Drinking regime in terms of evidence based medicine

Bachelor thesis "Drinking regime in terms of evidence based medicine" is focused on complex take on the topic of fluid intake based on current knowledge and most recent studies. The content of this work consists of body water function and hydration theoretical summary. Based on this summary is experimental part which should confirm the relationship between hydration and performance.Bakalářská práce s názvem "Pitný režim z pohledu evidence based medicine" je zaměřena na komplexní pojetí tématu příjmu tekutin v perspektivě posledních poznatků a provedených studií. Obsahem práce je shrnutí teoretických poznatků o funkci vody v těle a hydrataci. Z těchto poznatků pak vychází praktická část, která má potvrdit závislost výkonu na stavu hydratace.Klinika rehabilitace a tělovýchovného lékařstvíDepartment of Rehabilitation and Sports MedicineSecond Faculty of Medicine2. lékařská fakult

Structure change of the insulating composite

Modern power electric drives brought advantages in induction motor control. In the same time appeared problems with high frequency square waveform voltage (pulse stress) produced by the voltage converters. Voltage converters produce repetitive pulses with high level of voltage rise fronts (slew rates). Rise fronts attained values of up to tens kilovolts per microsecond and voltage pulse repetition frequency up to some tens of kilohertz. This technology is an advantage for a drive control. Significant is the impact of these voltage waveforms on the motor insulations. Degradation of the main wall insulation can reduce the reliability of the electric motor and whole drive. In this paper is discussed one possible solution. The promising modification in the insulation material structure is presented in the paper

Data archiving using longest common subsequence

This thesis examines existing algorithms for solving the problem of the longest common subsequence. It tries to apply them to the problem of effective archiving of data which differ a little, because they are, for example, just versions of one file. There are also new methods suggested. Those are based on the examined algorithms. Then the algorithms are compared according to the reached results and their running time

Data archiving using longest common subsequence

Práce zkoumá existující algoritmy pro řešení problému nejdelší společné podposloupnosti. Snaží se je aplikovat při řešení problému efektivního ukládání dat, která se velmi málo liší. Například se jedná o verze jednoho souboru. Rovněž jsou zde navrženy nové metody vycházející ze zkoumaných algoritmů. Algoritmy jsou pak srovnávány podle dosažených výsledků a rychlosti běhu.This thesis examines existing algorithms for solving the problem of the longest common subsequence. It tries to apply them to the problem of effective archiving of data which differ a little, because they are, for example, just versions of one file. There are also new methods suggested. Those are based on the examined algorithms. Then the algorithms are compared according to the reached results and their running time.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Artificial neural networks and their application for 3D-data processing

Neural networks represent a powerful means capable of processing various multi-media data. Two applications of artificial neural networks to 3D surface models are examined in this thesis - detection of significant features in 3D data and model classification. The theoretical review of existing self-organizing neural networks is presented and followed by description of feed-forward neural networks and convolutional neural networks (CNN). A novel modification of existing model - N-dimensional convolutional neural networks (ND- CNN) - is introduced. The proposed ND-CNN model is enhanced by an existing technique for enforced knowledge representation. The developed theoretical methods are assessed on supporting experiments with scanned 3D face models. The first experiment focuses on automatic detection of significant facial features while the second experiment performs classification of the models by their gender using the CNN and ND-CNN