic theory results to algorithms which are implemented in Octave (open source clone of MATLAB). The examples are available to download in the svn repository.

Contact:

Support of grants

SVN repository (with password only):

 

Odpovědnost za obsah: AS
Poslední změny: 28.12.2010

AS/education/e-materials/advanced

Examples on Bayesian decision making

This part of educational materials offers Bayesian decision making for experienced researchers and engineers. The provided examples deal with the research carried out in AS Department. Most of them are implemented in toolbox Mixtools available for download in the svn repository.


Contact:



SVN repository (with password only):

 

Odpovědnost za obsah: AS
Poslední změny: 28.12.2010

AS/education/educalibre

Edukalibre

Aims of Edukalibre

Edukalibre is a project aimed at the promotion of information and communications technology in education. Its main goal is to explore new ways of producing educational materials, based in the practices and procedures observed in the libre (free, open source) software development community. Some of the key aspects being explored are the stress on collaborative development, the interaction with users, and the use of version control systems for documentation, in the context of the educational communities. In the end, the tools and procedures for the creation of educational materials are proposed.

Partners

Edukalibre is the international project including the partners from universities and educational institutions of Spain, Portugal, Germany, UK, Switherland and Czech Republic.

AS participation

The part of AS Department's work in the Edukalibre consists in the preparing of the educational material on Bayesian Decision Making. The material is prepared under GNU licence and can be potentially used as the courseware content. It includes the theoretical part and the package of interactive examples implemented in Octave (open source clone of MATLAB) as well. Under the Edukalibre project the Moodle-server has been installed in the Department, which enabled publishing and editing of all educational resources, developed in AS for the Edukalibre. Moodle in AS is running at http://moodle.utia.cas.cz. Now it provides the theory and examples in one of their testing versions for download and complete documentation.
Download the examples
(use login as guest)

Contact:

E. Suzdaleva

 

Support of grants

The project has been supported by the following grant:

  • European Commission, No. 110330-CP-2003-ES-Minerva-M

 

Local SVN repository (accessible with password only):

 

Odpovědnost za obsah: AS
Poslední změny: 28.12.2010

AS/education/topics

Topic for students

Konkrétní nabídky studentských prací

Školitel Téma práce Klíčová slova
Šmídl Analýza scintigrafických obrazových sekvencí pro lékařskou diagnostiku Analýza hlavních komponent, Bayesovská statistika, matematické modelování, nukleární medicína
Suzdaleva Shluková analýza založená na využití modelu směsi distribucí shluková analýza, klastrování, datová analýza, model směsi distribucí
Suzdaleva Metody shlukové analýzy Semi-supervised clustering
Suzdaleva Algoritmy bayesovské klasifikace datová analýza, bayesovská klasifikace
Šmídl Duální řízení: inteligentní řízení systémů s neurčitostí dualní řízení, inteligentní systémy, systémy s neurčitostí
Belda Modelově orientované řízení robotů Průmyslové roboty, prediktivní řízení, řízení v reálném času, modelování, matematicko-fyzikální analýza
Belda Bezdrátová aktualizace softwaru řídicího modulu ZigBee komunikace ZigBee komunikační protokol, otevřené komponenty TinyOS
Belda Zpětnovazební řízení modelových motorových jednotek Distribuovaný mechatronický systém, zpětnovazební řízení, řízení v reálném čase, modelování, matematicko-fyzikální analýza
Nagy Testování nefyzikálních vazeb mezi dopravními veličinami Dopravní model, dopravní veličiny, předpověď
Nagy Predikce dopravních veličin Dopravní veličiny, denní průběh, predikce
Nagy Analýza dopravních dat z hlediska obsažené informace Dopravní data, model chování řidiče, informace pro řidiče, kvalita jízdy
Belda Logické řízení modelových motorových jednotek Logické řízení, pravdivostní tabulka, distribuovaný mechatronický systém
Kárný Může optimální rozhodování a učení obelstít jednorukého banditu? Rozhodování za neurčitosti, bayesovské odhadováni, adaptivní řízení
Nagy Metody data mining, jejich testování a porovnání Data mining, dopravní data, modelování, informace
Nagy Klasifikace módů dopravního systému Klasifikace, kvalita jizdy řidiče, rady operátorům
Kárný Teorie, algoritmy a software pro pravděpodobnostní podporu dispečerského řízení Adaptivní systémy, poradní systémy, bayesovské učení, pravděpodobnostní návrh
Kárný Normativní teorie a algoritmy distribuovaného dynamického rozhodování za neurčitosti a neúplné znalosti Adaptivní systémy, distribuované systémy, poradní systémy, bayesovské učení, pravděpodobnostní návrh, pravděpodobnostní kooperace
Kárný Aproximace plně pravděpodobnostní verze dynamického programování jako základ univerzálních učících se rozhodovacích a řídících systémů Adaptivní systémy, bayesovské učení a rozhodování, pravděpodobnostní návrh strategií, aproximace implicitně popsaných funkcí mnoha proměnných
Přikryl Zpětnovazební řízení silničního provozu v Praze Adaptivní systémy, modelování a automatické řízení dopravy, bayesovské učení, pravděpodobnostní návrh řízení
Přikryl Řízení dopravy změnou doby cyklu světelné signalizace Dopravní oblast, řízení, optimalizace, světelná signalizace, doba cyklu
Přikryl Dopravní model mikrooblasti pro více pruhů v ramenech Dopravní model, křižovatka, ramena, fáze, cyklus
Přikryl Využití informace ze všech detektoru v rameni křižovatky pro odhad délky kolony Dopravní model, odhadování, délka kolony
Přikryl Konstrukce optimální objízdné trasy vzhledem ke vzdálenosti a době jízdy Havárie, objízdná trasa, optimalizace
Přikryl Řešení dopravních konfliktů při nadměrném růstu kolony Řízení městské dopravy, kolona, přesahování kolon, optimalizace
Přikryl Řešení zelených vln pro optimálně řízené křižovatky Řízení dopravy, dynamické řízení, zelené vlny
Přikryl Úpravy dopravního modelu pro respektování zelené vlny Dopravní oblast, řízení dopravy ve městě, světelná signalizace, ofset signálního plánu
Přikryl Preference pro upřednostněná vozidla v křižovatkách mikrooblasti Doprava ve městech, řízení, preferovaná vozidla
Přikryl Bilance měřených vjezdů do mikrooblasti Dopravní mikrooblasti, délky kolon, řízení
Pavelková Porovnání metod pro odhad omezených veličin Bayesovský odhad, stavový model, omezený šum
Hofman Vývoj a úprava stávajících softwarových prostředků pro modelování síření škodlivin v ovzduší dispezrní modelování, vývoj SW, Python
Odpovědnost za obsah: AS
Poslední změny: 05.01.2011

