När nya betongbroar över järnväg ska upprättas finns det många aspekter som försvårar byggandet, som t.ex. att trafiken inte kan stoppas helt förutom under kortare perioder under byggtiden. Tågstoppen planeras in många år i förväg vilket gör att produktionen måste planeras och utföras utefter de planerade stoppen. Det är svårt att få till en industrialiserad brobyggnadsprocess med högre effektivitet och mer upprepning. Den traditionella platsgjutna metoden är den metod som är vanligast i Sverige trots att det finns metoder som skulle kunna ersätta den. Det huvudsakliga syftet med rapporten är att ta reda på om prefabricerade betongelement på plats är en möjlig metod för att underlätta byggandet av nya betongbroar över järnväg. Denna rapport baseras på en fallstudie av ett befintligt broprojekt för att se vilka fördelar och förbättringar metoden med prefab på plats kan ge i olika aspekter. Utöver detta har intervjuer och en enkätundersökning genomförts för att samla in material och åsikter om prefab och industrialiserade brobyggnadsprocesser. Resultatet i denna rapport visar att metoden med prefab på plats är mer fördelaktig än den traditionella platsgjutna metoden i flera hänseenden. Det är en metod som gör det möjligt att nå upprepning och serietillverkning av broar. Vid intervjuerna framkom att entreprenörerna är intresserade av prefablösningar men bland annat krav på utformning och konservativ syn på prefab från beställarna hindrar möjligheterna för att tillämpa metoden / When new concrete bridges over railway is to be established there are many aspects that complicates the construction, such as that traffic not can be stopped completely except for short periods during the construction process. The train stop is planned many years in advance, which means that construction must be planned and performed along the planned stops. It is difficult to get to an industrial bridge construction process with higher efficiency and more repetition. The traditional cast on site method is the most common in Sweden although there are methods that could replace it. The main purpose of the report is to find out if precast concrete elements on site is a possible method to facilitate the construction of new concrete bridges over the railway. This report is based on a case study of an existing bridge project to see the benefits and improvements the method with precast on site can provide in various aspects. Also interviews and surveys was conducted to collect opinions about prefabrication and industrial bridge construction. The results in this report show that the method of prefab on site is more advantageous than the traditional cast on site method in several respects. It is a method that makes it possible to reach repetition and serial production of bridges. The interviews revealed that contractors are interested in prefabricated solutions, but among other requirements for the design and conservative view of prefabrication from clients prevents the possibility of applying the method.

There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1

Ultra wideband (UWB) technology plays a significant role in wireless communication. The complex antenna transfer function (CATF) of an UWB antenna provides important information required for better channel designs and communication systems. In this dissertation the CATF of a Double ridge guide horn (DRGH) antenna is determined and used as a standard antenna for UWB measurements. Two methods were used: the two antenna method in an anechoic chamber and a modified gain-transfer method in a compact antenna test range (CATR). Measurements were performed with a vector network analyser (VNA) in the frequency domain, in the anechoic chamber and the CATR. The distance measurements required to calculate the CATF from the S-parameter measurements were performed in the time domain. The CATF of the standard antenna was determined using two identical antennas and then it was shown that a modified gain-transfer method can be used to determine the CATF of any unknown antenna in a CATR, using the standard antenna as a reference. Some of the challenges were to obtain the correct equations and measurement method to obtain the CATF in a CATR. The standard antenna was used to investigate uncertainty contributions for the measurements in the CATR. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

