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Adaptace programového vybavení pro hodnocení radiologických důsledků mimořádných úniků radionuklidů ze skladů vyhořelého jaderného paliva
Pecha, Petr; Pechová, E.
2018 - Czech
Revize implicitní grupy radionuklidů a rozšíření o typické postulované mimořádné úniky ze skladů vyhořelého paliva. bylo provedeno rozšíření databáze radionuklidů systému HARP o další nejdůležitější dlouhodobé radionuklidy. V zásadě se jednalo o výběr štěpných a aktivačních produktů a dále o transurany ze skupiny aktinidů. Jsou konstatovány velké neurčitosti v odhadech zdrojových členů úniku pro případy vyhořelého paliva. Je provedeno vzájemné srovnání pro obálkové scénáře s výsledky evropského environmentálního kódu COSYMA pro rozšířenou grupu transuranů.. Analysis of implicit group of radionuclides and its extension included nuclides with long half-llive of decay. Selection from spent fuel inventories. Selection from fision products, transurans an actinide group. Estimation of possible uncertainties. Comparison analysis is done betřween HARP and European COSYMA codes.
Keywords:
Spent fuel; discharges from repository; radiological impact
Fulltext is available at external website.
Adaptace programového vybavení pro hodnocení radiologických důsledků mimořádných úniků radionuklidů ze skladů vyhořelého jaderného paliva
Revize implicitní grupy radionuklidů a rozšíření o typické postulované mimořádné úniky ze skladů vyhořelého paliva. bylo provedeno rozšíření databáze radionuklidů systému HARP o další nejdůležitější ...
Heuristics in blind source separation
Kautský, Václav; Štěch, Jakub
2018 - English
This paper deals with application of heuristic algorithms (DEBR, MCRS) in blind source separation (BSS). BSS methods focus on a separation of the (source) signal from a linear mixture. The idea of using heuristic algorithms is introduced on the independent component extraction (ICE) model. The motivation for considering heuristics is to obtain an initial guess needed by many ICE algorithms. Moreover, the comparison of this initialization, and other algorithms accuracy is performed.\n
Keywords:
Blind Source Separation; DEBR; Independent Component Extraction
Fulltext is available at external website.
Heuristics in blind source separation
This paper deals with application of heuristic algorithms (DEBR, MCRS) in blind source separation (BSS). BSS methods focus on a separation of the (source) signal from a linear mixture. The idea of ...
Validation of comprehensive energy management system based on cloud-sourced information
Nedoma, P.; Herda, Z.; Franc, Z.; Plíhal, Jiří
2018 - English
The main research activity was devoted to develop an application that would enable testing interface between OIKOS board (based on the AURIXTM) and the dissemination module represented by Skoda vehicle demonstrator through serial port RS232. The testing was based on sending the GPS coordinates to the OIKOS unit and receiving recommended speed profile for the given track. While dissemination unit has received GPS coordinates, AURIX chip has sent back messages with prediction of possible speed profile. Further tasks included verification other forms of transmission, such as Wi-Fi, Bluetooth, Ethernet.\n\n
Keywords:
OIKOS board; dissemination module; GPS
Fulltext is available at external website.
Validation of comprehensive energy management system based on cloud-sourced information
The main research activity was devoted to develop an application that would enable testing interface between OIKOS board (based on the AURIXTM) and the dissemination module represented by Skoda ...
Experiment: Cooperative Decision Making via Reinforcement Learning
Berka, Milan
2018 - English
This report inspects cooperative decision making task using reinforcement learning. It serves for comparison with methodology based on fully probabilistic design of decision strategies.
Keywords:
decision making; reinforcement learning; cooperation
Fulltext is available at external website.
Experiment: Cooperative Decision Making via Reinforcement Learning
This report inspects cooperative decision making task using reinforcement learning. It serves for comparison with methodology based on fully probabilistic design of decision strategies.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies
Kárný, Miroslav; Hůla, František
2018 - English
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy properly balances exploitation with exploration. The inherent dimensionality\ncurse of decision making under incomplete knowledge prevents the realisation of the optimal design.
Keywords:
Exploitation; Exploration; Bayesian estimation; Adaptive systems; Fully probabilistic design; Kullback-Leibler divergence; Decision policy; Markov decision process
Fulltext is available at external website.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy ...
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 ...
Comparison of Shenoy’s Expectation Operator with Probabilistic Transforms and Perez’ Barycenter
Jiroušek, R.; Kratochvíl, Václav
2018 - English
Shenoy’s paper published in this Proceedings of WUPES 2018 introduces an operator that gives instructions how to compute an expected value in the Dempster-Shafer theory of evidence. Up to now, there was no direct way to get the expected value of a utility function in D-S theory. If eeded, one had to find a probability mass function corresponding to the considered belief function, and then - using this probability mass function - to compute the classical probabilistic expectation. In this paper, we take four different approaches to defining probabilistic representatives of a belief function and compare which one yields to the best approximations of Shenoy’s expected values of various utility functions. The achieved results support our conjecture that there does not exist a probabilistic representative of a belief function that would yield the same expectations as the Shenoy’s new operator.
Keywords:
expected utility; Dempster-Shafer theory; Shenoy's operator
Fulltext is available at external website.
Comparison of Shenoy’s Expectation Operator with Probabilistic Transforms and Perez’ Barycenter
Shenoy’s paper published in this Proceedings of WUPES 2018 introduces an operator that gives instructions how to compute an expected value in the Dempster-Shafer theory of evidence. Up to now, there ...
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 ...
Representations of Bayesian Networks by Low-Rank Models
Tichavský, Petr; Vomlel, Jiří
2018 - English
Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks defined as the product of these CPTs may become intractable by conventional methods of BN inference because of their dimensionality. In many cases, however, these probability tables constitute tensors of relatively low rank. Such tensors can be written in the so-called Kruskal form as a sum of rank-one components. Such representation would be equivalent to adding one artificial parent to all random variables and deleting all edges between the variables. The most difficult task is to find such a representation given a set of marginals or CPTs of the random variables under consideration. In the former case, it is a problem of joint canonical polyadic (CP) decomposition of a set of tensors. The latter fitting problem can be solved in a similar manner. We apply a recently proposed alternating direction method of multipliers (ADMM), which assures that the model has a probabilistic interpretation, i.e., that all elements of all factor matrices are nonnegative. We perform experiments with several well-known Bayesian networks.\n\n
Keywords:
canonical polyadic tensor decomposition; conditional probability tables; marginal probability tables
Fulltext is available at external website.
Representations of Bayesian Networks by Low-Rank Models
Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks defined as the product of these CPTs may become intractable by conventional ...
Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains
Sladký, Karel
2018 - English
In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential utility function with a given risk sensitivitycoefficient (so-called risk-sensitive models). If the risk sensitivity coefficient equals zero (risk-neutral case) we arrive at a standard Markov decision process. Then we can easily obtain necessary and sufficient mean reward optimality conditions and the variability can be evaluated by the mean variance of total expected rewards. For the risk-sensitive case, i.e. if the risk-sensitivity coefficient is non-zero, for a given value of the risk-sensitivity coefficient we establish necessary and sufficient optimality conditions for maximal (or minimal) growth rate of expectation of the exponential utility function, along with mean value of the corresponding certainty equivalent. Recall that in this case along with the total reward also its higher moments are taken into account.
Keywords:
continuous-time Markov decision chains; exponential utility functions; certainty equivalent; mean-variance optimality; connections between risk-sensitive and risk-neutral optimality
Fulltext is available at external website.
Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains
In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential ...
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