AS/education/seminars

Seminars

Our department is actively involved in activities of Czech Society for Cybernetics and Informatics (CSKI). Specifically, one its group "Decision-Making and Control under Uncertainty" (DCU) was founded by members of AS department. The aims of this group cover the main research interests of our department.

Moreover, local seminars are organized within regular meeting of members of the department every monday. Primary role of the local seminars is communication of knowledge within the department, everyone is welcome to attend these seminars. Information about "Monday seminars" are dissipated via email list seminar<at>utia.cas.cz.

To subscribe to this list, send a message to listproc@utia.cas.cz with content:

"SUBSCRIBE SEMINAR <Your Name>"

List of past seminars: AS and CSKI

Odpovědnost za obsah: AS
Poslední změny: 12.04.2012

AS/education/workshops

AS/education/lectures

University courses

Course name Lecturer Faculty Semester
Coding Theory and Cryptography Přikryl Fakulta dopravní ČVUT zimní
Dynamic Decision Making Kárný Fakulta jaderná a fyzikálně-inženýrská ČVUT zimní
Kódování a základy kryptologie Přikryl Fakulta dopravní ČVUT zimní
Large Scale Systems Control Bakule Fakulta jaderná a fyzikálně-inženýrská ČVUT letní
Matematické algoritmy Přikryl Fakulta dopravní ČVUT zimní
Mathematical Methods in Economics Nagy Fakulta dopravní ČVUT zimní
Modelování systémů a procesů Přikryl Fakulta dopravní ČVUT letní
Predictive Control Böhm Fakulta jaderná a fyzikálně-inženýrská ČVUT letní
Probability Theory and Statistics Nagy Fakulta dopravní ČVUT oba
Stochastic systems Nagy Fakulta dopravní ČVUT oba
Stochastic systems for Erasmus students Nagy Fakulta dopravní ČVUT oba
Odpovědnost za obsah: AS
Poslední změny: 04.01.2011

AS/about/prevgrants

Previous grants and projects

Our department worked on many grants and projects so far. On this page you can find a list of them.

