The objective of the thesis is to create software, which implements industrial PID controller enabling smooth switch-over and automatic tuning of parameters. The next objective is to create visualization for this controller. Whole controller have to be implemented into PLC Power Panel (B&R company). At the beginning of the thesis there is theoretical description of all implemented algorithms (discrete control algorithms derived from PID, smooth switch-over, antiwindup, identification algorithms). In the following there is designed program structure, which is state machine-shaped. All algorithms are implemented in ANSI C at first as s-function for Matlab/Simulink program, which enables to simulate and verify the controller. Implementation of controller into PLC Power Panel through the use of Automation Studio program from B&R company is described in the next part. Visualization is created in the same program. Simulations and verifications on mathematical and physical model demonstrates functionality of implemented algorithms, but also points out some problems associated with the use of identification algorithms in real world (noise, quantization in A/D and D/A converter).

Master’s thesis is focused on the adaptive controllers. The first theoretic part mainly describes the parametric identification, which belongs to the most important part of the adaptive controller’s structure. Classical identification methods (the recursive least squares methods) are firstly mentioned and afterwards the identification methods based on the neural network (the Marquardt-Levenberg algorithm and the new identification algorithm NIA inspired by the neural networks) are described. At the conclusion of the theoretic part there are mentioned the algorithm of the adaptive controller’s tuning which uses the identification parameters (the modified Z-N method) and the tested types of adaptive controllers. Particular results, which were found out by verifying of the adaptive controllers on the simulation and real models, are contained in second, the practical, part of the thesis. Finally, achieved results are compared with the classical discrete PID controller and with the adaptive controller of the B&R company.

This thesis studies inference to the complier treatment effect denoted LATE. The standard approach is to base the inference on the two-stage least squares (2SLS) estimator and asymptotic Neyman inference, i.e., the t-test. The paper suggests a Fisher Randomization Test based on the t-test statistic as an alternative to the Neyman inference. Based on the setup with a randomized experiment with noncompliance, for which one can identify the LATE, I compare the two approaches in a Monte Carlo (MC) simulations. The results from the MC simulation is that the Fisher randomization test is not a valid alternative to the Neyman’s test as it has too low power.

The hierarchical (<i>H-</i>) matrix format allows storing a variety of dense matrices from certain applications in a special data-sparse way with linear-polylogarithmic complexity. Many operations from linear algebra like matrix-matrix and matrix-vector products, matrix inversion and LU decomposition can be implemented efficiently using the <i>H</i>-matrix format. Due to its importance in solving many problems in numerical linear algebra like least-squares problems, it is also desirable to have an efficient QR decomposition of <i>H</i>-matrices. In the past, two different approaches for this task have been suggested. We will review the resulting methods and suggest a new algorithm to compute the QR decomposition of an <i>H</i>-matrix. Like other <i>H</i>-arithmetic operations the <i>H</i>QR decomposition is of linear-polylogarithmic complexity. We will compare our new algorithm with the older ones by using two series of test examples and discuss benefits and drawbacks of the new approach.

Tato práce se zabývá metodou úplně nejmenších čtverc·, která slouží pro odhad parametr· v lineárních modelech. V práci je uveden základní popis metody a její asymptotické vlastnosti. Je vysvětleno, jakým zp·sobem lze v konceptu metody využít neparametrický bootstrap pro hledání odhadu. Vlastnosti bootstrap od- had· jsou pak simulovány na pseudo náhodně vygenerovaných datech. Simulace jsou prováděny pro dvourozměrný parametr v r·zných nastaveních základního modelu. Jednotlivé bootstrap odhady jsou v rovině řazeny pomocí Mahalanobis a Tukey statistical depth function. Simulace potvrzují, že bootstrap odhad dává dostatečně dobré výsledky, aby se dal využít pro reálné situace.

Partial Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly correlated) variables. While being a well established modeling tool in chemometrics, this thesis adapts PLS to financial data to predict the movements of the business cycle represented by the OECD Composite Leading Indicators. High-dimensional data is used, and a model with automated variable selection through a genetic algorithm is developed to forecast different economic regions with good results in out-of-sample tests. / Partial Least Squares är både en regressionsmetod och ett verktyg för variabelselektion som är specielltlämpligt för modeller baserade på en stor mängd (möjligtvis korrelerade) variabler.Medan det är en väletablerad modelleringsmetod inom kemimetri, anpassar den häruppsatsen PLS till finansiell data för att förutspå rörelserna av konjunkturen,representerad av OECD's Composite Leading Indicator. Högdimensionella dataanvänds och en model med automatiserad variabelselektion via en genetiskalgoritm utvecklas för att göra en prognos av olika ekonomiska regioner medgoda resultat i out-of-sample-tester

