Dans cette thèse, on a d’abord considéré une équation d'onde. On a premièrement montré que l’équation est bien-posée presque sûre par la méthode de décomposition de fréquence de Bourgain sous l’hypothèse de régularité que s > 2(p−3)/(p-1). Ensuite, nous avons réduit de cette exigence de régulation à (p-3)/(p−1) en appelant une estimation probabiliste a priori. Nous considérons également l’approximation des solutions obtenues ci-dessus par des solutions lisses et la stabilité de cette procédure d’approximation. Et nous avons conclu que l’équation est partout mal-posée dans le régime de super-critique. Nous avons considéré ensuite l’équation du faisceau quintique sur le tore 3D. Et nous avons montré que cette équation est presque sûr bien-posée globalement dans certain régimes de super-critique. Enfin, nous avons prouvé que la mesure de l’image de la mesure gaussienne sous l’application de flot de l’équation BBM généralisé satisfait une inégalité de type log-Sobolev avec une petit peu de perte de l’intégrabilité. / In this thesis, we consider a wave equation. We first showed that the equation is almost sure global well-posed via Bourgain’s high-low frequency decomposition under the regularity assumption s > 2(p−3)/(p−1). Then we lowered down this regularity requirement to be (p−3)/(p−1) by invoking a probabilistic a priori estimate. We also consider approximation of the above achieved solutions by smooth solutions and the stability of this approximating procedure. And we concluded that this equation is everywhere ill-posed in the super-critical regime. Next, we considered the quintic beam equation on 3D torus. And we showed that this equation is almost sure global well-posed in certain super-critical regime. Lastly, we proved that the image measure of the Gaussian measure under the generalized BBM flow map satisfies a log-Sobolev type inequality with a little bit loss of integrability.

Diploma thesis is focused on design and optimalization of log splitter. Thesis consists of search of log splitter types and common used technologies, mechanical design, optimalization, design of propultion system, risk analysis and financial overview. Log splitter is designed to meet needs of automatization. Safety, industrial design and ergonomy has been considered in the design.

No description available.

The thesis is focused on the design of a measuring antenna for 1 – 6 GHz band. It deals with the types of log periodic antennas with the the detailed elaboration of log periodic dipole antennas (LPDA). One can find the description of their functions, maximum features and the possibilities to use the asymmetrical feeder. Also there is the procedure of LPDA design and the realization of the design itself including simulation in 4NEC2 programme. In the next part there is a planar LPDA realization with the microstrip-to-balanced stripline balun symmetrization. This design was simulated and tested within the frames of Zeland IE3D programme. The last part contains the measuring results of S parametres as well as beam characteristics of the planar version of LPDA.

A number of recent studies have shown that the dimensionality of the neural activity in the cortex is low. However, what network structures are capable of producing such activity is not theoretically well understood. In this thesis, I discuss a few possible solutions to this problem, and demonstrate that a network with a multidimensional attractor can give rise to such low dimensional activity. The network is created using the Neural Engineering Framework, and exhibits several biologically plausible features, including a log-normal distribution of the synaptic weights. / Ett antal nyligen publicerade studier has visat att dimensionaliten för neural aktivitet är låg. Dock är det inte klarlagt vilka nätverksstrukturer som kan uppbringa denna typ av aktivitet. I denna uppsats diskuterar jag möjliga lösningsförslag, och demonstrerar att ett nätverk med en flerdimensionell attraktor ger upphov till lågdimensionell aktivitet. Nätverket skapas med hjälp av the Neural Engineering Framework, och uppvisar ett flertal biologiskt trovärdiga egenskaper. I synnerhet är fördelningen av synapsvikter log-normalt fördelad.

Wenn sich Bibliotheksmitarbeiterin und Bibliotheksnutzer nicht von Angesicht zu Angesicht gegenüber stehen, ist diese Frage berechtigt: Sind Sie ein Mensch? Oder sind Sie ein automatisierter Chatroboter – vergleichbar mit telefonischen Informationshotlines, die wohl jeder aus leidvoller Erfahrung kennt? Gestellt wurde uns diese Frage tatsächlich – und mehrfach – von Bibliotheksnutzerinnen und -nutzern, die ihr Glück kaum fassen konnten, wie einfach es sein kann, eine Antwort direkt online zu bekommen. Es muss nicht immer die Auskunftstheke sein. Virtuell hilft auch!

This thesis evaluates the performance of different machine learning approaches to log classification based on a dataset derived from simulating intrusive behavior towards an enterprise web application. The first experiment consists of performing attacks towards the web app in correlation with the logs to create a labeled dataset. The second experiment consists of one unsupervised model based on a variational autoencoder and four super- vised models based on both conventional feature-engineering techniques with deep neural networks and embedding-based feature techniques followed by long-short-term memory architectures and convolutional neural networks. With this dataset, the embedding-based approaches performed much better than the conventional one. The autoencoder did not perform well compared to the supervised models. To conclude, embedding-based ap- proaches show promise even on datasets with different characteristics compared to natural language.

