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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Interface design and construction for viscous vacuum gauge data refinement
 Pohorský, Tomáš ; Věchet, Stanislav (referee) ; Appel, Martin (advisor)
This thesis deals with the design and implementation of a viscous vacuum gauge data refinement device that is used in the laboratory of the Czech Metrology Institute. The aim of the thesis is to get acquainted with the principle and operation of the viscous etalon with the spinning rotor gauge (SRG). Another aim is to design a device which, using the current value of the ball rotation frequency in the vacuum gauge and using the current temperature to refine the vacuum gauge value. This refined value is then displayed on a display that is part of the device and also allows it to be transmitted over a serial link to the parent system (e.g. a personal computer).
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Synthesis of digital landscape surface data
 Šebesta, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
A procedural generation of landscapes often meets a need for real spatial data at finer resolution that data available at the moment. We introduce a method that refines the spatial data at the coarse resolution into the finer resolution utilizing other data sources which are already at the better resolution. We construct weighted local linear statistical models from both the coarse and utility data and use the by- models-learned dependencies between the data sources to predict the needed data at better resolution. To achieve higher computational speed and evade utility data imperfection, we utilize truncated singular value decomposition which reduce a dimensionality of the data space we work with. The~method is highly modifiable and its application shows plausible real-like results. Thanks to this, the method can be of practical use for simulation software development. Powered by TCPDF (www.tcpdf.org)
Detailed record
Synthesis of digital landscape surface data
 Šebesta, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
A procedural generation of landscapes often meets a need for real spatial data at finer resolution that data available at the moment. We introduce a method that refines the spatial data at the coarse resolution into the finer resolution utilizing other data sources which are already at the better resolution. We construct weighted local linear statistical models from both the coarse and utility data and use the by- models-learned dependencies between the data sources to predict the needed data at better resolution. To achieve higher computational speed and evade utility data imperfection, we utilize truncated singular value decomposition which reduce a dimensionality of the data space we work with. The~method is highly modifiable and its application shows plausible real-like results. Thanks to this, the method can be of practical use for simulation software development. Powered by TCPDF (www.tcpdf.org)
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