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Material printing of ozone dosimeters
Petříčková, Zuzana ; Čeppan,, Michal (referee) ; Veselý, Michal (advisor)
This study presents various methods of measuring ozone and consolidates current state of knowledge in the field of disposable printed ozone dosimeters. The paper also identifies prospective dyes for preparation of ozone dosimeters available on the market. Information gathered from literature review was used to prepare dosimeters based on two different dyes. These were calibrated for screen printing technology and their sensitivity to ozone was optimized to achieve visual detectability. Prepared samples were observed and studied while being exposed to ozone. Then, long term mechanical and chemical properties of these were tested in order to identify appropriate conditions for storage.
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Printing inks optimization for ozone dosimeter
Petříčková, Zuzana ; Krystyník,, Pavel (referee) ; Veselý, Michal (advisor)
In the study various methods to measure concentration of ozone were investigated. Emphasis was placed on opto-chemical sensors, which change their colour when exposed to augmented dose of the substance. Numerous solutions have been prepared by changing ratios of substances and were used to make ozone dosimeter. The goal was to produce highly sensitive composition which would change colour noticeably when exposed to ozone, with good mechanical properties when dried up. When the solution with required properties had been discovered, it is suitably for silk-screen printing was tested.
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Room Arrangement Generation
Dvořák, Ondřej ; Jelínková, Eva (advisor) ; Petříčková, Zuzana (referee)
The aim of this thesis is to create a program which generates layouts of furniture in a room on the basis of specific constraints. This program is called Spaceout and it is based on genetic algorithms. The thesis also gives a basic overview of existing programs for creating projects of interiors and exteriors. The thesis contains a programmer and user documentation of Spaceout.
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Modely výpočetní inteligence pro hydrologické predikce
Paščenko, Petr ; Neruda, Roman (advisor) ; Petříčková, Zuzana (referee)
The thesis deals with the application of computational artificial intelligence models on hydrological predictions. The short term rainfall-runoff prediction problem is studied on the real data of physical time seriesmeasured in the watershed of river Plučnice. A brief statistical study including correlation and regression analyses is performed. The high level of variance and noise is concluded. The evolution of the proper input filter providing an input set for the neural network is performed. In the main part of the thesis several neural network models based on multilayer perceptron, RBF units, and neuroevoution are constructed together with two neural ensembles inspired by the bagging method. The models are tested on the three subsequent years summer data. The greater generalization ability of multilayer perceptron architectures is concluded. The resulting multilayer perceptron models are able to reduce the mean squared error of the prediction by 15% compared to the prediction by the previous value.
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Technical analysis of stock trends using artificial neural networks
John, Pavel ; Petříčková, Zuzana (advisor) ; Pilát, Martin (referee)
Although the discipline has not received the same level of acceptance in the past, the technical analysis has been part of financial practice for centuries. One of the big issues was the absence of widely respected fully rational background that is necessary for the modern science. The presence of geometrical shapes recognized by a human eye in historical data charts remained as one of the most important tools till the last decades. Nowadays, it is possible to find commercial trading software which employs neural networks. However, a freely accessible tool is difficult to obtain. The aim of this work was to investigate the usability of applications of neural networks on the technical analysis and to develop a software tool that would implement the knowledge acquired. An application was created and a new promising trading strategy proposed along with experimental data. The advantages of the program presented include the ease of extensibility and a high variability in trading strategies setting.
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Artificial Neural Networks and Their Usage For Knowledge Extraction
Petříčková, Zuzana ; Mrázová, Iveta (advisor) ; Procházka, Aleš (referee) ; Andrejková, Gabriela (referee)
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petříčková Department: Department of Theoretical Computer Science and Mathema- tical Logic Supervisor: doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Computer Science and Mathematical Logic Abstract: The model of multi/layered feed/forward neural networks is well known for its ability to generalize well and to find complex non/linear dependencies in the data. On the other hand, it tends to create complex internal structures, especially for large data sets. Efficient solutions to demanding tasks currently dealt with require fast training, adequate generalization and a transparent and simple network structure. In this thesis, we propose a general framework for training of BP/networks. It is based on the fast and robust scaled conjugate gradient technique. This classical training algorithm is enhanced with analytical or approximative sensitivity inhibition during training and enforcement of a transparent in- ternal knowledge representation. Redundant hidden and input neurons are pruned based on internal representation and sensitivity analysis. The performance of the developed framework has been tested on various types of data with promising results. The framework provides a fast training algorithm,...
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Improving and extending the multiple sequence alignment suite PRALINE
Hudeček, Jan ; Mráz, František (advisor) ; Petříčková, Zuzana (referee)
The aim of this work is to study potential improvements in the core routines of multiple sequence alignment suite PRALINE. A general overview of multiple sequence alignment methods used with emphasis on representation of the alignment core is given. A new option for aligning sequence profiles was implemented and its usefulness assessed. This option allows a user to input a profile which is used in an advanced phase of the progressive protocol as if it was a result of the previous steps. Two new protocols using profile Hidden Markov models (HMM) and their alignment were implemented and tested. The HMMGUIDE protocol creates for each sequence a preprofile consisting of segments of other sequences with high local similarity. HMM is generated from each preprofile by HMMER, and alignment of every pair is scored by PRC. The protocol then progressively aligns the sequence whose HMMs achieved the best score. The PRCALIGN protocol works similarly but aligns the sequences according to the best alignment of the HMMs. While not all test alignments were finished successfully for both protocols, the results constitute a statistically significant improvement over the original PRALINE protocol.
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Multilayer hierarchical models
Béger, Michal ; Štanclová, Jana (advisor) ; Petříčková, Zuzana (referee)
This diploma thesis deals with hierarchical associative memories (HAM), which have been experimentally analysed only in the case of two layer hierarchy so far. The aim of this thesis is to study existing hierarchical models and evaluate experimentally their performance for more than two layer hierarchy. We show, that existing hierarchical model HAM is not suitable for three or more layer hierarchy. For that reason, we propose a new version of hierarchical model (called HAM-N), which enables utilization of any number of layers. The new model HAM-N uses the structure of the HAM model. However, due to modied learning and recall process, the HAM-N model eliminates the above-mentioned drawbacks of the HAM model. Finally, the HAM-N model is experimentally studied with respect to processing of large amounts of correlated patterns. Thesis also includes analysis of experiment results.
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Gradient learning for networks of smoothly pulse neurons
Hošek, Lukáš ; Šíma, Jiří (advisor) ; Petříčková, Zuzana (referee)
Networks of spiking neurons present a biologically more plausible alternative to perceptron networks, having great potential for processing time series. However, as of now, no practically usable learning algorithm has been known. SpikeProp, based on a gradient descent method, and its modifications have a fundamental problem with dis-continuity of spike creation and deletion. A new nontrivial gradient learning algorithm for a model of smoothly spiking neurons is proposed as a possible way to solve this problem. The goal of this work is to implement and test this model and eventually propose further improvements.
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Airport - Time and resource constrained project sheduling
Vandas, Marek ; Petříčková, Zuzana (advisor) ; Pangrác, Ondřej (referee)
This thesis identifies constraints for safe ground airport operations. These operations consist of runway assignment, taxi operations planning and gate scheduling. The aim of this thesis is to show how this problem can be formulated as constraint satisfaction problem and then solved as a scheduling problem. Based on this model, an application that ilustrates these concepts is designed and implemented. This application enables a visualisation of results. An extendable constraint solver was implemented for the purpose of this application. This solver can be used to solve problems from other domains as well and also enables easy change of search strategy.
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