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SPMS 2023

26–30 Jun 2023
Sloup v Čechách
Europe/Prague timezone

Local Organizing Committee

Session

Dynamic Decision Making

29 Jun 2023, 09:15

Presentation materials

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  1. 1. Beta- Version of Application for Preference Tailoring
    Tereza Siváková
    29/06/2023, 09:15

    This presentation focuses on the specification of user's preferences in decision-making, particularly in cases where users have limited knowledge of decision-making (DM) theory and express their preferences incompletely.
    The decision-making task is solved by a fully probabilistic design (FPD), which models a closed loop between the user and the system. The FPD introduces an ideal probability...

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  2. 16. Does quantum ​version of decision theory make sense?
    Mr Aleksej Gaj (Department of Mathematics, FNSPE, Czech Technical University in Prague)
    29/06/2023, 09:35

    Contemporary decision making (DM) theory stands on classical probability. However, it has been shown that there is a variety of situations when the decision theory fails to explain some psychological and cognitive effects observed in human decision making. Other aspects not covered by the classical approach are that the results of merging information depend on the order of merging, or that the...

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  3. 10. Optimal control systems for rolling stock
    Jiří Nábělek
    29/06/2023, 09:55

    In this presentation, we will focus on mathematic approaches to optimal control systems for rolling stock. We will discuss several formulations of optimal control problems for trains and trams. We will then look into optimising the energy consumption under various constraining conditions, including the available power and force, time schedule, and speed limits. We will present the tram optimal...

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  4. 9. Power Spectral Features of Musculoskeletal Disorders and Their Classification
    Nichita Vatamaniuc
    29/06/2023, 10:15

    The analysis of biomedical data on musculoskeletal disorders using data provided by motion sensors. The patient's features are extracted from the signal represented as power in dB. The feature dimensionality is then reduced using Data Whitening and classified into two classes: healthy and diseased. Two different methods are used for classification: SVM and MLP.

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