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An efficiency comparison of simulation methods for artificial neural network training and inverse analysis
Nezval, Michal ; Novák, Drahomír (referee) ; Lehký, David (advisor)
The thesis deals with inverse analysis which is based on combination of artificial neural network and stochastic methods. The goal is to compare an efficiency of new simulation method Hierarchical Subset Latin Hypercube Sampling to classical Monte Carlo method and standard Latin Hypercube Sampling method used for neural network training. The efficiency is compared for a different neural network structures. The inverse analysis is then applied for engineering tasks – identification of limit state fiction parameters related to pitched-roof frame and material parameters of concrete specimen subjected to three-point bending. Finally an efficiency of Hierarchical Subset Latin Hypercube method comparing to Monte Carlo and Latin Hypercube Sampling methods is discussed.
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Superresulution of photography using deep neural network
Holub, Jiří ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.
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Analysis of Human Signature Based on Artificial Neural Network
Ševčík, Pavel ; Horák, Karel (referee) ; Pohl, Jan (advisor)
This bachelor thesis deals with methods of human signature and its analysis in practical service of artificial neural network. Actual processing and analysis of human signature consist in few steps. First of all, the signature pattern is digitized and processed with the assistance of preprocessing and segmentation methods. Afterwards, the object of human signature pattern is described with the assistance of centric geometric moments and moments invariant characteristics. Finally, the pattern is classified by multilayer perceptron, whose outputs determine the person, to that signature belongs to.
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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.
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Main Text Extraction from Web Documents
Mrózek, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis deals with the main text extraction from the web documents in HTML format. It describes some methods that are already used and their separation. The goal of the practical part is to propose an algorithm for main text detection in HTML pages using primarily text features in combination with position features. Block classification is solved by multilayer perceptron. It also describes implementation of the proposed algorithm, the testing procedure and presentation of the obtained results.
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Web application for Cybersecurity Job Ads Analysis
Turek, Adam ; Sikora, Marek (referee) ; Ricci, Sara (advisor)
Cílem bakalářské práce je vytvoření interaktivní celosvětové mapy zobrazující databázi pracovních inzerátů ve webové aplikaci a provedení filtrování podle různých parametrů, kde je následně provedena analýza strojového učení. Také mapa zobrazuje počet inzerátů na pracovní pozice podle příslušných států. Webová aplikace je vytvořena pomoci JavaScriptové knihovny ReactJS spojené s LeafletJS, které zajišťují hlavní funkcionalitu. Část se strojovým učením a změna skriptů je realizována pomocí programovacího jazyku Python. Práce popisuje teoretickou část a implementaci jednotlivých funkcí mapy a dále se zabývá popisem a úspěsnou úpravou skriptů pro účely provedení strojového učení.
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Classification of heart beats using artificial neuronal networks
Doležalová, Radka ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This work deals with using of artificial neural networks (ANN) for ECG classification. The issue of ECG and ANN technique are described theoretically at first, the next section describes use of Matlab to design ANN and graphical user interface. ECG data (namely QRST segments from the orthogonal X- lead from seven phases of the experiment) obtained from experiments in isolated hearts of rabbits are used for learning and testing of the classifier. The result of this work is the software with GUI that allows user to set various parameters and structure of ANN. After learning phase, ANN realized in this work able to classify cardiac cycles according to their morphology into seven groups.
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Acoustic detection of starlings through artificial intelligence
Zezula, Benjamin ; Vlachová Hutová, Eliška (referee) ; Marcoň, Petr (advisor)
To protect the vineyards from starlings is very costly and ineffective with the available resources over a period of time. In my project, I have designed a detection module that relays information about the flock location in the vineyard to the control module and thus produces scaring. I have designed two kinds of detection modules, which are compared. The first variant consists of a single directional microphone that rotates and sequentially senses the entire area of the vineyard. The second variant consists of four microphones, each one is directed to the cardinal point and based on the intensity of sound, the module will provide the information about the direction where the flock is located. Both detection modules process the signal in a Raspberry Pi 4 single board computer using an artificial neural network algorithm powered by MobileNetV2 architecture.
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Melody Harmonization
Trnkóci, Andrej ; Jaroš, Jiří (referee) ; Fapšo, Michal (advisor)
Computer scientists have long been considering music as a particularly interesting art Indeed, the history of computer music is almost as long as the history of computer science. Programs to compose music, or to make music" at various levels of the composition process have been designed since the 50s. This bachelor's thesis surveys the main approaches in the field of automatic harmonization, i.e. the problem of producing musical arrangements (scores) from given melodies, and focuses on the most widely used techniques to do so. The main goal of this paper is the issue of design and implementation of a software system for an automatic music harmonization which should learn the rules of harmony from the database of midi file. In the paper. In this thesis I describe existing systems for harmonization and furthermore I focus mainly on principles of machine learning - theory and application of Artificial Neural Networks and their use for harmonization.
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Control of storage function of the reservoir
Hon, Matěj ; Matoušek, Petr (referee) ; Kozel, Tomáš (advisor)
Dispatcher graphs state the amount of discharged water according to the current status of a water tank. The thesis describes the technique of creating a zonal dispatcher graph and its simulation on a large open water reservoir Vranov. Using predictions, the model was tested to know how it works with the ability to determine incoming tributaries. The dispatcher graph method is commonly rated among deterministic control methods. These methods are advantageous in one specific output and that is a control variable. Other possible ways are stochastic methods that work with probability and give us the control over the choice of a procedure. Both types of methods are further described in the work. For the suitable prediction of flows, the use of a neural network operating on the principle of reverse propagation was chosen.
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