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Optimization of Basic Magnetic Field Homogeneity in MR Tomography
Hadinec, Michal ; Brančík, Lubomír (referee) ; Frollo, Ivan (referee) ; Bartušek, Karel (advisor)
This thesis is concerned with problems of measuring and mapping of magnetic field in MR tomograph, for purpose of magnetic flux density homogeneity optimization. Attention is paid to mapping techniques on rotary symmetric volume and to ways of magnetic fields optimization with utilization of passive and active correction systems. Theoretical analysis of magnetic field decomposition with utilization of spherical harmonics and numerical decomposition is made. Mapping and approximation techniques of basic magnetic field are verified by experiments in the laboratory at the Institute of Scientific Instruments AS CR in Brno.
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Image composition
Smital, Petr ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
This thesis deals with methods of improving the quality of image output of an electron microscope by composing consecutive frames. The thesis contains a brief description of an electron microscope and the problems that deteriorate its image quality, and three basic mathematical algorithms that can be used to remove them. These are the simple averaging method, the method of least squares and the method of vector space. For all of the methods, the thesis describes ways of their implementation, the possibilities of their use and their mutual comparison. This thesis also deals with the problem of so-called superresolution, i.e. exceeding the physical resolution of a sensor using the methods mentioned above, and the problem of electron beam centring.
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Electrical motors parameters identification using subspace based methods
Jenča, Pavol ; Veselý, Ivo (referee) ; Blaha, Petr (advisor)
The electrical motors parameters identification is solved in this master’s thesis using subspace based methods. Electrical motors are simulated in Matlab/Simulink interactive environment, specifically permanent magnet DC motor and permanent magnet synchronous motor. Identification is developed in Matlab interactive environment. Different types of subspace algorithms are used for the estimation of parameters. Results of subspace parameters estimation are compared with least squares parameters estimation. The thesis describes subspace method, types of subspace algorithms, used electrical motors, nonlinear approach of identification and comparation of parameters identification.
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Permanent Magnet Synchronous Motor Parameters Identification
Dušek, Martin ; Veselý, Ivo (referee) ; Blaha, Petr (advisor)
This work deals with the on-line identification of permanent magnet synchronous motor parameters. There is discussed the use of four different identification algorithms based on the least squares method and MRAS. The functionality of the algorithms is verified in Matlab - Simulink environment. Simulation results are compared in terms of rate and accuracy of identification, resistance to noise and other factors.
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Industrial PID controller with autotuning and visualisation
Prudký, Miroslav ; Chomát, Luděk (referee) ; Pivoňka, Petr (advisor)
The objective of the thesis is to create software, which implements industrial PID controller enabling smooth switch-over and automatic tuning of parameters. The next objective is to create visualization for this controller. Whole controller have to be implemented into PLC Power Panel (B&R company). At the beginning of the thesis there is theoretical description of all implemented algorithms (discrete control algorithms derived from PID, smooth switch-over, antiwindup, identification algorithms). In the following there is designed program structure, which is state machine-shaped. All algorithms are implemented in ANSI C at first as s-function for Matlab/Simulink program, which enables to simulate and verify the controller. Implementation of controller into PLC Power Panel through the use of Automation Studio program from B&R company is described in the next part. Visualization is created in the same program. Simulations and verifications on mathematical and physical model demonstrates functionality of implemented algorithms, but also points out some problems associated with the use of identification algorithms in real world (noise, quantization in A/D and D/A converter).
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Adaptive controllers with principles of artificial intelligence
Pitra, Michal ; Malounek, Petr (referee) ; Pivoňka, Petr (advisor)
The diploma thesis is focused on an adaptive control, especially in a self-tuning controller area. The thesis is divided into two main parts. The first part deals with identification methods of the adaptive control. The recursive least squares algorithm and the neural network method are the most popular identification methods. These methods are contrasted and the identification quality evaluation is done. The second part compares various types of the adaptive controllers with a non-adjustable PSD controller. The adaptive controllers are investigated from the various identification method point of view. The user graphic interface was realized for the identification and regulatory part. The time behavior of system responses after incoming disturbance and dynamic process changes during simulation systems control is compared with a physical model connected to B&R PLC. The comparison is done with the application of Matlab/Simulink program.
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Least-Squares Method in System Identification
Matějka, Jiří ; Bělohradský, Petr (referee) ; Máša, Vítězslav (advisor)
This bachelor thesis deals with the description of the least squares method and its application in system identification of dynamic processes. It contains mathematical derivation of least squares method and basic principles of system identification. It describes parametrization of an mathematical model of experimental unit for biomass combustion.
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Least squares method using genetic algorithm
Holec, Matúš ; Tichý, Vladimír (advisor) ; Šalamon, Tomáš (referee)
This thesis describes the design and implementation of genetic algorithm for approximation of non-linear mathematical functions using the least squares method. One objective of this work is to theoretically describe the basics of genetic algorithms. The second objective is to create a program that would be potentially used to approximate empirically measured data by the scientific institutions. Besides the theoretical description of the given subject, the text part of the work mainly deals with the design of the genetic algorithm and the whole application solving the given problem. Specific part of the assignment is that the developed application has to support approximation of points by various mathematical non-linear functions in several different intervals, and then it has to insure, that resulting functions are continuous throughout all the intervals. Described functionality is not offered by any available software.
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