This study work deals with the determination of the gravimetric geoid model for Kazakhstan by using the KTH-method. A number of data sets were collected for this work, such as the gravity anomalies, high-resolution Digital Elevation Model (DEM), Global Geopotential Models (GGMs) and GPS/Levelling data. These data has been optimally combined through the KTH approach, developed at the Royal Institute of Technology (KTH) in Stockholm. According to this stochastic method, Stokes’ formula is being used with the original surface gravity anomaly, which combine with a GGM yields approximate geoid heights. The corrected geoid heights are then obtained by adding the topographic, downward continuation, atmospheric and ellipsoidal corrections to the approximate geoid heights. To compute the geoid model for Kazakhstan as accurately as possible with available data set different numerical tests have been performed: Choice of the best fit geopotential model in the computation area Investigations for the best choice of the initial condition for determination of the least-squares parameters Selection of the best parametric model for reducing the effect of the systematic error and data inconsistencies between computed geoid heights and GPS/Levelling heights.  Finally, 5'x5' Kazakh gravimetric geoid (KazGM2010) has been modelled.

The primary objective of this study was to develop an empirical model that accounts for factors affecting the demand for international tourist hotels, and that enables demand estimation and projection of demand, in the context of the metropolitan Seoul area. Models were hypothesized to explain market behavior of selected segments of the hotel industry. Demand Model I accounted for international hotel demand incorporating such explanatory variables as price, trade volume and events. Both demand and price were treated as endogenous variables and the time-dependent error processes were all examined. Demand Model I was further expanded by incorporating segmentation variables. The inclusion of segmentation variables into the model in Demand Model II enabled the analysis of the interdependence of market segments that affects demand. Along with the incorporation of a time series structure, a system of equations was employed for Demand Model II. The results indicated that the explanatory variables, which were own price, the number of events and the volume of trade, had a significant impact on international hotel demand. The results from the demand equations also revealed that the demand for a segment is significantly influenced by price and demand for other segments. A cross-segment substitution effect on the demand side is quite relevant for the international lodging market in Seoul and demand for a market segment fluctuates in the same direction as the total market demand changes. From the price equations, the market price of lodging services was found to be related to demand and supply influences simultaneously. The results also indicated that seasonality and economic factors, such as exchange rates and consumer price index, have significant influence directly on international lodging prices. Such factors also were found to have indirect effects on the demand level. / Ph. D.

This study aims at analysing how African oil benchmarks within the OPEC Reference Basket relative to the WTI and Brent benchmarks which are considered as global pricing benchmarks for the period starting from 1997-2008. The Nigerian Bonny Light and Algerian Saharan blend were the two benchmarks used for this study. A time series analysis was applied to the weekly price data series set and with the aid of a breakpoint unit root test and Cusum of Squared test to determine if there was a change in the persistence of the spread of each African benchmark relative to the global benchmarks. The results for from the unit root test indicated the presence of a structural break in the price spread in 2004 for the Bonny Light benchmark and in 2005 for the Saharan blend relative to both global benchmarks. The Cusum Squared test for the four benchmark pairings indicated a change in persistence of the price spreads. The null hypothesis was rejected for the alternative hypothesis of the price spread process having a relatively high persistence value after a while. The Cusum Test results showed a change in persistence for both African benchmarks relative to the WTI benchmark and no change in persistence relative to the Brent benchmark. The results of from the Time series analysis indicated the competitive nature of African benchmarks relative to global benchmarks and this could benefit exporting countries by virtue of setting up derivative markets. The derivative markets would allow for the trade of benchmark spreads, futures contracts, options and other financial instruments for African oil producers.

Chatbots are very widely used nowadays. However, much of the research on Chatbots have had a technology focus or has been limited to studies of adoption. To take advantage of the potential associated with chatbots, research that addresses the issues online users face when interacting with such programs is needed. The study described in this paper used the task-to technology fit theory to address the question of how individual characteristics and task/technology requirements influence the performance and utilization of chatbots. This paper used the quantitative methodology over two sets of data collected independently from two different populations. The first dataset of 100 respondents was obtained firstly through a structured questionnaire administered at Linnaeus University Campus in Växjö. The respondents are students in the university who use chatbots regularly. A second dataset was also collected from 20 participants through a practical test experiment with three different chatbots (Eliza, Rose, and Watson). The result and the data were then recorded through an online interview via the zoom application. The two datasets were analyzed quantitatively using comparative factor analysis with the aid of Smart PLS software. While few variables provided little support for the claims, the majority of the variables show strong support for the importance of task–technology fit, as a measure of chatbot utilization and performance based on individual characteristics as well as the task/technology requirements.