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Míry podobnosti pro nominální data v hierarchickém shlukování
Šulc, Zdeněk ; Řezanková, Hana (advisor) ; Šimůnek, Milan (referee) ; Žambochová, Marta (referee)
This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.
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Effect of snowpack on runoff generation during rain on snow event.
Juras, Roman ; Máca, Petr (advisor) ; Ladislav , Ladislav (referee)
During a winter season, when snow covers the watershed, the frequency of rain-on-snow (ROS) events is still raising. ROS can cause severe natural hazards like floods or wet avalanches. Prediction of ROS effects is linked to better understanding of snowpack runoff dynamics and its composition. Deploying rainfall simulation together with hydrological tracers was tested as a convenient tool for this purpose. Overall 18 sprinkling experiments were conducted on snow featuring different initial conditions in mountainous regions over middle and western Europe.
Dye tracer brilliant blue (FCF) was used for flow regime determination, because it enables to visualise preferential paths and layers interface. Snowpack runoff composition was assessed by hydrograph separation method, which provided appropriate results with acceptable uncertainty. It was not possible to use concurrently these two techniques because of technical reasons, however it would extend our gained knowledge. Snowmelt water amount in the snowpack runoff was estimated by energy balance (EB) equation, which is very efficient but quality inputs demanding. This was also the reason, why EB was deployed within only single experiment.
Timing of snowpack runoff onset decrease mainly with the rain intensity. Initial snowpack properties like bulk density or wetness are less important for time of runoff generation compared to the rain intensity. On the other het when same rain intensity was applied, non-ripe snowpack featuring less bulk density created runoff faster than the ripe snowpack featuring higher bulk density. Snowpack runoff magnitude mainly depends on the snowpack initial saturation. Ripe snowpack with higher saturation enabled to generate higher cumulative runoff where contributed by max 50 %. In contrary, rainwater travelled through the non-ripe snowpack relatively fast and contributed runoff by approx. 80 %.
Runoff prediction was tested by deploying Richards equation included in SNOWPACK model. The model was modified using a dual-domain approach to better simulate snowpack runoff under preferential flow conditions. Presented approach demonstrated an improvement in all simulated aspects compared to the more traditional method when only matrix flow is considered.
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Design of Experiment for Non-Stationary Processes of Production
Jadrná, Monika ; Macák, Tomáš (advisor)
The doctoral thesis is concerned with the services sector and the area of mass production. Particularly, the optimization of the product portfolio of the travel agency and the optimization of production rounds of ammunition.
The theoretical part deals with the current overview of discussed topic. Further, the terminology and methods of the decision-making process are defined to support decision making. The theoretical basis of research focused on the choice of appropriate input variables in the area of services, and on the choice of a particular material option in the production area and appropriate equipment for the production. The theoretical part forms the basis for the practical part of the thesis.
For the doctoral thesis was chosen an enterprise operating in the defined sector. Product portfolio for the services sector is optimised using Fuzzy logic and Fuzzy sets so that the enterprise can maintain its competitiveness in todays highly ambitious market. Product portfolio for manufacture is optimised for achieving desired properties of the product.
The main aim of the thesis is to propose a new methodological approach for the management of selected business processes in their nonstationary time course. The aim of the practical implementation is to verify the functionality of the proposed methodological approach, both in the area of services and in the field of mass production.
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Swarm Intelligence
Winklerová, Zdenka ; Šaloun, Petr (referee) ; Škrinárová,, Jarmila (referee) ; Zbořil, František (advisor)
The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.
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Image analysis in tribodiagnostics
Machalík, Stanislav ; Stodola,, Jiří (referee) ; Tillová,, Eva (referee) ; Zemčík, Pavel (advisor)
Image analysis of wear particles is a suitable support tool for detail analysis of engine, gear, hydraulic and industrial oils. It allows to obtain information not only of basic parameters of abrasion particles but also data that would be very difficult to obtain using classical ways of evaluation. Based on the analysis of morphological or image characteristics of particles, the progress of wearing the machine parts out can be followed and, as a result, possible breakdown of the engine can be prevented or the optimum period for changing the oil can be determined. The aim of this paper is to explore the possibilities of using the image analysis combined with the method of analytical ferrography and suggest a tool for automated particle classification. Current methods of wear particle analysis are derived from the evaluation that does not offer an exact idea of processes that take place between the friction surfaces in the engine system. The work is based upon the method of analytical ferrography which allows to evaluate the state of the machine. The benefit of use of classifiers defined in this wirk is the possibility of automated evaluation of analytical ferrography outputs; the use of them eliminates the crucial disadvantage of ferrographical analysis which is its dependence on the subjective evaluation done by the expert who performs the analysis. Classifiers are defined as a result of using the methods of machine learning. Based on an extensive database of particles that was created in the first part of the work, the classifiers were trained as a result, they make the evaluation of ferrographically separated abrasion particles from oils taken from lubricated systems possible. In the next stage, experiments were carried out and optimum classifier settings were determined based on the results of the experiments.
