M.Ing. / The aim of this research was to investigate the use of stream processing on the graphics processing unit (GPU) and to apply it into the hydraulic modelling of a water distribution system. The stream processing model was programmed and compared to the programming on the conventional, sequential programming platform, namely the CPU. The use of the GPU as a parallel processor has been widely adopted in many different non-graphic applications and the benefits of implementing parallel processing in these fields have been significant. They have the capacity to perform from billions to trillions of floating-point operations per second using programmable shader programs. These great advances seen in the GPU architecture have been driven by the gaming industry and a demand for better gaming experiences. The computational performance of the GPU is much greater than the computational capability of CPU processors. Hydraulic modelling of water distribution systems has become vital to the construction of new water distribution systems. This is because water distribution networks are very complex and are nonlinear in nature. Further, modelling is able to prevent and anticipate problems in a system without physically building the system. The hydraulic model that was used was the Gradient Method, which is the hydraulic model used in the EPANET software package. The Gradient Method produces a linear system which is both positive-definite and symmetric. The Cholesky method is currently being used in the EPANET algorithm in order to solve the linear equations produced by the Gradient Method. Thus, a linear solution method had to be selected for the use in both parallel processing on the GPU and as a hydraulic network solver. The Conjugate Gradient algorithm was selected as an ideal algorithm as it works well with the hydraulic solver and could be converted into a parallel algorithm on the GPU. The Conjugate Gradient Method is one of the best-known iterative techniques used in the solution of sparse symmetric positive definite linear systems. The Conjugate Gradient Method was constructed both in the sequential programming model and the stream processing model, using the CPU and the GPU respectively on two different computer systems. The Cholesky method was also programmed in the sequential programming model for both of the computer systems. A comparison was made between the Cholesky and the Conjugate Gradient Methods in order to evaluate the two methods relative to each other. The findings in this study have shown that stream processing on the GPU can be used in the parallel GPU architecture in order to perform general-purpose algorithms. The results further affirmed that iterative linear solution methods should only be used for large linear systems.

Thesis (Ph. D.)--University of Wisconsin--Milwaukee, 2000. / Major Professor: David C. Yu. Includes bibliographical references.

We document how sampling from a conditional Student's t distribution is implemented in stochvol. Moreover, a simple example using EUR/CHF exchange rates illustrates how to use the augmented sampler. We conclude with results and implications. (author's abstract)

published_or_final_version / Electrical Engineering / Master / Master of Philosophy

No description available.

M. Tech. Electrical Engineering. / This dissertation presents an effective method to determine current/voltage harmonic emission levels caused by various devices in the network. The method is implemented based on the existing power monitoring devices installed in power generation plants. The Digital Fault Recorders (DFRs) are used in this work as the monitoring devices. Due to the advancement of data processing and communication capabilities of such devices, a large amount of information generated by DFRs is concentrated in the utilitys head office. This data is archived for permanent storage and analysis. The presented work aims to assist the power utilities in harmonic invigilation problems based on DFRs.

There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environmental variables (mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature, mean maximum summer temperature, mean minimum winter temperature, mean daily radiation, mean daily summer radiation, mean daily June radiation, lithology and topography) were used to model the distribution of each of the plant species in the study area. Model predictive performance was measured as the area under the curve of a receiver operating characteristic (ROC) plot. The initial predictive performance of logistic regression models and generalised additive models (GAMs) using unrestricted, temperature restricted, major gradient restricted and climatic domain restricted data gave results that were contrary to current practice in species distribution modelling. Although climatic domain restriction has been used in other studies, it was found to produce models that had the lowest predictive performance. The performance of domain restricted models was significantly (p = 0.007) inferior to the performance of major gradient restricted models when the predictions of the models were confined to the climatic domain of the species. Furthermore, the effect of data restriction on model predictive performance was found to depend on the species as shown by a significant interaction between species and data restriction treatment (p = 0.013). As found in other studies however, the predictive performance of GAM was significantly (p = 0.003) better than that of logistic regression. The superiority of GAM over logistic regression was unaffected by different data restriction regimes and was not significantly different within species. The logistic regression models used in the initial performance comparisons were based on models developed using the forward selection procedure in a rigorous-fitting model-building framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding framework involved testing for the significant reduction in model deviance (p = 0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter estimates and their standard errors were inspected because large estimates and/or standard errors are an indication of model degradation from overfilling or effecls such as mullicollinearily. For additional variables to be included in a model, they had to contribule significantly (p = 0.025) to the model prediclive performance. An attempt to improve the performance of species distribution models using logistic regression models in a rigorousfitting model-building framework, the backward elimination procedure was employed for model selection, bul it yielded models with reduced performance. A liberal-filling model-building framework that used significant model deviance reduction at p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the major criterion for variable selection was employed for the development of logistic regression models using the forward selection and backward elimination procedures. Liberal filling yielded models that had a significantly greater predictive performance than the rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the former models was comparable to that of GAM and classification tree models (CTMs). The low significance liberal-filling models had a much larger number of variables than the high significance liberal-fitting models, but with no significant increase in predictive performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size of a full CTM for a given species. The 10-fold cross-validated model deviance for the subtrees was plotted against the size of the subtree as a means of selecting an appropriate tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance. Species geographical range and species prevalence within the study area were used to categorise the tree species into different distributional forms. These were then used, to compare the effect of plant species rarity on the predictive performance of logistic regression models, GAMs and CTMs. The distributional forms included restricted and rare (RR) species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC) species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides), widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC) species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were significant differences (p = 0.076) in predictive performance among the distributional forms for the logistic regression and GAM. The predictive performance for the WR distributional form was significantly lower than the performance for the other plant species distributional forms. The predictive performance for the RC and RR distributional forms was significantly greater than the performance for the WC distributional form. The trend in model predictive performance among plant species distributional forms was similar for CTMs except that the CTMs had poor predictive performance for the RR distributional form. This study shows the importance of data restriction to model predictive performance with major gradient data restriction being recommended for consistently high performance. Given the appropriate model selection strategy, logistic regression, GAM and CTM have similar predictive performance. Logistic regression requires a high significance liberal-fitting strategy to both maximise its predictive performance and to select a relatively small model that could be useful for framing future ecological hypotheses about the distribution of individual plant species. The results for the modelling of plant species for conservation purposes were encouraging since logistic regression and GAM performed well for the restricted and rare species, which are usually of greater conservation concern.

