A Unified Load Generator for Geographically Distributed Generation ofNetwork Traffic

During the last decade, the Internet usage has been growing at an enormous rate which has beenaccompanied by the developments of network applications (e.g., video conference, audio/videostreaming, E-learning, E-Commerce and real-time applications) and allows several types ofinformation including data, voice, picture and media streaming. While end-users are demandingvery high quality of service (QoS) from their service providers, network undergoes a complex trafficwhich leads the transmission bottlenecks. Considerable effort has been made to study thecharacteristics and the behavior of the Internet. Simulation modeling of computer networkcongestion is a profitable and effective technique which fulfills the requirements to evaluate theperformance and QoS of networks. To simulate a single congested link, simulation is run with asingle load generator while for a larger simulation with complex traffic, where the nodes are spreadacross different geographical locations generating distributed artificial loads is indispensable. Onesolution is to elaborate a load generation system based on master/slave architecture.

Finite element modeling of straightening of thin-walled seamless tubes of austenitic stainless steel

During this thesis work a coupled thermo-mechanical finite element model (FEM) was builtto simulate hot rolling in the blooming mill at Sandvik Materials Technology (SMT) inSandviken. The blooming mill is the first in a long line of processes that continuously or ingotcast ingots are subjected to before becoming finished products. The aim of this thesis work was twofold. The first was to create a parameterized finiteelement (FE) model of the blooming mill. The commercial FE software package MSCMarc/Mentat was used to create this model and the programing language Python was used toparameterize it. Second, two different pass schedules (A and B) were studied and comparedusing the model. The two pass series were evaluated with focus on their ability to healcentreline porosity, i.e. to close voids in the centre of the ingot. This evaluation was made by studying the hydrostatic stress (σm), the von Mises stress (σeq)and the plastic strain (εp) in the centre of the ingot. From these parameters the stress triaxiality(Tx) and the hydrostatic integration parameter (Gm) were calculated for each pass in bothseries using two different transportation times (30 and 150 s) from the furnace. The relationbetween Gm and an analytical parameter (Δ) was also studied. This parameter is the ratiobetween the mean height of the ingot and the contact length between the rolls and the ingot,which is useful as a rule of thumb to determine the homogeneity or penetration of strain for aspecific pass. The pass series designed with fewer passes (B), many with greater reduction, was shown toachieve better void closure theoretically. It was also shown that a temperature gradient, whichis the result of a longer holding time between the furnace and the blooming mill leads toimproved void closure.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.