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Statements

Subject Item
n2:MEB090607
rdf:type
n18:Projekt
rdfs:seeAlso
http://www.isvav.cz/projectDetail.do?rowId=MEB090607
dcterms:description
An enduring problem in FDI is that of characterizing the dynamics of complex systems. This is a key step towards feature extraction. The difficulty stems from the fact that in many cases it is not possible to derive a suitable model from the first principles. Given historical data, the typical way to proceed is via data projection into latent structures or by using integral transforms (orthogonal decomposition). Two main topics are addressed within the proposal: (1) Employment of Bayesian framework as systematic approach to feature extraction via exploiting on-line and historical process data available. The considered solution supposes dynamic probabilistic mixture modelling of the underlying process. The probabilistic extraction of information from process data allows us to overcome the dependence on application and does not require detailed knowledge of system dynamics. Furthermore, the related algorithms and used techniques mostly have a long tradition and already proved to be efficient and acc An enduring problem in FDI is that of characterizing the dynamics of complex systems. This is a key step towards feature extraction. The difficulty stems from the fact that in many cases it is not possible to derive a suitable model from the first principles. Given historical data, the typical way to proceed is via data projection into latent structures or by using integral transforms (orthogonal decomposition). Two main topics are addressed within the proposal: (1) Employment of Bayesian framework as systematic approach to feature extraction via exploiting on-line and historical process data available. The considered solution supposes dynamic probabilistic mixture modelling of the underlying process. The probabilistic extraction of information from process data allows us to overcome the dependence on application and does not require detailed knowledge of system dynamics. Furthermore, the related algorithms and used techniques mostly have a long tradition and already proved to be efficient and acc
dcterms:title
BAYESIAN DECISION MAKING TO SUPPORT CHANGE DETECTION IN COMPLEX MANUFACTURING SYSTEMS BAYESIAN DECISION MAKING TO SUPPORT CHANGE DETECTION IN COMPLEX MANUFACTURING SYSTEMS
skos:notation
MEB090607
n3:aktivita
n7:ME
n3:celkovaStatniPodpora
n13:celkovaStatniPodpora
n3:celkoveNaklady
n13:celkoveNaklady
n3:datumDodatniDoRIV
2010-05-07+02:00
n3:druhSouteze
n21:VS
n3:duvernostUdaju
n12:S
n3:fazeProjektu
n17:69263497
n3:hlavniObor
n9:BD
n3:hodnoceniProjektu
n10:U
n3:kategorie
n11:VV
n3:klicovaSlova
Modelling of complex systems; Fault detection; Bayesian estimation; Adaptive control
n3:partnetrHlavni
n15:ico%3A67985556
n3:pocetKoordinujicichPrijemcu
0
n3:pocetPrijemcu
1
n3:pocetSpoluPrijemcu
0
n3:pocetVysledkuRIV
0
n3:pocetZverejnenychVysledkuVRIV
0
n3:posledniUvolneniVMinulemRoce
2008-10-29+01:00
n3:prideleniPodpory
n14:9186%2F2008-32
n3:sberDatUcastniciPoslednihoRoku
n4:2008
n3:sberDatUdajeProjZameru
n4:2009
n3:soutez
n6:SMSM2008ME3
n3:statusZobrazovaneFaze
n16:DUU
n3:typPojektu
n20:P
n3:ukonceniReseni
2008-12-31+01:00
n3:vedlejsiObor
n9:BC n9:JS
n3:zahajeniReseni
2008-01-01+01:00
n3:zhodnoceni+vysledku+projektu+dodavatelem
Projekt podpořil výměnu zkušeností v oblasti řízení a rozhodování. Navíc, vyřešil úlohu stavové filtrace z neznámými maticemi modelu, přispěl k odhadu stavu pro smíšená data, navrhl částečné zapomínání pro jednotlivé parametry měnící se různě rychle. The project yielded an exchange of experience on decision methods and solved state filtering with unknown model matrices, developed partial forgetting for the case of different variability of individual parameters.
n3:zivotniCyklusProjektu
n19:JU
n3:klicoveSlovo
Fault detection Bayesian estimation Modelling of complex systems