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Long Term Discharge Prediction in River Hydrometric Profile
Šelepa, Milan ; Menšík, Pavel (referee) ; Marton, Daniel (advisor)
The diploma thesis is focused on the long term prediction of mean monthly flows in hydrometric profile for purposes of reservoir control optimization and optimization of reservoir systems. Discharges were predicted using by artificial neural network method. Predicted flows were statistically evaluated by relevant coefficients and then compared with the measured flows for given river hydrometric profiles.
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Indoor Robot - Control Neural Network
Křepelka, Pavel ; Kopečný, Lukáš (referee) ; Žalud, Luděk (advisor)
In this document, I describe possibilities of mobile robot navigation. This problems are solving many different ways, but there isn’t satisfactorily result to this day. You find there describe of deterministic algorithms, this algorithms can be used for simply actions like obstacle avoiding or travel in corridor. For global navigation this algorithms fails. In next part of document is theory of artificial neural nets (perceptron, multi layer neural nets, self organization map) and using them in mobile robots. Own navigation algorithms was tested on constructed mobile robot or simulated in SW described in chapter 6. Design own control algorithms is based on neural net (Kohonen net). Designed algorithms can be used for one-point navigation or complex global navigation. In document, there is comparing of various ways to navigation, their advantages and disadvantages. Goal of this document is find effective algorithm for navigation and artificial intelligence appears to be the right solution.
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Image Compression Based on Artificial Neural Network
Vondráček, Jiří ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
The thesis is focused on the image compression based on artificial neural network with practical implementation. The objective of this thesis is to explore possibilities of an image compression by artificial neural network and analyze results. In the theoretical part of the work, the fundamentals of artificial neural network are described and basic image compression techniques are explained. In the practical part there is a brief description of the compression program, the comparison of different settings and result evaluation.
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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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Artificial neural network for modeling electromagnetic fields in a car
Kostka, Filip ; Škvor, Zbyněk (referee) ; Raida, Zbyněk (advisor)
The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelve monoconic antennas operating in the frequency range from 2 to 6 GHz. Antennas were located inside a mathematical model of Octavia II. Using CST simulations, electromagnetic fields in a car were obtained. Trained networks are described by regressive characteristics andthe mean square error of training. Algorithms improving generalization are applied on the created and trained networks. The performance of individual networks is mutually compared.
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Use of higher-order cumulants for heart beat classification
Dvořáček, Jiří ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This master‘s thesis deals with the use of higher order cumulants for classification of cardiac cycles. Second-, third-, and fourth-order cumulants were calculated from ECG recorded in isolated rabbit hearts during experiments with repeated ischemia. Cumulants properties useful for the subsequent classification were verified on ECG segments from control and ischemic group. The results were statistically analyzed. Cumulants are then used as feature vectors for classification of ECG segments by means of artificial neural network.
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Heart beat classification
Potočňák, Tomáš ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (P-QRS-T cycles) selected from animals orthogonal ECGs were preprocessed and used as the input signals. Spectral features vectors (values of cross spectral coherency), principal component and HRV parameters were derived from the beats. The beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. from 87,2 to 100%. Presented results can be suitable in future studies aimed at automatic classification of ECG.
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