Grant Leader From Till
Solution of Modelling and Algorithmic Problems of Bayesian Estimation in Nuclear Medicine and Dosimetry of Ionising Radiation Ladislav Jirsa 2000 2003
Shell International Donation no. C9993079/00/021297 for a two month visit to the U.S.A., incl. presentation at the 36th IEEE Conference on Decision and Control Ferdinand Kraffer 2000 2003
Návrh počítačového modulu pro informační analýzu časových řad odezev autonomních proteinových systémů - MIAPS (IGA MZCR) Jiří Knížek 2001 2003
Identifikace modelů s poruchou na výstupu Miroslav Kárný 2001 2003
Řešení modelovacích úloh a algoritmických problémů bayesovského odhadování v nukleární medicíně a dozimetrii ionizujícího záření Ladislav Jirsa 2000 2003
Nové směry lineárního řízení Petr Zagalák 2001 2003
Nelineární odhadování a detekce změn stochastických systémů Rudolf Kulhavý 2001 2003
Hybrid Self-Tuning Controller Tatiana Guy 2000 2003
Algorithms and Implementation of Self-tuning Multivariate Controllers Josef Bohm 1999 2002
Decision-support tool for complex industrial processes based on probabilistic data clustering Miroslav Kárný 1999 2002
Redundant Parallel Robots and their Control Josef Bohm 1999 2002
Research and Education Center in Adaptive Systems: a pilot project, RECiAS Miroslav Kárný 1999 2001
Fault Detection and Isolation - Cooperation with Slovenia Miroslav Kárný 1998 2001
Decision-support tool for complex industrial processes based on probabilistic data clustering Miroslav Kárný 1998 2001
Algebro-geometric methods for polynomial matrix operations with applications in control system design Ferdinand Kraffer 1999 2001
Geometric methods in algebraic theory implementation to multivariable systems Ferdinand Kraffer 1999 2001
Co-operation on localization of RODOS systém Petr Pecha 2000 2001
Bayesian approximate recursive identification and on-line adaptive control of Markov chains with high order and large state space Hong Gao 1998 2000
Influence of biophysical factors on thyroid cancer treatment Miroslav Kárný 1998 2000
New approach to optimality and adaptivity of uncertain systems Miroslav Kárný 1997 1999
Enhancement of the EU decision support system RODOS and its customisation for use in Eastern Europe Petr Nedoma 1997 1999
Adaptive systems: theory, algorithms and software for practice Petr Nedoma 1997 1999
Adaptive dynamic elements and their connections for dynamic decision making under uncertainty Miroslav Kárný 1996 1998
Global approximation of model in recursive Bayesian parameter estimation Rudolf Kulhavý 1995 1997
Modeling of transitive economy using short time series Rudolf Kulhavý 1996 1997
Qualitative and analytical model based fault detection for chemical processes Rudolf Kulhavý 1994 1997
Adaptive and predictive control with physical constraints Josef Bohm 1994 1997
Efficient method of non-linear recursive estimation: theoretical background and application to selected models Rudolf Kulhavý 1994 1996
Micro-controller framed innovative technology: Instruments for adaptive process control J. Maršík 1993 1996
Objective evaluation of data measured for diagnostic and therapeutic purposes in nuclear medicine Miroslav Kárný 1994 1996
Quality assurance for processing of data measured for diagnostic and therapeutic purposes in nuclear medicine Miroslav Kárný 1994 1996
Computer aided engineering for pretuning of sophisticated computer control of technological processes Miroslav Kárný 1993 1995
Design of multivariate adaptive control Miroslav Kárný 1993 1995
Central European Graduate School in Systems and Control Theory Miroslav Kárný 1994 1995
Finite-dimensional approximation of recursive Bayesian parameter estimation Rudolf Kulhavý 1993 1995
Microprocessor based innovating technology: Adaptive controllers of industrial processes J. Maršík 1993 1995
Microprocessor-oriented innovative technologies: Hardware for adaptive control of technological processes J. Maršík 1993 1995
Parallel programming system and architectures with application to CAD of control systems Petr Nedoma 1993 1995
Practical aspects of self tuning controllers: algorithms and implementation Josef Bohm 1994 1995
Postdoctoral Fellowship at the Thematic Term on Linear Algebra and Applica- tions to Control Theory, Centro Internacional de Matematica (Fundacao da Universidade de Lisboa) Ferdinand Kraffer
Customisation of RODOS system for Czech Republic Petr Pecha
Odpovědnost za obsah: AS
Poslední změny: 24.07.2010

AS/research/advcontrol/adlqc

Advanced Control

Introduction | Adaptive Linear Quadratic Control | Generalized Predictive Control | Literature ]

 

Linear Quadratic Control (LQ Control) investigated in the department consists in minimization of quadratic criterion by dynamic programming. The adaptive character of the control is achieved by addition of on-line identification of controlled system. In the initializing step of the identification, the structure of identified model is determined and the first setting of model parameters is done. During the run, the identification improves individual model parameters.