Maskininlärning (ML) är idag ett mycket aktuellt, populärt och aktivt forskat område. Därav finns det idag en stor uppsjö av olika avancerade och moderna ML-algoritmer. Svårigheten är att bland dessa identifiera den mest optimala att applicera på ens tillämpningsområde. Algoritmer som bygger på Gradientförstärkning (eng. Gradient Boosting (GB)) har visat sig ha ett väldigt brett spektrum av appliceringsområden, flexibilitet, hög förutsägelseprestanda samt låga tränings- och förutsägelsetider. Huvudsyftet med denna studie är på klassificerings- och regressiondataset utvärdera och belysa prestandaskillnaderna av 5 moderna samt 2 äldre GB-algoritmer. Målet är att avgöra vilken av dessa moderna algoritmer som presterar i genomsnitt bäst utifrån på flera utvärderingsmått. Initialt utfördes en teoretisk förstudie inom det aktuella forskningsområdet. Algoritmerna XGBoost, LightGBM, CatBoost, AdaBoost, SnapBoost, KTBoost, GBDT implementerades på plattformen Google Colab. Där utvärderades dess respektive, tränings- och förutsägelsestid samt prestandamåtten, uppdelat i ROCAUC och Log Loss för klassificering samt R2 och RMSE för regression. Resultaten visade att det generellt var små skillnader mellan dom olika testade algoritmerna. Med undantag för AdaBoost som i allmänhet, med större marginal, hade den sämsta prestandan. Därmed gick det inte i denna jämförelse utse en klar vinnare. Däremot presterade SnapBoost väldigt bra på flera utvärderingsmått. Modellresultaten är generellt sätt väldigt begränsade och bundna till det applicerade datasetet vilket gör att det överlag är väldigt svårt att generalisera det till andra datauppsättningar. Detta speglar sig från resultaten med svårigheten att identifiera ett ML-ramverk som utmärker sig och presterar bra i alla scenarier. / Machine learning (ML) is today a very relevent, popular and actively researched area. As a result, today there exits a large numer of different advanced and modern ML algorithms. The difficulty is to identify among these the most optimal to apply to one’s area of application. Algorithms based on Gradient Boosting (GB) have been shown to have a very wide range of application areas, flexibility, high prediction performance and low training and prediction times. The main purpose of this study is on classification and regression datasets evaluate and illustrate the performance differences of 5 modern and 2 older GB algorithms. The goal is to determine which of these modern algorithms, on average, performs best on the basis of several evaluation metrics. Initially, a theoretical feasibility study was carried out in the current research area. The algorithms XGBoost, LightGBM, CatBoost, AdaBoost, SnapBoost, KTBoost, GBDT were implemented on the Google Colab platform. There, respective training and prediction time as well as the performance metrics were evaluated, divided into ROC-AUC and Log Loss for classification and R2 and RMSE for regression. The results showed that there were generally small differences between the different algorithms tested. With the exception of AdaBoost which in general, by a larger margin, had the worst performance. Thus, it was not possible in this comparison to nominate a clear winner. However, SnapBoost performed very well in several evaluation metrics. The model results are generally very limited and bound to the applied dataset, which makes it generally very difficult to generalize it to other data sets. This is reflected in the results with the difficulty of identifying an ML framework that excels and performs well in all scenarios.

Transmitted data logging in routers is the main point of this semestral project. The suggestion of a system for data flows logging in routers and selection of suitable technology, that is used by implementation of the system within this thesis, is based on this analysis. In the thesis, a law responsibility of router administrator for transmitting data is analysed. In the next part, a general introduction to issue of data logging in computer networks including basic description of protocols and fundamentals of standard communication models is presented. Analysis of real enviroment is following. Suggestion and implementation of the system is described too. In the last part a reached results of this thesis are revealed.

[pt] O principal objetivo desta dissertação é realizar a previsão da reserva IBNR. Para isto foi desenvolvido um modelo estatístico de distribuições combinadas que busca uma adequada representação dos dados. A reserva IBNR, sigla em inglês para Incurred But Not Reported, representa o montante que as seguradoras precisam ter para pagamentos de sinistros atrasados, que já ocorreram no passado, mas ainda não foram avisados à seguradora até a data presente. Dada a importância desta reserva, diversos métodos para estimação da reserva IBNR já foram propostos. Um dos métodos mais utilizado pelas seguradoras é o Método Chain Ladder, que se baseia em triângulos run-off, que é o agrupamento dos dados conforme data de ocorrência e aviso de sinistro. No entanto o agrupamento dos dados faz com que informações importantes sejam perdidas. Esta dissertação baseada em outros artigos e trabalhos que consideram o não agrupamento dos dados, propõe uma nova modelagem para os dados não agrupados. O modelo proposto combina a distribuição do atraso no aviso da ocorrência, representada aqui pela distribuição log-normal truncada (pois só há informação até a última data observada); a distribuição da quantidade total de sinistros ocorridos num dado período, modelada pela distribuição Poisson; e a distribuição do número de sinistros ocorridos em um dado período e avisados até a última data observada, que será caracterizada por uma distribuição Binomial. Por fim, a quantidade de sinistros IBNR foi estimada por método e pelo Chain Ladder e avaliou-se a capacidade de previsão de ambos. Apesar da distribuição de atrasos do modelo proposto se adequar bem aos dados, o modelo proposto obteve resultados inferiores ao Chain Ladder em termos de previsão. / [en] The main objective of this dissertation is to predict the IBNR reserve. For this, it was developed a statistical model of combined distributions looking for a new distribution that fits the data well. The IBNR reserve, short for Incurred But Not Reported, represents the amount that insurers need to have to pay for the claims that occurred in the past but have not been reported until the present date. Given the importance of this reserve, several methods for estimating this reserve have been proposed. One of the most used methods for the insurers is the Chain Ladder, which is based on run-off triangles; this is a format of grouping the data according to the occurrence and the reported date. However this format causes the lost of important information. This dissertation, based on other articles and works that consider the data not grouped, proposes a new model for the non-aggregated data. The proposed model combines the delay in the claim report distribution represented by a log normal truncated (because there is only information until the last observed date); the total amount of claims incurred in a given period modeled by a Poisson distribution and the number of claims occurred in a certain period and reported until the last observed date characterized by a binomial distribution. Finally, the IBNR reserve was estimated by this method and by the chain ladder and the prediction capacity of both methods will be evaluated. Although the delay distribution seems to fit the data well, the proposed model obtained inferior results to the Chain Ladder in terms of forecast.