Thesis (Ph.D. in Analytic Health Sciences, Department of Preventive Medicine and Biometrics) -- University of Colorado Denver, 2007. / Typescript. Includes bibliographical references (leaves 75-77). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;

M.Com. (Informatics) / Critical Infrastructure is the term used to describe assets that are of utmost importance, or in other words, essential in the functioning of an environment. Societies depend on their critical infrastructure in order to maintain and continuously improve on their population’s standard of living. The creation of more self-sustainable methods of energy consumption and generation drives towards the creation of a better and more efficient evolution of the power grid critical infrastructure, named the smart grid. The introduction of the smart grid brought in a paradigm shift towards the practices used to manage the generation and distribution of electric power. The introduction of highly capable information systems to intrinsically work with current power grid technologies provided the ability to enhance economic and environmental efficiency of power systems. Although providing a wide variety of benefits, such information systems also created new points of vulnerabilities, which if exploited, place the smart grid at risk of disruptions. In order to address the security issues that occur at the application and data exchange level of smart grid information systems, the dissertation proposed the use of a security model to protect the smart grid. The Multi-Agent Smart Grid Security (MA-SGS) model is based on the use of multiple autonomous intelligent software agents which attempt to create operational stability and efficiency in the smart grid...

Many municipalities around Sweden use public access spatial data from Artportalen to track and fight invasive species, but anyone can upload data to Artportalen and not all of it becomes verified. They could potentially enhance their efforts by incorporating street view technology for more reliable data. One municipality that uses Artportalen data is Jönköping, which suffers from Reynoutria japonica. I randomly selected 50 areas around Jönköping with reports of R. japonica and went through them using Street View. The purpose of this study is to test if Street View can be used to improve the reliability of Artportalen data and if it can fully replace other methods to do so. I counted each individual of R. japonica I could identify and compared it to the number of reports in each area. Among the 50 areas only 26 were reachable in Google Street View and some plants were impossible to identify if they were R. japonica or not. When looking at the areas that could be reached it showed no difference between Reports in Artportalen and what was found in Street View if you included the unidentifiable plants. When excluding unidentified plants or when including areas that could not be reached it showed a significant difference. Using Google Street View to complement Artportalen data for R. japonica comes with benefits and limitations. Many areas were impossible to reach, but where accessible, it proved effective in identifying misreports and finding unreported plants without the need to visit the location. Street View data can enhance the reliability of distribution data used for monitoring invasive species without ever needing to travel, however it cannot fully replace other methods of enhancement and for the best reliability, a combination of Street View and on-site visits is necessary. / Många kommuner runt om i Sverige använder offentligt tillgängliga geodata från Artportalen för att spåra och bekämpa invasiva arter, men vem som helst kan ladda upp data till Artportalen och inte all data blir verifierad. Kommunerna skulle potentiellt kunna förbättra sina insatser genom att använda Street View teknologi för att få mer tillförlitliga data. En kommun som använder sig av data från Artportalen är Jönköping, som lider av den invasiva växten Reynoutria japonica. Syftet med den här rapporten är att testa om Street View kan användas för att komplimentera data från Artportalen och om det kan helt byta ut andra metoder att göra samma sak. Jag valde slumpmässigt ut 50 områden runt Jönköping med rapporter om R. japonica och gick igenom dem med Street View. Jag räknade varje individ av R. japonica jag kunde identifiera och jämförde det med antalet rapporter i varje område. Bland de 50 områdena var det bara 26 som kunde nås med Google Street View och vissa växter var omöjliga att identifiera om de var R. japonica eller inte. När man tittade på de områden som kunde nås visade det ingen skillnad i antal växter mellan rapporterna i Artportalen och vad som hittades i Street View om man inkluderade de oidentifierbara växterna. När man exkluderade oidentifierade växter eller när man inkluderade områden som inte kunde nås visade det en signifikant skillnad. Att använda Google Street View för att komplettera Artportalen-data för R. japonica har både fördelar och begränsningar. Många områden var omöjliga att nå, men där de var 2 tillgängliga visade det sig effektivt för att identifiera felrapporter och hitta orapporterade växter utan att behöva besöka platsen. Street View-data kan förbättra tillförlitligheten av distributionsdata som används för att övervaka invasiva arter utan att man någonsin behöver resa, men det kan inte fullt byta ut andra metoder att öka tillförlighet och för bästa resultat är en kombination av Street View och platsbesök nödvändig.