Disertační práce / Dissertations FEI (Ph.D.)
Permanentní URI k tomuto záznamuhttps://hdl.handle.net/10195/38790
Procházet
3 výsledky
Search Results
Disertační práce Otevřený přístup Similarity Space and Its Applications(Univerzita Pardubice, 2024) Rozinek, Ondřej; Mareš, Jan; Horálek, Josef; Kukal, JaromírMathematical spaces have been studied for centuries and belong to the basic mathematical theories, which are used in various real-world applications. In general, a mathematical space is a set of mathematical objects with an associated structure. This structure can be specified by a number of operations on the objects of the set. These operations must satisfy certain axioms of mathematical space. Similarity and dissimilarity functions are widely used in many research areas: in information retrieval, data mining, machine learning, cluster analysis and applications in database search, protein sequence comparison and many more. When a dissimilarity function is used, a distance metric is normally required. On the other hand, although similarity functions are used, there is no formally accepted definition of this concept. In this dissertation is used for the first time the novel term similarity space. A significant contribution of this dissertation is the identification of a class of functions that satisfy the axioms of similarity space, alongside the development of novel mathematical theorems and definitions that extend our understanding of similarity. This includes the exploration of duality between similarity and metric spaces, the introduction of normalization transformations that addresses to solution to open unsolved problem, and the establishment of new descriptions and definitions for convergence, continuity, and other fundamental properties within similarity spaces. A significant section is dedicated to developing a new fixed-point theory in similarity space, establishing solutions for differential equations, and introducing a new convergence criterion for the Newton method. Another theoretical contribution is the novel application of similarity space in linear regression. Within the framework of Natural Language Processing (NLP) and Artificial Intelligence (AI), this dissertation applies theoretical insights to address real-world challenges, particularly in the areas of approximate string matching, complex fuzzy record matching and deduplication. By developing a novel convolution-based string matching model, proposing an advanced mathematical model for fuzzy record similarity, and introducing an optimal Q-gram filter for bipartite matching, this research presents novel solutions that significantly improve upon the state-of-the-art methods in terms of efficiency, accuracy, and applicability. In conclusion, this dissertation not only advances the theoretical understanding of similarity spaces but also demonstrates their vast potential for application in data processing and analysis. By bridging the gap between abstract mathematical theory and practical computational challenges, this work lays the groundwork for future innovations across broad range of fields.Disertační práce Otevřený přístup Dynamic Stochastic Modeling for Optimization of Environmental Measurements(Univerzita Pardubice, 2018) Ezeora, Obiora Sam; Krőmer, Pavel; Pelikán, EmilMinimization of energy consumption of environmental measurement systems is important to ensure their extended operational lifetime and low maintenance cost. This needs to be realized without sacrificing on data quality. One possible way to achieving this is the use of energy-aware sampling techniques such as adaptive and event-triggered sampling. In this work, new methods based on these sampling techniques have been developed. The first method produces stochastic models that accurately predict missed and future data with minimal energy. The method also determines the optimal sampling interval. The second method utilizes new type of event-triggered mechanism that adjusts sampling interval so that it adapts to the changes in measurement data. Algorithms have been developed and all methods demonstrated using field data. Obtained results have been thoroughly analyzed from the perspective of approximation error and energy savings. Models have been validated and favorable results obtained. High R-squared values and low values of mean square normalized error have been obtained. Battery lifetime is extended by more than 87% when sampling interval increases from 15 to 30 seconds. Furthermore, about 45% daily savings of energy consumption of analog-to-digital converter has been achieved in a case study analysis involving the new algorithm, an ADC and field data.Disertační práce Otevřený přístup Trajectory Tracking of Differential Drive Mobile Robot by Model Predictive Control(Univerzita Pardubice, 2017) Kizhakke Illom, Rahul Sharma; Dušek, František; Gazdoš, František; Kukal, JaromírV práci je navrženo dvouúrovňové řízení pohybu mobilního dvoukolového robotu zajišťující sledování známé trajektorie. V obou úrovních je použit prediktivní regulátor s uvažováním omezení. Pro návrh řízení ve vyšší úrovni vycházející z kinematického modelu (závislost polohy a orientace robotu na jeho aktuální tangenciální a úhlové rychlosti) byly vytvořeny dva nelineární modely chyby sledování známé trajektorie. Pro návrh řízení v nižší úrovni a pro možnost simulačního ověření celého řízení robotu byl vytvořen metodou matematicko-fyzikální analýzy lineární dynamický model robotu popisující závislost jeho tangenciální a úhlové rychlosti na napětích elektromotorů pohánějící obě kola. Simulačně byly porovnány průběhy řízení pro různé struktury řízení i regulátory. Pozornost byla věnována zejména vlivu zanedbání dynamiky robotu a přínosu prediktivních regulátorů oproti standardně používaným řešením jak v kinematické tak i dynamické úrovni. Výsledky simulací ukazují, že prediktivní regulátor dynamické části, kromě respektování zadaných omezení, také ovlivňuje kinematickou část a zvyšuje celkovou kvalitu regulace.