Control theory of complex systems by utilization of artificial intelligent algorithms is relatively new science field and it can be used in many areas of technical practise. Best known algorithms to solved similar tasks are genetic algorithm, differential evolution, HC12 Nelder-Mead method, fuzzy logic and grammatical evolution. Complex solution is presented at selected examples from mathematical nonlinear systems to examples of anthems design and stabilization of deterministic chaos. The goal of this thesis is present examples of implementation and utilization of artificial algorithms by multi-objective optimization. To achieve optimal results is used designed software solution by multi-platform application, which used Matlab and Java interfaces. The software solution integrate every algorithms of this thesis to complex solution and it extends possible application of those approaches to real systems and practical world.

This master´s thesis deals with the reseach of technologies using deep learning method, being able to use when processing image data. Specific focus of the work is to evaluate the suitability and effectiveness of deep learning when comparing two image input data. The first – theoretical – part consists of the introduction to neural networks and deep learning. Also, it contains a description of available methods, their benefits and principles, used for processing image data. The second - practical - part of the thesis contains a proposal a appropriate model of Siamese networks to solve the problem of comparing two input image data and evaluating their similarity. The output of this work is an evaluation of several possible model configurations and highlighting the best-performing model parameters.

The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.

The goal of this master thesis is creation of an online system serving as a performing application for presentation results of visual geo-localization in nature and mountain environment. The system offers the users to choose one of the pre-defined photographs or~to~upload one's own photography while choosing a file or inserting an URL address. The~system will localizate a camera of a given image based on a visual geo-localization. The~geo-localization uses the mountain horizon as a key characteristic when searching for similar horizons. The~curve line of the horizon is extracted by a fully automatic algorithm based on supervised learning and dynamic programming. Visual geo-localization running on the server which using new inversed index with cache politic. This allows further scaling of the system. The server processing detected horizon curve and respond with set of the best candidates on results. Results are visualised to the user in form of classic map, detailed sattelite view and rendering of found panorama.

V dnešním světě je neustále se zvyšující poptávka po spolehlivých automatizovaných mechanismech pro detekci a lokalizaci osob pro různé účely -- od analýzy pohybu návštěvníků v muzeích přes ovládání chytrých domovů až po hlídání nebezpečných oblastí, jimiž jsou například nástupiště vlakových stanic. Představujeme metodu detekce a lokalizace osob s pomocí nízkonákladových termálních kamer FLIR Lepton 3.5 a malých počítačů Raspberry Pi 3B+. Tento projekt, navazující na předchozí bakalářský projekt "Detekce lidí v místnosti za použití nízkonákladové termální kamery", nově podporuje modelování komplexních scén s polygonálními okraji a více termálními kamerami. V této práci představujeme vylepšenou knihovnu řízení a snímání pro kameru Lepton 3.5, novou techniku detekce lidí používající nejmodernější YOLO (You Only Look Once) detektor objektů v reálném čase, založený na hlubokých neuronových sítích, dále novou automaticky konfigurovatelnou termální jednotku, chráněnou schránkou z 3D tiskárny pro bezpečnou manipulaci, a v neposlední řadě také podrobný návod instalace detekčního systému do nového prostředí a další podpůrné nástroje a vylepšení. Výsledky nového systému demonstrujeme příkladem analýzy pohybu osob v Národním muzeu v Praze.

This thesis deals with CT data segmentation using convolutional neural nets and describes the problem of training with limited training sets. User interaction is suggested as means of improving segmentation quality for the models trained on small training sets and the possibility of using transfer learning is also considered. All of the chosen methods help improve the segmentation quality in comparison with the baseline method, which is the use of automatic data specific segmentation model. The segmentation has improved by tens of percents in Dice score when trained with very small datasets. These methods can be used, for example, to simplify the creation of a new segmentation dataset.

The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.

The article describes how to produce energy necessary for sensor supply. These generators are used as a local source operating on vibratory principle. Mechanical vibrations that occur in moving machines, in nature etc. are used in order to gain required energy. So are kinesis principles and analysis employed in some of the avaible generators. It also contains patent and literary background research. Work is oriented to solving mechatronic perimeter, in the concrete micro - generator. Mechatronic perimeter piles from seat power control parts and mechanical parts, where common solving equation system leads to correct solving. Mentioned analyses be of consequence for usage optimization methods artificial intelligence. Optimization method are used on optimum solving proposal micro - generator. To inquest dynamism system was used program Simulink (part of MATLAB), generator is buckthorn for f = 17 [Hz] acceleration yam = 0,5g [ms-2]. As a algorithm is used SOMA – All to one.

The thesis deals with design, implementation and optimization of a model based on artificial intelligence and neural networks, which is able to predict future time series prices on a stock market. Main goal is to create an object oriented application for successful future trend prediction of financial derivatives with the use of cooperating methods such as Hurst exponent evaluation and automated market simulation.

The dissertation thesis deal with the design and the optimization of the permanent magnet synchronous machine (SMPM) based on the artificial intelligence. The main target is to apply potential optimization methods on the design procedure of the machine and evaluate the effectiveness of optimization and the optimization usefulness. In general, the optimization of the material properties (NdFeB or SmCo), the efficiency maximization with given nominal input parameters, the cogging torque elimination are proposed. Moreover, the magnet shape optimization, shape of the air gap and the shape of slots were also performed. The well known Genetic algorithm and Self-Organizing migrating algorithm produced in Czech were presented and applied on the particular optimization issues. The basic principles (iterations) and definitions (penalty function and cost function) of proposed algorithms are demonstrated on the examples. The results of the vibration generator optimization (VG) with given power 7mW (0.1g acceleration) and the results of the SMPM 1,1kW (6 krpm) optimization are practically evaluated in the collaboration with industry. Proposed methods are useful for the optimization of PM machines and they are further theoretically applied on the low speed machine (10 krpm) optimization and high speed machine (120 krpm) optimization.