For multivariate data, when testing homogeneity of covariance matrices arising from two or more groups, Bartlett's (1937) modified likelihood ratio test statistic is appropriate to use under the null hypothesis of equal covariance matrices where the null distribution of the test statistic is based on the restrictive assumption of normality. Zhang and Boos (1992) provide a pooled bootstrap approach when the data cannot be assumed to be normally distributed. We give three alternative bootstrap techniques to testing homogeneity of covariance matrices when it is both inappropriate to pool the data into one single population as in the pooled bootstrap procedure and when the data are not normally distributed. We further show that our alternative bootstrap methodology can be extended to testing Flury's (1988) hierarchy of covariance structure models. Where deviations from normality exist, we show, by simulation, that the normal theory log-likelihood ratio test statistic is less viable compared with our bootstrap methodology. For functional data, Ramsay and Silverman (2005) and Lee et al (2002) together provide four computational techniques for functional principal component analysis (PCA) followed by covariance structure estimation. When the smoothing method for smoothing individual profiles is based on using least squares cubic B-splines or regression splines, we find that the ensuing covariance matrix estimate suffers from loss of dimensionality. We show that ridge regression can be used to resolve this problem, but only for the discretisation and numerical quadrature approaches to estimation, and that choice of a suitable ridge parameter is not arbitrary. We further show the unsuitability of regression splines when deciding on the optimal degree of smoothing to apply to individual profiles. To gain insight into smoothing parameter choice for functional data, we compare kernel and spline approaches to smoothing individual profiles in a nonparametric regression context. Our simulation results justify a kernel approach using a new criterion based on predicted squared error. We also show by simulation that, when taking account of correlation, a kernel approach using a generalized cross validatory type criterion performs well. These data-based methods for selecting the smoothing parameter are illustrated prior to a functional PCA on a real data set.

<div>Target detection, estimation, and discrimination have long been important research issues in the field of radar. Waveform design, analog signal processing, and digital signal processing are some techniques that can improve the detection, estimation, and discrimination ability. In this dissertation, we first address the sidelobe suppression from the waveform design point of view. We synthesize a non-constant modulus waveform for illumination of radar targets by applying a collection of constant modulus (linear frequency modulated (LFM) waveforms with different frequency offsets) waveforms from each transmitting array element in an antenna array, and we show from the ambiguity function that the non-constant modulus waveform has better performance with respect to the larger ambiguity function mainlobe-to-peak-sidelobe ratio than this ratio of a constant modulus (LFM-only) waveform. Furthermore, from the angular resolution point of view, the synthesized non-constant modulus waveform also has better performance than the angular resolution of a constant modulus waveform at the expense of the decrease in the signal energy on targets.</div><div><br></div><div>Secondly, we investigate radar delay-Doppler resolution enhancement from the digital signal processing viewpoint. We introduce the noise-target fringe analysis technique and combine it with the coherent CLEAN algorithm to provide accurate target parameter estimates in terms of delay, Doppler shift and intensity. Furthermore, the accuracy of target parameter estimates can be further improved by applying weighted non-linear least squares estimation.</div><div><br></div><div>Finally, we further aim for the improvement in radar delay-Doppler resolution. Instead of using the matched filter only, we propose a hybrid filter which combines a chirp matched filter and chirp mismatched filters. The hybrid filter output response shows much better performance in delay and Doppler resolution compared to the chirp matched filter output response. Thus, this hybrid filter design has better target identification capability than the original chirp matched filter. Furthermore, from a real implementation perspective, there is no need to significantly increase the hardware and software complexity of the radar, since we only need to mismatch the received waveform to another chirp waveform and perform some additional non-linear processing. Then a chirp radar system with high delay-Doppler resolution and accurate target discrimination ability can be easily achieved.</div>

The master's thesis deals with famous Notre-Dame de Paris, which was destroyed by fire in 2019. The work focuses specifically on the design of completion of the construction. The cathedral is situated in the historical centre of Paris on the Île de la Cité. A lot of damage was caused to the building after the devastating fire on 15 April 2019. The aim of the thesis was to create a spiritual place that would maintain its past, but at the same time manage to follow ecological solutions in the present. The new design of the attic creates a sacred place, in which we realize the importance of faith and the peace of God. The cathedral is open to the general public ? religious people from all over the world come here to experience the love, joy and happiness that the cathedral invokes. The whole attic can be described as an open space, which symbolizes infinity or immortality from the religious point of view. It represents the connection of the past, present and future. The space is designed to hold priestly celebrations and to be open for the public. It can also be used for various events, such as exhibitions of sculptures that survived the fire.

