|
|
Rain Prediction Using Meteo-Radar
Putna, Lukáš ; Grézl, František (referee) ; Szőke, Igor (advisor)
This work deals with the concept and implementation of short term rain prediction system using meteo-radar. Some basic methods are mentioned and then artificial neural networks are discussed and used for solution. It is proposed how the prediction system using neural networks works. The process of preparing radar data, training neural network with the data and a few scoring methods are discussed. There are shown some experimental results and several improvements are devised at the end of this paper.
|
|
|
Texture-Based Object Recognition
Hutárek, Jiří ; Švub, Miroslav (referee) ; Španěl, Michal (advisor)
Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is implemented. Comprehensive texture database is employed to test its performance under different conditions. In the end, the system is applied to solve a real-world problem - the segmentation of aerial photos.
|
|
|
Stock Prediction Using Artificial Neural Networks
Putna, Lukáš ; Grézl, František (referee) ; Szőke, Igor (advisor)
This work deals with the usage of neural network for the purpose of stock market prediction. A basic stock market theory and trading approaches are mentioned at the beginning of this work. Then neural networks and their application are discussed with their deeper description. Similar approaches are referred and finally two new prediction systems are designed. These systems are utilized by proposed trading model and tested on selected data. The results are compared to human and random trading models and new development steps are devised at the end of this work.
|
|
|
Deep Neural Networks
Habrnál, Matěj ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermore, evaluation and analysis of the results and lessons learned from the experimentation with classical and Deep Neural Networks are integrated in the thesis.
|
|
|
Algorithmic Trading
Ďuriač, Peter ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
The Bachelor thesis focuses on the utilization of neural networks in prediction of stock market price development and then testing by means of chosen platform on the real demo account. In this thesis is described the basic theory of stock market trading as well as theory of neural network functioning. The neural network is created and to this network are designed two trading models. These models are simulated on the chosen data and results are compared with trading model of a human. Consequently, created trading systems are tested in the real trading by means of chosen trading platform. Results of simulation are compared with reached real results. Another orientation of system development is designed.
|
|
|
Proposal of prediction model sales of selected food commodities
Řešetková, Dagmar ; Dostál, Petr (referee) ; Krčmarský, Miroslav (referee) ; Zelinka, Ivan (referee) ; Rais, Karel (advisor)
The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.
|
|
|
Methods and Tools for Image and Video Quality Assessment
Slanina, Martin ; Říčný, Václav (advisor)
Disertační práce se zabývá metodami a prostředky pro hodnocení kvality obrazu ve videosekvencích, což je velmi aktuální téma, zažívající velký rozmach zejména v souvislosti s digitálním zpracováním videosignálů. Přestože již existuje relativně velké množství metod a metrik pro objektivní, tedy automatizované měření kvality videosekvencí, jsou tyto metody zpravidla založeny na porovnání zpracované (poškozené, například komprimací) a originální videosekvence. Metod pro hodnocení kvality videosekvení bez reference, tedy pouze na základě analýzy zpracovaného materiálu, je velmi málo. Navíc se takové metody převážně zaměřují na analýzu hodnot signálu (typicky jasu) v jednotlivých obrazových bodech dekódovaného signálu, což je jen těžko aplikovatelné pro moderní komprimační algoritmy jako je H.264/AVC, který používá sofistikovené techniky pro odstranění komprimačních artefaktů. V práci je nejprve podán stučný přehled dostupných metod pro objektivní hodnocení komprimovaných videosekvencí se zdůrazněním rozdílného principu metod využívajících referenční materiál a metod pracujících bez reference. Na základě analýzy možných přístupů pro hodnocení video sekvencí komprimovaných moderními komprimačními algoritmy je v dalším textu práce popsán návrh nové metody určené pro hodnocení kvality obrazu ve videosekvencích komprimovaných s využitím algoritmu H.264/AVC. Nová metoda je založena na sledování hodnot parametrů, které jsou obsaženy v transportním toku komprimovaného videa, a přímo souvisí s procesem kódování. Nejprve je provedena úvaha nad vlivem některých takových parametrů na kvalitu výsledného videa. Následně je navržen algoritmus, který s využitím umělé neuronové sítě určuje špičkový poměr signálu a šumu (peak signal-to-noise ratio -- PSNR) v komprimované videosekvenci -- plně referenční metrika je tedy nahrazována metrikou bez reference. Je ověřeno několik konfigurací umělých neuronových sítí od těch nejjednodušších až po třívrstvé dopředné sítě. Pro učení sítí a následnou analýzu jejich výkonnosti a věrnosti určení PSNR jsou vytvořeny dva soubory nekomprimovaných videosekvencí, které jsou následně komprimovány algoritmem H.264/AVC s proměnným nastavením kodéru. V závěrečné části práce je proveden rozbor chování nově navrženého algoritmu v případě, že se změní vlastnosti zpracovávaného videa (rozlišení, střih), případně kodéru (formát skupiny současně kódovaných snímků). Chování algoritmu je analyzováno až do plného vysokého rozlišení zdrojového signálu (full HD -1920 x 1080 obrazových bodů).
|
|
|
Water cooling intensity prediction for given thickness of oxide layer
Haluza, Vít ; Hrabovský, Jozef (referee) ; Pohanka, Michal (advisor)
This diploma thesis is dealing with the impact of oxide scales on heat conduction. One of the main tools that were used are numerical simulations. Heat conduction is modelled by solving partial differential equations. Regression models and artificial neural networks are used for the prediction of the influence of oxides on cooling intensity. Determination of the conditions when the cooling was intensified and comparison of individual methods of prediction are the main results of the thesis.
|
|
|
Network element project by means of neural network
Pokorný, Petr ; Krček, Petr (referee) ; Šťastný, Jiří (advisor)
The diploma thesis deal with a priority network switch whose model was made in programming environment Matlab - Simulink. Problem of optimal switching is solved by Hopfield’s artificial neural network. Produce of the diploma thesis is a model of packet switch and time-severity comparison of optimalization problem solved with or without artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.
|
|
| |
guest ::