Computer-aided design and self-tuning

The challenge of the research is a design of self-tuning for the parameters of Linear Quadratic Gaussian Controllers (LQG Controllers). The tuning is a complex process composed of several steps starting with the plant analysis and ending with the verification of the designed controller.

Author:

J. Böhm

Contact (info):

Software tools:

On request.
Odpovědnost za obsah: AS
Poslední změny: 09.03.2012

AS/research/advcontrol/gpc

Advanced Control

Introduction | Adaptive Linear Quadratic Control | Generalized Predictive Control | Literature ]

 

Generalized Predictive Control (GPC) is a multi-step approach. It combines feed-forward part and feedback part. The feed-forward part is represented by prediction via mathematical model describing a controlled system. This part forms the dominant part of control actions. The feedback, closed from measured outputs, compensates some inaccuracies of the model and certain bounded disturbances.

The real design consists in composition of equations of predictions and minimization of quadratic criterion, in which the equations of predictions are involved. The minimization is performed within finite horizons.

The research is focused on state-space control design applied to deterministic linear systems, deterministic nonlinear systems and slightly stochastic systems. Developed control algorithms are tested on mechanical systems as industrial robotic structures.

Basic algorithms of predictive control are available in GPC toolbox for MATLAB&Simulink. The toolbox contains both m-functions and c-coded functions and Simulink schemes.

Authors:

J. Böhm, K. Belda

Contact:

Software tools:

GPC toolbox for MATLAB&Simulink:
Odpovědnost za obsah: AS
Poslední změny: 09.03.2012

AS/research/advcontrol/literature

Advanced Control


Textbook:

Digital Self-tuning Controllers
Algorithms, Implementation and Applications

Series: Advanced Textbooks in Control and Signal Processing
Bobál, V., Böhm, J., Fessl, J., Macháček, J.
Springer 2005, XVI, 318 p. 187 illus., Softcover
ISBN: 1-85233-980-2
www book publisher site

About this textbook:

Adaptive control theory has developed significantly over the past few years; self-tuning control represents one branch of adaptive control that has been successfully applied in practice. Controller design requires knowledge of the plant to be controlled which is not always readily accessible; self-tuning controllers gather such information during normal operation and adjust controller designs on-line as required.
Digital Self-tuning Controllers are presented you with a complete course in self-tuning control, beginning with a survey of adaptive control and the formulation of adaptive control problems. Modelling and identification are dealt with before passing on to algebraic design methods and particular PID and linear-quadratic forms of self-tuning control. Finally, laboratory verification and experimentation will show you how to ground your theoretical knowledge in real plant control.

Further references on literature:

See Menu item Publications
Odpovědnost za obsah: AS
Poslední změny: 09.03.2012

AS/research/advising

Advising: Optimized Bayesian Dynamic Advising

Quality of maintaining of complex man-machine systems very much depends on experience, skills and performance of the human decision-makers (operators) managing the system. The task is complicated by complexity and dimensionality of the system managed as well as limited abilities of the operator.

The research concerns developing prescriptive theory of Bayesian dynamic decision-making (DM) under uncertainty that allows to construct efficient adaptive DM systems and to create systems supporting human decision-makers. The adopted approach relies on black-box modeling and on the availability of informative data. Specialization of the developed theory to dynamic mixtures combined with fully probabilistic design provides a practical tool of broad applicability.

The general idea is to process historical data available to model of the managed system behavior under various working conditions in a form of multi-dimensional probability mixtures (learning phase). The mixture learned and mixture expressing DM aims are employed to build an advisory mixture describing DM strategy (design phase). Decision designed by an advisory system is the prediction of advisory mixture made for the actually incoming data. Advising supposes providing this prediction in a suitable form to the decision-maker. The decision-maker is responsible to accept or to reject the offered advise.

The established solution has proven to able to cope with dynamically changing incompletely known multi-attribute environment and to learn and optimize dynamic decision-making strategy realized either by human being or automatically.

The developed generic optimized dynamic advising covers:

  • concise problem formulation
  • general solution of learning phase, complemented by its detailed elaboration to normal and Markov-chain mixtures
  • solution of learning initialization problem
  • general solution of the design phase, complemented by its detailed elaboration to normal and Markov-chain mixtures

The developed theory has been practically implemented into algorithmic and software toolsets (Mixtools) and tested on several full-scale applications (see Advisory system).

