Global ETD Search

1	Náhodné kótované množiny / Random marked sets Kráľová, Veronika January 2016 (has links) In this thesis, two models of marked point processes are investigated. One of the marks have a continuous distribution on a compact Riemannian manifold. The von Mises distribution and its properties are studied. Metropolis-Hastings algorithm of Markov chain Monte Carlo method is used for the simulation of Gibbs segment process. Takacs-Fiksel estimator and its modified version are examined. A kernel density estimator and entropy estimator are proposed and applied to simulated and real data. Powered by TCPDF (www.tcpdf.org)
2	Nestacionární procesy částic / Nonstationary particle processes Jirsák, Čeněk January 2011 (has links) Title: Nonstacionary particle processes Author: Čeněk Jirsák Department: Department of Probability and Mathematical Statistics Supervisor: Doc. RNDr. Jan Rataj, CSc., Mathematical Institute, Charles University Supervisor's e-mail address: rataj@karlin.mff.cuni.cz Abstract: Many real phenomena can be modeled as random closed sets of different Hausdorff dimension in Rd . One of the main characteristics of such random set is its expected Hausdorff measure. In case that this measure has a density, the density is called intensity function. In present paper we define a nonparametric kernel estimation of the intensity function. The concept of Hk -rectifiable set has a key role here. Properties of kernel estimation such as unbiasness or convergence behavior are studied. As the esti- mation may be difficult to compute precisely numerical approximations are derived for practical use. Parametric models are also briefly mentioned and the kernel estimation is used with the minimum contrast method to estimate the parameters of the model. At last the suggested methods are tested on simulated data. Keywords: stochastic geometry, intensity measure, random closed set, kernel estimation 1
3	Náhodné kótované množiny a redukce dimenze / Random marked sets and dimension reduction Šedivý, Ondřej January 2014 (has links) Random closed sets and random marked closed sets present an important general concept for the description of random objects appearing in a topological space, particularly in the Euclidean space. This thesis deals with two major tasks. At first, it is the dimension reduction problem where dependence of a random closed set on underlying spatial variables is studied. Solving this problem allows to find the most significant regressors or, possibly, to identify the redundant ones. This work achieves both theoretical results, based on extending the inverse regression techniques from classical to spatial statistics, and numerical justification of the methods via simulation studies. The second topic is estimation of characteristics of random marked closed sets which is primarily motivated by an application in the microstructural research. Random marked closed sets present a mathematical model for the description of ultrafine-grained microstructures of metals. Methods for statistical estimation of their selected characteristics are developed in the thesis. Correct quantitative characterization of microstructure of metals allows to better understand their macroscopic properties.
4	Formal Concept Analysis Methods for Description Logics / Formale Begriffsanalyse Methoden für Beschreibungslogiken Sertkaya, Baris 09 July 2008 (has links) (PDF) This work presents mainly two contributions to Description Logics (DLs) research by means of Formal Concept Analysis (FCA) methods: supporting bottom-up construction of DL knowledge bases, and completing DL knowledge bases. Its contribution to FCA research is on the computational complexity of computing generators of closed sets. Description Logics Formal Concept Analysis knowledge base bottom-up construction ontology completion attribute exploration closed set minimal generators Beschreibungslogik Formale Begriffsanalyse Wissensbasis Ontologie Attributexploration abgeschlossene Menge minimale Erzeuger ddc:004 rvk:ST 125
5	Formal Concept Analysis Methods for Description Logics Sertkaya, Baris 15 November 2007 (has links) This work presents mainly two contributions to Description Logics (DLs) research by means of Formal Concept Analysis (FCA) methods: supporting bottom-up construction of DL knowledge bases, and completing DL knowledge bases. Its contribution to FCA research is on the computational complexity of computing generators of closed sets. info:eu-repo/classification/ddc/004 ddc:004
6	Machine Learning for Speech Forensics and Hypersonic Vehicle Applications Emily R Bartusiak (6630773) 06 December 2022 (has links) <p>Synthesized speech may be used for nefarious purposes, such as fraud, spoofing, and misinformation campaigns. We present several speech forensics methods based on deep learning to protect against such attacks. First, we use a convolutional neural network (CNN) and transformers to detect synthesized speech. Then, we investigate closed set and open set speech synthesizer attribution. We use a transformer to attribute a speech signal to its source (i.e., to identify the speech synthesizer that created it). Additionally, we show that our approach separates different known and unknown speech synthesizers in its latent space, even though it has not seen any of the unknown speech synthesizers during training. Next, we explore machine learning for an objective in the aerospace domain.</p> <p><br></p> <p>Compared to conventional ballistic vehicles and cruise vehicles, hypersonic glide vehicles (HGVs) exhibit unprecedented abilities. They travel faster than Mach 5 and maneuver to evade defense systems and hinder prediction of their final destinations. We investigate machine learning for identifying different HGVs and a conic reentry vehicle (CRV) based on their aerodynamic state estimates. We also propose a HGV flight phase prediction method. Inspired by natural language processing (NLP), we model flight phases as “words” and HGV trajectories as “sentences.” Next, we learn a “grammar” from the HGV trajectories that describes their flight phase transition patterns. Given “words” from the initial part of a HGV trajectory and the “grammar”, we predict future “words” in the “sentence” (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data. We also show that it can be used in a transfer learning scenario to predict flight phases of HGV trajectories that exhibit new maneuvers and behaviors never seen before during training.</p> Audio processing Computer vision Digital forensics Deep learning machine learning deep learning speech forensics media forensics convolutional neural network transformer convolutional transformer ensemble spectrogram analysis mel spectrogram analysis synthesized speech detection synthesized speech attribution closed set open set t-stochastic neighbor embedding latent space analysis hypersonics hypersonic glide vehicles vehicle classification flight phase prediction stochastic grammar k-nearest neighbors support vector machine probabilistic context-free grammar automatic distillation of structure generalized earley parser transfer learning

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