Number of found documents: 1664
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Tourist interest in illicit zone of ice caves
Nováková, Eva; Kuda, František; Kubalíková, Lucie
2018 - English
Ledové sluje (Ice Caves) in the Podyjí National Park represents one of the most spectacular sites within the area. It consists of the large boulder field and several pseudokarst caverns on the north-western slope of the ridge that are very important from the ecological and geomorphological point of view. The access to the site is restricted for the visitors of National Park as there exists a risk of damage and disturbance of these unique phenomena; tourist can use the marked paths leading around the site, they can reach the top part of the ridge.\nCurrently, there are only several persons who have legal access to the site (employees of the National Park Administration and other researches with the permission issued by NP Administration). However, the installed sensor that counts the passages proved that the site is visited more frequently than it should be. The number of people who visit this site (situated within the first zone of National Park where there is no marked path and so the access is forbidden by decree) is quite alarming. Based on these findings, some proposals for the solution of this unfavourable situation are proposed and other possibilities how to avoid this undesirable phenomenon are discussed.\n Keywords: Podyji National Park; restricted area; passages monitoring Available in a digital repository NRGL
Tourist interest in illicit zone of ice caves

Ledové sluje (Ice Caves) in the Podyjí National Park represents one of the most spectacular sites within the area. It consists of the large boulder field and several pseudokarst caverns on the ...

Nováková, Eva; Kuda, František; Kubalíková, Lucie
Ústav geoniky, 2018

Dynamic Bayesian Networks for the Classification of Sleep Stages
Vomlel, Jiří; Kratochvíl, Václav
2018 - English
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy. Keywords: Dynamic Bayesian Network; Sleep Analysis Fulltext is available at external website.
Dynamic Bayesian Networks for the Classification of Sleep Stages

Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced ...

Vomlel, Jiří; Kratochvíl, Václav
Ústav teorie informace a automatizace, 2018

Employing Bayesian Networks for Subjective Well-being Prediction
Švorc, Jan; Vomlel, Jiří
2018 - English
This contribution aims at using Bayesian networks for modelling the relations between the individual subjective well-being (SWB) and the individual material situation. The material situation is approximated by subjective measures (perceived economic strain, subjective evaluation of the income relative to most people in the country and to own past) and objective measures (household’s income, material deprivation, financial problems and housing defects). The suggested Bayesian network represents the relations among SWB and the variables approximating the material situation. The structure is established based on the expertise gained from literature, whereas the parameters are learnt based on empirical data from 3rd edition of European Quality of Life Study for the Czech Republic, Hungary, Poland and Slovakia conducted in 2011. Prediction accuracy of SWB is tested and compared with two benchmark models whose structures are learnt using Gobnilp software and a greedy algorithm built in Hugin software. SWB prediction accuracy of the expert model is 66,83%, which is significantly different from no information rate of 55,16%. It is slightly lower than the two machine learnt benchmark models. Keywords: Subjective well-being; Bayesian networks Fulltext is available at external website.
Employing Bayesian Networks for Subjective Well-being Prediction

This contribution aims at using Bayesian networks for modelling the relations between the individual subjective well-being (SWB) and the individual material situation. The material situation is ...

Švorc, Jan; Vomlel, Jiří
Ústav teorie informace a automatizace, 2018

Platební regulační mechanismus jako zdroj zvyšování platů ve zdravotnictví
Grim, Jiří
2018 - Czech
Princip zdravotního pojištění předpokládá, že se pacient v případě potřeby obrátí na lékaře, který mu poskytne odbornou pomoc, přičemž výkon lékaře, výdaje za léky a doplňující vyšetření proplácí zdravotní pojišťovna. Výsledkem je spontánní nárůst nákladů zdravotní péče u nás dobře známý z devadesátých let. Je zřejmé, že v systému, ve kterém o poskytnuté zdravotní péči musí rozhodovat lékaři v kontaktu s pacienty a její náklady následně hradí zdravotní pojišťovny, chybí záporná zpětná vazba, která by působila proti růstu nákladů. Důsledkem této hrubé systémové chyby je trvalý tlak na zvyšování výdajů za poskytnutou zdravotní péči a hrozící platební neschopnost nutí zdravotní pojišťovny zavádět regulační opatření k omezení růstu nákladů. The principle of health insurance presupposes that the patient will contact a doctor who will provide him / her with professional help, whereby the doctor, medical expenses and additional examinations are paid by the health insurance company. The result is a spontaneous increase in health care costs well-known in the nineties. It is clear that there is no negative feedback in the system where the healthcare provided must be made by doctors in contact with patients and its costs are being covered by health insurance companies. As a result of this gross systemic error, there is a continuing pressure to increase healthcare spending and imminent insolvency forces the health insurers to introduce regulatory measures to curb the cost increase. Keywords: Medical care; finance; regulation mechanismus Fulltext is available at external website.
Platební regulační mechanismus jako zdroj zvyšování platů ve zdravotnictví

Princip zdravotního pojištění předpokládá, že se pacient v případě potřeby obrátí na lékaře, který mu poskytne odbornou pomoc, přičemž výkon lékaře, výdaje za léky a doplňující vyšetření proplácí ...