Contact:

M. Kárný

 
Odpovědnost za obsah: AS
Poslední změny: 04.08.2010

AS/research/multi

Multiple Participant Decision Making

Distributed dynamic decision-making and learning under uncertainty in complex and changing situations are emerging as the key competencies required to support future information-based systems. The Bayesian paradigm is acknowledged to provide a consistent and rigorous theoretical basis for joint learning and dynamic decision-making. The established theory already provides a class of efficient adaptive strategies. However, this approach fails to overcome the computational complexity barrier encountered in complex settings. This project aims to create a theoretical and algorithmic basis of a mathematically rigorous, but computationally tractable Bayesian distributed dynamic decision-making system, fully scalable in the number of local decision makers.

Objectives

The project aims to develop theory, algorithms and software for Bayesian distributed dynamic decision-making. It will make a qualitatively new step towards a generic theory of multi-participant, multi-step decision making in complex dynamic situations. The project will transform the theory into a generic algorithmic and software toolset.

The theory and its conversion into a practical tool will provide: 

  1.  Rigorous learning methods applicable both to quantitative and qualitative data. 
  2.  Learning of dynamic, mixed-data,probabilistic mixtures. 
  3.  Computationally tractable approximation to the fully probabilistic design of decision-making strategies. 
  4.  Design of a tool set for tailoring of the generic decision-making tools to a specific application. 
  5.  Numerically robust algorithmic counterparts of theoretical operations. 
  6.  Structured training materials for users of a varying level of competence and background. 
  7.  Portable software implementation allowing accommodation of new elements developed in the heterogeneous research situation.

Applications to non-trivial problems will be used to measure the project?s success. Simulation, pilot-plants and real-life (in rolling mill industry) tests will serve this purpose.

Contact:

M. Kárný

Odpovědnost za obsah: AS
Poslední změny: 04.08.2010

AS/news/seminars

Seminars

News  | Seminars ]

Nadpis Date&Time
CSKI seminar: Inverse modelling for source term reconstruction 20.10.2014 - 14:00
AS seminář: Distribuované dynamické odhadování v difuzních sítích 06.10.2014 - 11:00
AS seminář: Rozdělení časových rozestupů pro systémy interagujících částic 08.09.2014 - 11:00
ČSKI seminář: Částicové stochastické systémy spojované s modelovaním dopravních jevů 17.06.2014 - 14:00
AS seminář: Aproximace plně pravděpodobnostního návrhu pomocí metod lokální regrese 02.06.2014 - 11:00
ČSKI seminář: Jak to vidí počítač 20.05.2014 - 14:00
ČSKI seminář: Stochastické nelineární vlnové rovnice 06.05.2014 - 14:00
AS seminář: Odhad struktury lineárního modelu, jeho rozšíření a aplikace 05.05.2014 - 11:00
AS seminář: Simulační výstupy algoritmu řízení světelné signalizace NOMŘÍZ 07.04.2014 - 11:00
CSKI seminar: Classification of idempotent semiring modules with strongly independent basis 20.03.2014 - 14:00
AS seminář: Znovu k základům plně pravděpodobnostního návrhu 03.03.2014 - 11:00
AS seminář: Supra-Bayesovská kombinace pravděpodobnostních distribucí – pokračování 03.02.2014 - 11:00
AS seminar: Exact-Approximate Bayesian Inference for Gaussian Process Classifiers 09.12.2013 - 14:00
AS seminář: Optimalizace ekologie jízdy na základě průběžně měřených dat 02.12.2013 - 11:00
ČSKI seminář: Proč lidé počítají? Co lidé počítají? 19.11.2013 - 14:00
ČSKI seminář: O původu vnitřního uspořádání v systémech se sociálními interakcemi 05.11.2013 - 14:00
AS seminar: MELT SPINNING PROCESS: ANALYTICAL AND NUMERICAL SOLUTIONS 04.11.2013 - 11:00
ČSKI seminář: Kompoziční modely s nepřesností 22.10.2013 - 14:00
ČSKI seminář: Kauzální kompoziční modely 15.10.2013 - 14:00
CSKI seminar: Analysis of the Solution Map Governed by a Parametrized Differential Inclusion 08.10.2013 - 14:00
Odpovědnost za obsah: AS
Poslední změny: 04.01.2011

AS/research-main

Clusters of Research Oriented Activities

Bayesian Dynamic Decision Making

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used. Long-term research covers: i) theoretical support whole design leading to fully probabilistic design generalising Bayesian DM; ii) support of particular steps of DM, like structure estimation; iii) formulation and solutions specific tasks as like probabilistic support of operators or trading with futures; iv) algorithmisation facing, for instance, poorly informative data, local nature of models, approximate estimation of dynamic mixtures; v)  distributed DM, especially, performed by decision makers with limited cognitive abilities.      

