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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Configuration and diagnostics for Smart Grid
 Hrbek, Martin ; Šťastný, Ladislav (referee) ; Franek, Lešek (advisor)
This thesis designs a solution for configuration and diagnostics for the Smart Grid. It creates use case diagram for configuration and diagnostics and designs individual software modules of the system. The thesis informs a developer about suitable devices for configuration and diagnostics for the Smart Grid, especially about rugged handhelds. The thesis chooses suitable software tools for development of designed modules and contains recommendations for developers, how to use this tools. The realization of one of the designed modules verifies the correctness of the designed solutions and approaches.
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Configuration and diagnostics for Smart Grid
 Hrbek, Martin ; Šťastný, Ladislav (referee) ; Franek, Lešek (advisor)
This thesis designs a solution for configuration and diagnostics for the Smart Grid. It creates use case diagram for configuration and diagnostics and designs individual software modules of the system. The thesis informs a developer about suitable devices for configuration and diagnostics for the Smart Grid, especially about rugged handhelds. The thesis chooses suitable software tools for development of designed modules and contains recommendations for developers, how to use this tools. The realization of one of the designed modules verifies the correctness of the designed solutions and approaches.
Detailed record
Robust portfolio optimization model
 Löw, Alexandr ; Pelikán, Jan (advisor) ; Fábry, Jan (referee)
Portfolio optimization models aim to optimally distribute capital among selected stocks, bonds and other securities and financial products offered on financial markets. An important factor in the optimization is the risk, which is by definition very abstract concept and is very difficult to quantify. Robust portfolio optimization model is based on the general robust binary model. The robustness of this model lies in a two-stage optimization, where every solution is subject to maximization of losses and from these pessimistic estimates such a solution is selected that best meets the user's criteria, in our case total return of the portfolio.
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