The aim of the study was to evaluate dierent types of logistic regression to find the optimal model to predict the gender of hospitalized COVID-19 patients. The models were based on COVID-19 case series data from Pakistan using a set of 18 explanatory variables out of which patient age and BMI were numerical and the rest were categorical variables, expressing symptoms and previous health issues.  Compared were a logistic regression using all variables, a logistic regression that used stepwise variable selection with 4 explanatory variables, a logistic Ridge regression model, a logistic Lasso regression model and a logistic Elastic Net regression model.  Based on several metrics assessing the goodness of fit of the models and the evaluation of predictive power using the area under the ROC curve the Elastic Net that was only using the Lasso penalty had the best result and was able to predict 82.5% of the test cases correctly.

This thesis discusses Slot antennas that are based in gap waveguide technology, which allows them to work with high frequency signals. It contains theoretical findings about antennas and waveguides, which are later used in the design. The practical section of this thesis concentrates on designing a specific gap waveguide for 10 and 24 GHz frequencies, starting with modeling and parameter optimalization. This gap waveguide is then used as a base for slot antenna design. The final antenna is designed for frequency of 10 GHz, for both linear and right-handed circular polarizations. With right-handed circular polarization, the achieved band was approximately 1.41 GHz, while the gain was 7,6 dB.

To assist technology advancements, it is important to continue the search for new materials. The stability of a crystal structures is closely connected to its formation energy. By calculating the formation energies of theoretical crystal structures it is possible to find new stable materials. However, the number of possible structures are so many that traditional methods relying on quantum mechanics, such as Density Functional Theory (DFT), require too much computational time to be viable in such a project. A presented alternative to such calculations is machine learning. Machine learning is an umbrella term for algorithms that can use information gained from one set of data to predict properties of new, similar data. Feature vector representations (descriptors) are used to present data in an appropriate manner to the machine. Thus far, no combination of machine learning method and feature vector representation has been established as general and accurate enough to be of practical use for accelerating the phase diagram calculations necessary for predicting material stability. It is important that the method predicts all types of structures equally well, regardless of stability, composition, or geometrical structure. In this thesis, the performances of different feature vector representations were compared to each other. The machine learning method used was primarily Kernel Ridge Regression, implemented in Python. The training and validation were performed on two different datasets and subsets of these. The representation which consistently yielded the lowest cross-validated error was a representation using the Voronoi tessellation of the structure by Ward et. al. [Phys. Rev. B 96, 024104 (2017)]. Following up was an experimental representation called the SLATM representation presented by Huang and von Lilienfeld [arXiv:1707.04146], which is partially based on the Radial Distribution Function. The Voronoi representation achieved an MAE of 0.16 eV/atom at 3534 training set size for one of the sets, and 0.28 eV/atom at 10086 training set size for the other set. The effect of separating linear and non-linear energy contributions was evaluated using the sinusoidal and Coulomb representations. The result was that separating these improved the error for small training set sizes, but the effect diminishes as the training set size increases. The results from this thesis implicate that further work is still required for machine learning to be used effectively in the search for new materials.

In the highly competitive environment that companies find themselves in today, it is key to have a well-functioning supply chain. For manufacturing companies, having a good supply chain is dependent on having a functioning production planning. The production planning tries to fulfill the demand while considering the resources available. This is complicated by the uncertainties that exist, such as the uncertainty in demand, in manufacturing and in supply. Several methods and models have been created to deal with production planning under uncertainty, but they often overlook the complexity in the supply uncertainty, by considering it as a stochastic uncertainty. To improve these models, a prediction based on earlier data regarding the supplier or item could be used to see when the delivery is likely to arrive. This study looked to compare different predictive models to see which one could best be suited for this purpose. Historic data regarding earlier deliveries was gathered from a large international manufacturing company and was preprocessed before used in the models. The target value that the models were to predict was the actual delivery time from the supplier. The data was then tested with the following four regression models in Python: Linear regression, ridge regression, Lasso and Elastic net. The results were calculated by cross-validation and presented in the form of the mean absolute error together with the standard deviation. The results showed that the Elastic net was the overall best performing model, and that the linear regression performed worst.