Advanced Control

The inspected problems ranges from extansions of  linear control theories, over adaptive, numerically robust, linear-quadratic control, its extension to predictive controllers oriented towards mechatronic systems. The progress is driven by advanced applications oriented, for instance, towards rolling mills or electrical motors. They call for ellaborating various technigues like: i) non-linear filtering based on marginalised particle filtering or design of soft sensors; ii)  control design for specific, say, mechatronic systems or universal controlled high-dimensional dynamic mixture models; iii) inspecting dual and distributed variants of control desing.

Traffic Control

Strong research group oriented on traffic-control domain covers theoretical, algorithmic and application-specific aspects like: i) traffic-lights based adaptive hierarchical control of town traffic; ii) estimation of an exact position of vehicle facing GPS inaccessability; iii) personal advanced system supporting economical driving style.  

Nuclear Safety

Advanced physical modelling, custemisation of general techniques to Czech teritory, tailoring of advanced Bayesian technique for data asimilation, specific algorithms fighting with problem dimensionality are key techniques developed and used for solving nuclear safety problems, especially, for advising to authorities in case of nuclear (possibly chemical or even terroristic) threats.      

Medical applications

Bayesian techniques have been traditionally developed, refined and applied in variaty of medical, predominantly dignostics, problems.  Beign improtant on their own, they have served as test field with a a alck of universal (physical-like) models, very limited amount of very uncertain measured data of mixed nature and significant consequences for patients with dangerous diseases like thyroid gland cancer or lymphedema. 

Accumulation of Acquired Knowledge 

Complexity of the targeted research makes care about knowledge accumulation in software and educational material its indispenseable part.  

Odpovědnost za obsah: AS
Poslední změny: 15.02.2011

AS/applications-main

Application-oriented projects

In general, research activities in the department are always motivated by real-world application. Projects listed on this page are those that goes beyonds academic considerations and actually implemented the developed algorithms in real environment. List of theoretical projects is available here

List of former application-oriented projects

Odpovědnost za obsah: AS
Poslední změny: 28.01.2011

AS/applications-former

Former application-oriented projects

Odpovědnost za obsah: AS
Poslední změny: 28.01.2011

AS/research-former

Former research projects

Odpovědnost za obsah: AS
Poslední změny: 28.01.2011

AS/research/BayesDM

Bayesian Dynamic Decision Making

Dynamic decision making (DM) maps knowledge into DM strategy, which ensures reaching DM aims under given constraints. Under general conditions, Bayesian DM, minimizing expected loss over admissible strategies, has to be used. Existing limitations of the paradigm impede its applicability to complex DM as:

  1. Complexity of the information processing often crosses resources accessible.
  2. Quantification of domain-specific knowledge, aims and constraints is weakly supported. It concerns mapping of domain-specific elements on probabilistic distributions (pd).
  3. Methodology of the DM with multiple aims is incomplete.

The research aims to overcome these problems. It relies on distributed DM and fully probabilistic design (FPD) of strategies. The goal is to build a firm theoretical background of FPD of distributed DM strategies. Besides, it will enrich available results and unify them into internally consistent theory suitable for a flat cooperation structure.

This aim implies the main tasks:

  1. Inspection of conditions leading to FPD
  2. Extension of FPD to design with sets of ideal pds
  3. Design of computerized conversion of knowledge and aims into environment-describing and ideal pds
  4. Elaboration of theoretical framework for selecting cooperation tools

 

 

Active projects

Former projects

Odpovědnost za obsah: AS
Poslední změny: 14.02.2011

AS/applications/drives

Electrical drives: sensorless control

Electrical drives are part of everyday world. While the technology for their control is well known and reliable, new challenges are comming with new technology and new requirements. The always present pressure for better reliability, safety and cost of production and operation are the driving force for inovation.

The electrical drives are also good laboratory to test new theoretical results. We apply the results of reasearch in areas of:

The resulting algorithms help us to improve reliability of the drive in low speed regimes.

Contact:

V.Smidl

Odpovědnost za obsah: AS
Poslední změny: 12.04.2012

AS/news/plan-seminars

Plán seminářů

3.2.Vladimíra Sečkárová
3.3 Miroslav Kárný
7.4. Jan Přikryl
5.5. Václav Šmídl
2.6. Karel Macek
8.9. Pavel Hrabák
6.10.Kamil Dedecius 
3.11.Ondřej Tichý 
1.12.Ladislav Jirsa
  
Odpovědnost za obsah: AS
Poslední změny: 04.09.2014

AS/applications/car_transp

Current projects:

Car transport: Fuel consumption optimization

This research project aims at optimization of fuel consumption both from the economical and ecological points of view.

