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DEVELOPMENT OF IMMIGRATION POLICY AND IMMIGRATION IN AUSTRALIA
Žáková, Samantha ; Janská, Eva (advisor) ; Pavelková, Lenka (referee)
The main aim of the thesis is to describe the development of immigration policy and immigration in Australia since the first colonization of the country up to the present. Individual waves since 1778 are analyzed, i.e. from the colonization of British settlers and immigration associated with gold rush to non-British immigration after the World War II. The work describes the shift from discriminatory White Australia policy which was abandoned mainly after Migration Act 1958 to multicultural policy. There were two different approaches to multiculturalism: assimilation of immigrants during the 1950s and their integration in the 1960s. Gaugh Whitlam government finally abolished the White Australia policy in 1973 replacing both previous positions with multicultural policy. Governments of prime ministers and changes of their approaches to multicultural policies since 1970s up to the present are discussed. Current migration and humanitarian program as well as special Department of Immigration and visa system are also part of this work.
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Porovnání metod pro odhad omezených veličin s aplikací na ekonomická data
Musil, Karel ; Pavelková, Lenka (advisor) ; Hlávka, Zdeněk (referee)
The thesis introduces an overview of techniques for filtering of unobserved variables using a state-space representation of a model and state inequality constraints. It is mainly aimed at a derivation of the linear Kalman filter, its extension into a form of a non-linear filter and imposing state constraints. The state uniform model with noise bounds and the sequential importance sampling, as a method of particle filters using Monte Carlo simulations, are described as alternative methods. These three methods are applied on a simple semi-structural model for a monetary policy analysis. The filtration is based on Czech macroeconomic data and reflects an imposed non-negative state constraint on the interest rate. Results of the algorithms are compared and discussed.
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Spatial Distribution of Immigrants in Italy
Pavelková, Lenka ; Drbohlav, Dušan (advisor) ; Siwek, Tadeusz (referee)
This thesis focuses on spatial distribution of immigrants in Italy. In the beginning, it presents three migration theories used in the thesis (network theory, Human Capital Model, institutional theory) and related hypothesis for the research. Then it summarises the migration history of Italy since the Second World War and the current situation. After that, the tex proceeds to the examination of the spatial distribution of immigrants in Italy. The examination is done in two ways. First, there is analysis of statistical data by correlation analysis which tries to find factors which explain the immigrants' distribution in Italy. Second is the analysis of the results of a survey done in Trieste, Italy, between March and June 2011 among the two largest groups of immigrants living in the province of Trieste (those with citizenship of Serbia and Romania). The main results are that one of the most important factors for the immigrants' distribution are the job opportunities and better earnings for themselves and for their families. Interestingly, the network theory wasn't supported by the research, especially for Romanians who seem to prefer provinces with less Romanians (and foreigners in general) already present. After the analysis, the results are compared with the situation in the Czech Republic. For further...
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Normal and uniform noise - violation of the assumption on noise distribution in model identification
Jirsa, Ladislav ; Pavelková, Lenka
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has created the basis for theoretical and algorithmic solutions of respective tasks. However, many continuous variables are strictly bounded and their uncertainty may have origin in various physical processes which causes a non-normal distribution of their noise. Furthermore, adaptation of algorithms based on normal model for identification of models with bounded noise can distort the estimates due to inconsistent handling of uncertainty. This report describes a study to compare results of estimation algorithms based on assumption of normal and uniform noise. Data sequences processed by the algorithms have normal noise bounded by a low limit with respect to standard deviation. We illustrate disparity between noise assumption and a true noise distribution and its influence on the quality of the estimates. It is a part of an effort to develop theory and fast algorithms for estimation with bounded noise, applicable in practice.
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Fuel consumption optimization: Modication of optimality criterion in the case of speeding risk
Pavelková, Lenka
The report describes some experiments related to the project Ekodrive, TA ČR TA0103012, which concerns with fuel consumption optimisation under condition of keeping the recommended speed. During control, a speeding risk can occur if designed control leads to a vehicle speed that is higher than speed limit. This problem is solved by switching to an additional (non-optimal) regulator and/or by using a deterministic logic control block whenever a speed limit overrun is predicted. Here, an alternative approach is proposed which uses an original controller only but the optimality criterion is extended by one terms that penalises vehicle speed increments.
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Preliminaries of probabilistic hierarchical fault detection
Jirsa, Ladislav ; Pavelková, Lenka ; Dedecius, Kamil
The paper proposes a novel probabilistic fault detection and isolation (FDI) system that enables to evaluate dynamically the industrial system condition (health) at any level of its functional hierarchy. The investigated industrial system is considered as a set of interconnected individual components. Each component acts in its noisy environment as an imperfect participant, more or less dependent on neighbouring components and, in turn, influencing some others. The nature of the problem prevents us from expressing sufficiently hard propositions about the health of the system as a whole at once but we can observe and construct propositions at lower system hierarchies. These propositions (opinions) are combined at higher levels using the rules of probabilistic logic, retaining the ignorance and finally yielding a single opinion on the health of the whole monitored system.
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State estimation with missing data and bounded uncertainty
Pavelková, Lenka
The paper deals with two problems in the state estimation: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time state space model whose uncertainties are bounded is proposed here. The algorithm also copes with situations when some data for identification are missing. The Bayesian approach is used and maximum a posteriori probability estimates are evaluated in the discrete time instants. The proposed estimation algorithm is applied to the estimation of vehicle position when incomplete data from global positioning system together with complete data from the inertial measurement unit are at disposal.
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Parciální zapomínání. Nová metoda sledování časově proměnných parametrů
Dedecius, Kamil ; Nagy, Ivan ; Kárný, Miroslav ; Pavelková, Lenka
Tracking of slowly varying parameters is an important task in the theory of adaptive systems. Majority of prediction and control algorithms, employing regression models like autoregression model (AR), autoregression model with exogenous inputs (ARX), autoregression model with moving average (ARMA) etc., assume a carefully defined model structure and correctly estimated parameters. Problems arise, when the model parameters vary in time. The problems of slowly time-varying model parameters were given a thorough attention. The proposed partial forgetting method tries to solve this issue by a new approach.
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