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Sharing Local Information for Faster Scanning-Window Object Detection
Hradiš, Michal ; Kälviäinen, Heikki (referee) ; Matas, Jiří (referee) ; Zemčík, Pavel (advisor)
Cílem této dizertační prace je vylepšit existující detektory objektů pomocí sdílení informace a výpočtů mezi blízkými pozicemi v obraze. Navrhuje dvě metody, které jsou založené na Waldově sekvenčním testu poměrem pravděpodobností a algoritmu WaldBoost. První z nich, Early non-Maxima Suppression , přesunuje rozhodování o potlačení nemaximálních pozic ze závěrečné fáze do fáze vyhodnocování detektoru, čímž zamezuje zbytečným výpočtům detektoru v nemaximálních pozicích. Metoda neighborhood suppression doplňuje existující detektory o schopnost zavrhnout okolní pozice v obraze. Navržené metody je možné aplikovat na širokou škálu detektorů. Vyhodnocení obou metod dokazují jejich výrazně vyšší efektivitu v porovnání s detektory, které vyhodnocují jednotlivé pozice obrazu zvlášť. Dizertace navíc prezentuje výsledky rozsáhlých experimentů, jejichž cílem bylo vyhodnotit vlastnosti běžných obrazových příznaků v několika detekčních úlohách a situacích.
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HUMAN ACTION RECOGNITION IN VIDEO
Řezníček, Ivo ; Baláž, Teodor (referee) ; Sojka, Eduard (referee) ; Zemčík, Pavel (advisor)
Tato disertační práce se zabývá vylepšením systémů pro rozpoznávání činností člověka. Současný stav vědění v této oblasti jest prezentován. Toto zahrnuje způsoby získávání digitálních obrazů a videí společně se způsoby reprezentace těchto entit za použití počítače. Dále jest prezentováno jak jsou použity extraktory příznakových vektorů a extraktory pros- torově-časových příznakových vektorů a způsoby přípravy těchto dat pro další zpracování. Příkladem následného zpracování jsou klasifikační metody. Pro zpracování se obecně obvykle používají části videa s proměnlivou délkou. Hlavní přínos této práce je vyřčená hypotéza o optimální délce analýzy video sekvence, kdy kvalita řešení je porovnatelná s řešením bez restrikce délky videosekvence. Algoritmus pro ověření této hypotézy jest navržen, implementován a otestován. Hypotéza byla experimentálně ověřena za použití tohoto algoritmu. Při hledání optimální délky bylo též dosaženo jistého zlepšení kvality klasifikace. Experimenty, výsledky a budoucí využití této práce jsou taktéž prezentovány.
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Optimization of network flow monitoring
Žádník, Martin ; Lhotka,, Ladislav (referee) ; Matoušek, Radomil (referee) ; Sekanina, Lukáš (advisor)
The thesis deals with optimization of network flow monitoring. Flow-based network traffic processing, that is, processing packets based on some state information associated to the flows which the packets belong to, is a key enabler for a variety of network services and applications. The number of simultaneous flows increases with the growing number of new services and applications. It has become a challenge to keep a state per each flow in a network device processing high speed traffic. A flow table, a structure with flow states, must be stored in a memory hierarchy. The memory closest to the processing is known as a flow cache. Flow cache management plays an important role in terms of its effective utilization, which affects the performance of the whole system. This thesis focuses on an automated design of cache replacement policy optimized to a deployment on particular networks. A genetic algorithm is proposed to automate this process. The genetic algorithm generates and evaluates evolved replacement policies by a simulation on obtained traffic traces. The proposed algorithm is evaluated by designing replacement policies for two variations of the cache management problem. The first variation is an evolution of the replacement policy with an overall low number of state evictions from the flow cache. The second variation represents an evolution of the replacement policy with a low number of evictions belonging to large flows only. Optimized replacement policies for both variations are found while experimenting with various encoding of the replacement policy and genetic operators. The newly evolved replacement policies achieve better results than other tested policies. The evolved replacement policy lowers the overall amount of evictions by ten percent in comparison with the best compared policy. The evolved replacement policy focusing on large flows lowers the amount of their evictions two times. Moreover, no eviction occurs for most of the large flows (over 90%). The evolved replacement policy offers better resilience against flooding the flow cache with large amount of short flows which are typical side effects of scanning or distributed denial of service activities. An extension of the replacement policy is also proposed. The extension complements the replacement policy with an additional information extracted from packet headers. The results show further decrease in the number of evictions when the extension is used.
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Is genetic diversity congruent with morphological diversity across the distributional range of the Melampyrum subalpinum group (Orobanchaceae)?
CHLUMSKÝ, Jan
Allozymes were used to assess the genetic structure of 27 populations of Melampyrum subalpinum group and an artificial pollination experiment was carried out to examine the possibility of autogamy. Genetic variation was generally congruent with the known morphological variation of the group. The results corresponded with the central-marginal concept. Allelic enrichment due to hybridization with M. nemorosum was observed in some Austrian populations. Czech and Slovak populations do not differ from Austrian populations. The high inbreeding coefficient and the pollination experiment do not contradict the possibility of autogamy.
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