Contact:

 

Ecology of driving

The project aims at creation an advisory system for drivers of vehicles or other transportation means. The advisory system will measure the traffic variables and in dependence on them it will evaluate the ecological quality of driving. The advisory system will be based on a cluster model describing the data space. In this space, the data vectors are represented by points. The model describes and evaluates the point cluster that represent individual working regimes. According to the evaluation of these clusters, the quality of driving is assigned.

Contact:


Estimation of Vehicle Trajectory during GPS Signal Outages Based on Inertial Sensor Data

The aim of joint project under the research centre DAR   is to refine the information about the moving vehicle position obtained from global positioning system (GPS). In the case of signal outage, GPS does not work properly. During this outage, the vehicle position is estimated. The estimation is based on the principle of the inertial navigation (INS). Data required for the estimation are obtained from the following sources: (i) controller-area network (CAN) that provides a data from the vehicle sensors; (ii) external device that provides an acceleration and an angular velocity in the three axes from MEMS sensors; (iii) GPS navigation. All above mentioned devices are tightly connected with the vehicle.

Contact:

HMI – Human Machine Interface

Industrial case studies focused on an on-board HMI supporting interactions between driver and on-board systems, e.g. for measuring quantitative and qualitative parameters of driver behaviour. This area of research is mainly focused on investigation driver bio-physiological parameters including driver decoy. Development systems providing information about actual, vehicle-related and predicted HMI parameters based on communication on car bus with support HMI Simulator. HMI simulator is understand as equipment, on which is possible in laboratory way objectively measure driver stress induced by different disturbing effects and subsequently judge influence of stress on safety factors.

Contact:

Industrial partners:

Doc. Ing. Jaroslav Machan, CSc. / ŠKODA AUTO a.s.  Head of Department TC

Ing. Pavel Nedoma, PhD / ŠKODA AUTO a.s. Deputy Head of Department TC

Odpovědnost za obsah: AS
Poslední změny: 25.05.2012

AS/research/uniform

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.

However, the modelled quantities are often physically constrained. Then, methods based on the Gaussian distribution with unbounded support do not work properly and they have to be adapted. The alternative sophisticated algorithms based on “unknown-but-bounded errors” principle address the same problem but they are poorly harmonised
with the subsequent dynamic decision making (like control, prediction of hidden quantities or future measurements) to which any learning serves.

This research operates in probabilistic framework while coping with bounded uncertainties and physically constrained quantities. Here, learning algorithms for models with constraints are constructed that (i) are based on the Bayesian principle, (ii) are recursive and (iii) have relatively simple setting and maintenance, (iv) are at disposal to subsequent
dynamic decision making.

Contact:

L. Pavelková

Odpovědnost za obsah: AS
Poslední změny: 05.05.2014

AS/applications/sensor

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). Thickness measurement devices minimizing or eliminating the delay are very expensive, therefore other ways of output thickness estimation/prediction are utilized. The rolling process is modelled using either well-established time-proven principles or simple black-box linear regressive models. The models are treated as probablilistic mixture and their thickness predictions are merged by the dynamic model weights in the mixture, respecting instantaneous operating state of the plant. At the same time, measurement methodology of several quantities (speed, pressure, ...) has been revised and improvement in precision and reliability indication has been achieved.

This application is a joint effort of ÚTIA, Compureg Plzeň, Jožef Stefan Institute and INEA, both from Ljubljana, Slovenia.

Kontakt: Jirsa, Dedecius

Odpovědnost za obsah: general
Poslední změny: 26.10.2012

AS/publications/history

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.

V. Peterka and S. Bláha. Synthesis of regulation loops according to quadratic criterion. Kybernetika, 1:127-143, 1966. Download.

V. Peterka. New approach to identification of discrete systems. Kybernetika, 4:113-135, 1968. Download.

V. Peterka. Use of pseudo-random signals for identification of dynamic systems. Kybernetika, 5:406-421, 1969. Download.

A.V. Balakrishnan and V. Peterka. Identification in automatic control systems. pages 1-43. 1969. Download.

V. Peterka. Tally... In Preprints of the 2nd IFAC Symposium on Identification and Process Parameter Estimation, page paper ... Prague, 1970. Download.

V. Peterka. On steady-state minimum variance control strategy. Kybernetika, 8:219-231, 1972. Download.

V. Peterka. A square-root filter for real-time multivariable regression. Kybernetika, 11:53-67, 1975. Download.

V. Peterka. Experience accumulation for decision making in multivariate time series. volume 7, pages 143-159. Laxenburg, 1978. Download.