Grim, Jiří
Ústav teorie informace a automatizace, 2018

Použití modelu efektivní trhliny k analýze odezvy válcových těles se šípovým zářezem
Halfar, P.; Frantík, P.; Šimonová, H.; Daněk, P.; Keršner, Z.; Vavro, Leona; Vavro, Martin
2018 - Czech
Příspěvek uvádí aplikaci modelu efektivní trhliny k analýze odezvy válcových těles se\nšípovým zářezem namáhaných v tříbodovém ohybu. Jednalo se o testy tří zkušebních\ntěles z pískovce z lokality Javorka a tří betonových těles z nosné konstrukce nádražní\nbudovy v Ostravě-Vítkovicích. K výpočtům byl použit akademický software\nchevroncylinder na bázi metody konečných prvků. This paper introduces the application of an effective crack model to analyse the response\nof chevron-notched cylindrical specimens loaded in three-point bending. There were\nanalysed the three sandstone specimens from the Javorka locality and three concrete\nspecimens from the building structure of the Ostrava-Vítkovice railway station. The\nchevroncylinder software based on the finite element method was used for the\ncalculations. Keywords: fracture test; effective crack model; chevroncylinder software; sandstone; concrete Available in digital repository of the ASCR
Použití modelu efektivní trhliny k analýze odezvy válcových těles se šípovým zářezem

Příspěvek uvádí aplikaci modelu efektivní trhliny k analýze odezvy válcových těles se\nšípovým zářezem namáhaných v tříbodovém ohybu. Jednalo se o testy tří zkušebních\ntěles z pískovce z lokality ...

Halfar, P.; Frantík, P.; Šimonová, H.; Daněk, P.; Keršner, Z.; Vavro, Leona; Vavro, Martin
Ústav geoniky, 2018

Vyhodnocování grantové soutěže pomocí otevřené expertní databáze
Grim, Jiří
2018 - Czech
Keywords: open expert database; research funding; grants Fulltext is available at external website.
Vyhodnocování grantové soutěže pomocí otevřené expertní databáze

Grim, Jiří
Ústav teorie informace a automatizace, 2018

A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions
Vlček, Jan; Lukšan, Ladislav
2018 - English
Keywords: Unconstrained minimization; variable metric methods; limited-memory methods; the repeated BFGS update; global convergence; numerical results Available in digital repository of the ASCR
A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions

Vlček, Jan; Lukšan, Ladislav
Ústav informatiky, 2018

Soupis publikovaých prací pana prof. Ing. Mirko Nováka, DrSc. zpracovaný ke dni 13. dubna 2018 knihovnou Ústavu informatiky AV ČR, v. v. i. s ohledem na dostupnost uvedených prací
Nývltová, Ludmila; Ramešová, Nina; Šírová, Tereza
2018 - Czech
Keywords: bibliografie Available in digital repository of the ASCR
Soupis publikovaých prací pana prof. Ing. Mirko Nováka, DrSc. zpracovaný ke dni 13. dubna 2018 knihovnou Ústavu informatiky AV ČR, v. v. i. s ohledem na dostupnost uvedených prací

Nývltová, Ludmila; Ramešová, Nina; Šírová, Tereza
Ústav informatiky, 2018

Numerical solution of generalized minimax problems
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2018 - English
Keywords: Numerical optimization; nonlinear approximation; nonsmooth optimization; generalized minimax problems; recursive quadratic programming methods; interior point methods; smoothing methods; algorithms; numerical experiments Available in digital repository of the ASCR
Numerical solution of generalized minimax problems

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
Ústav informatiky, 2018

Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
Plajner, Martin; Vomlel, Jiří
2018 - English
Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide good results. In this paper we present a method to improve learning results for small data sets by using additional information about the modelled system. This additional information is represented by monotonicity conditions which are restrictions on parameters of the model. Monotonicity simplifies the learning process and also these conditions are often required by the user of the system to hold. \n\nIn this paper we present a generalization of the previously used algorithm for parameter learning of Bayesian Networks under monotonicity conditions. This generalization allows both parents and children in the network to have multiple states. The algorithm is described in detail as well as monotonicity conditions are.\n\nThe presented algorithm is tested on two different data sets. Models are trained on differently sized data subsamples with the proposed method and the general EM algorithm. Learned models are then compared by their ability to fit data. We present empirical results showing the benefit of monotonicity conditions. The difference is especially significant when working with small data samples. The proposed method outperforms the EM algorithm for small sets and provides comparable results for larger sets. Keywords: Bayesian networks; Learning model parameters; monotonicity condition Fulltext is available at external website.
Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions

Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide ...

Plajner, Martin; Vomlel, Jiří
Ústav teorie informace a automatizace, 2018

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