This thesis is concerned with the removal of artefacts typical for JPEG im- age compression. First, we describe the mathematical formulation of the JPEG format and the problem of artefact removal. We then formulate the problem as an optimization problem, where the minimized functional is obtained via Bayes' theorem and complex wavelets. We describe proximal operators and algorithms and apply them to the minimization of the given functional. The final algorithm is implemented in MATLAB and tested on several test problems. 1

The impact of climate changes on various sectors of human activity and preparation of mitigation and adaptation measures are becoming an important subject of global policy, thus also the policy of the Czech Republic. According to the research results, the impact of climate changes on minimum discharges is very significant and indicates a threat to storage function of water sources, without adequate accumulation for period of drought. One of the ways of preventing or completely avoiding the appearance of these problems is the construction of new reservoirs or the re-evaluation of the size of the storage and protective capacity of existing reservoirs. In relation to climate changes and their impact on the water yield there is a growing need for the participation of operative control progressive methods of reservoir storage function today. The dissertation thesis is aimed at storage function of water management system. Aim of the dissertation work is to create program with graphical user interface, which allows solving the storage function of water management system. Program allows automatically set up optimization and simulation model of any water supply system and solve of optimal control and optimal development of the water supply system. The program was verified on the selected water supply subsystem.

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.

In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two chapters we define prediction task and link the classical econometric analysis to neural networks. We also present optimization methods which will be used in the tests, conjugate gradient, Levenberg-Marquardt, and evolutionary search method. Further on, we present statistical methods for comparing the predictive accuracy of the non-nested models, as well as economic significance measures. In the empirical tests we first show the power of neural networks on Mackey-Glass chaotic time series followed by real-world data of the daily and weekly returns of mentioned stock exchanges for the 2000:2006 period. We find neural networks to have significantly lower prediction error than classical models for daily DAX series, weekly PX50 and BUX series. The lags of time-series were used, and also cross-country predictability has been tested, but the results were not significantly different. We also achieved economic significance of predictions with both daily and weekly PX-50, BUX and DAX with 60% accuracy of prediction. Finally we use neural network to learn Black-Scholes model and compared the pricing errors of...

This master thesis deals with optimization of the shape of the stator and rotor slots of induction motor with focusing on increasing efficiency. The theoretical part introduces the principles of optimization algorithms. Another part is devoted to design the shape of slots and its influence on the shape of slots on machine. On the models of motor was conducted the optimized shape of slots by using a genetic algorithm. Firstly, it was performed by using the analytical calculation in the RMxprt program, then by using the finite element method on two different models, whose difference simulates the influence of the production technology on the efficiency of the motor. Laboratory measurement was made on a real machine as well. The results of the measurement, calculations and optimizations are compared in the work.

First part of the thesis is about literature research of optimization algorithms. Three of the algorithms were implemented and tested, concretely the ant colony algorithm, tabu search and simulated annealing. All three algorithms were implemented to solve the traveling salesman problem. In second part of the thesis the algorithms were tested and compared. In last part the influence of the ant colony parameters was evaluated.

This Master’s thesis deals with optimization of loading patterns of nuclear reactors VVER. In the thesis is described the process of both types of fuel cycle – the closed and the open one. The middle part of fuel cycle containing the optimization process represents the crucial part of the thesis. The thesis is focused on the fuel cycles of the nuclear power plant Dukovany. The problem of the optimization is solved by using different programs. In the final part of the thesis, the program Athena for the loading patterns optimation is explained by a practical calculation for different versions of Moby-Dick macrocode and the calculated values for the optimization of the third unit of the nuclear power plant Dukovany are compared.

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.

The Master's thesis focuses on loading patterns of nuclear reactors VVER. It describes the whole process of fuel cycle, since production to storage or reprocessing. The author puts emphasise on the middle part of fuel cycle in Czech nuclear power plants - Dukovany and Temelín and he also explains which fuels are used in both power plants now and which were used in history. The thesis also contains a basic overview of approaches to loading patterns optimisation in foreign countries. The final part of the thesis discusses practical calculation of loading patterns in the Block III of Nuclear power plant Dukovany, by using optimisation programme Athena and Moby-Dick macrocode.

This thesis deals with analysis of genetic algorithms. It is focused on various approaches to creation of new populations. A comparison between basic principles of operation of genetic algorithms and processes occurring in living organisms is drawn here. Some methods of application of particular steps of genetic algorithms are introduced and a suitability of the methods to certain types of problems is considered. The main goal in the thesis is to apply genetic algorithms in solving three types of optimization problems, namely the solution of functions with a single major extreme, functions with flat (slight) extreme and also functions with many local extremes.