V. Peterka and M. Kárný. Bayesian system classification. In Preprints of the 5th IFAC Symposium on Identification and System Parameter Estimation, volume 1, pages 349-356. Darmstadt, 1979. Download.

V. Peterka. Bayesian system identification. In Preprints of the 5th IFAC Symposium on Identification and System Parameter Estimation, volume 1, pages 99-114. Darmstadt, 1979. Download.

V. Peterka. Real-time parameter estimation and output prediction for ARMA-type system models. Kybernetika, 17:526-533, 1981. Download.

V. Peterka. Bayesian approach to system identification. pages 1-80. 1981. Download.

J. Böhm and M. Kárný. Self-tuning regulators with restricted inputs. Kybernetika, 18(6):529-544, 1982. Download.

R. Kulhavý and M. Kárný. Tracking of slowly varying parameters by directional forgetting. In Preprints of the 9th IFAC World Congress, volume X, pages 178-183. Budapest, 1984. Download.

J. Böhm, A. Halousková, M. Kárný, and V. Peterka. Simple LQ self-tuning controllers. In Preprints of 9th IFAC World Congress, volume VII, pages 171-176. Budapest, 1984. Download.

V. Peterka. Algorithms for LQG self-tuning control based on input-output delta models. In Proc. of 2nd IFAC Workshop on Adaptive Systems in Control and Signal Processing, pages 13-18. Lund, Sweden, 1986. Download.

R. Kulhavý. Restricted exponential forgetting in real-time identification. Automatica, 23(5):589-600, 1987. Download.

V. Peterka. Self-tuning control with alternative sets of uncertain process models. In Proc. of IFAC Symposium on Adaptive Systems in Control and Signal Processing, pages 409-414. Glasgow, UK, 1989. Download.

R. Kulhavý and E. Kliokys. Tracking of time-varying parameters in delta models. Problems of Control and Information Theory, 28(2):107-123, 1989. Download.

E. Kliokys and R. Kulhavý. Bayesian filtering for discrete-time systems with random structure. In IFAC Symposium Adaptive Control and Signal Processing, pages 611-615. Glasgow, 1989. Download.

J. Böhm, M. Kárný, and R. Kulhavý. Practically-oriented LQ self-tuners. In Preprints of the IFAC Workshop on Evaluation of Adaptive Control Strategies in Industrial Applications. Tbilisi, 1989. Download.

V. Peterka. Predictive and LQG optimal control: equivalences, differences and improvements. In D.Henrichsen and B.Mårtenson, editors, Proc. of an Int. Workshop on Control of Uncertain Systems, pages 221 - 244. Birkhäuser, Boston - Basel - Berlin, 1990. Download.

Odpovědnost za obsah: AS
Poslední změny: 28.10.2012

AS/softwaretools/image_sequences

Software for blind source separation and image sequence decomposition

This page contains algorithms published in different papers.

Sparse Blind Source Separation and Deconvolution Using Sparsity Priors

Algorithm solving blind source separation problem with convolution model using sparsity priors. Generalization of estimative procedure over previous method is given. See describtion as well as download link  here.

 

Blind Compartment Model Separation

Algorithm presented in paper Tichý O., Šmídl V., Šámal M. : Model-based Extraction of Input and Organ Functions in Dynamic Medical Imaging , Computational Vision and Medical Image Processing (VipIMAGE 2013), p. 75-80, IV ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, (Funchal, PT, 14.10.2013-16.10.2013). The code can be downloaded from here.

 

Sparse Blind Source Separation and Deconvolution

Algorithm presented in Šmídl Václav, Tichý Ondřej : Sparsity in Bayesian Blind Source Separation and Deconvolution , Machine Learning and Knowledge Discovery in Databases, p. 548-563, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013), (Praha, CZ, 24.09.2013-26.09.2013). The code can be downloaded from here.

Orthogonal Variational PCA

This code uses orthogonal probabilistic model of PCA to automatically determine the number of relevant principal components. See report for more details. The code can be downloaded from here.

 

Odpovědnost za obsah: AS
Poslední změny: 15.09.2014

Cenu Werner von Siemens získal Ondřej Tichý

CenaSiemens_OT

Společnost Siemens a Fórum průmyslu a VŠ vyhlásili 13. ročník soutěže o nejlepší bakalářské, diplomové a doktorské práce „Cena Siemens – Werner von Siemens Excellence Award 2010“.

Schedule & Presentations

Time

Title

Authors

7:30—7:50

Opening session

Organisers

7:50—8:20

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