O presente trabalho propõe um modelo de operação sustentado por um sistema de suporte à decisão para operar a distribuição de água em tempo real atendendo a condições / restrições hidráulicas com o mínimo custo de energia elétrica. O atendimento às condições / restrições hidráulicas são avaliadas por um modelo simulador hidráulico previamente montado e calibrado. O conjunto de resultados avaliados pelo modelo de simulação hidráulica é analisado por um modelo de otimização proposto com solução de programação linear. As condições de operação em tempo real geram a necessidade de alimentação de informações operacionais automáticas a qualquer momento e com curto espaço de tempo – menor que horário. Para uma operação otimizada, previamente analisada por um modelo de simulação hidráulica cria uma condição critérios para uma previsão do consumo a ser atendido nas próximas horas. Um refinamento desses critérios são utilizados em um modelo de previsão de demanda de água que prevê e checa seus resultados de forma dinâmica. O modelo de operação proposto cria uma interface entre todos esses sistemas. Essa interface é testada e avaliada a partir de um estudo de caso aplicado no Sistema Adutor Metropolitano de São Paulo. A eficiência do modelo de operação proposto é apresentada tendo como resultado uma redução no custo de energia elétrica. / This assignment considers an operation model supported by decision support systems to operates the water supply systems in real time, considering the hydraulical conditions while achieving some performance goals, in this case, reducing electricity costs (minimization of pumping costs) the attempt of the hydraulic constraints are evaluated by an hydraulical simulator previously calibrated. The set of results are analyzed by an optimization model which uses a linear programming. The operation conditions in real time requires automatic feeding operational information shortly at any time (less than an hour) for an optimized operation, previously analyzed by a hydraulic simulation model with creates condition criteria of consumption within following hours. These criteria are refined according to a demand prediction model that dynamically previews and checks the consumption results. This proposed model creates an interface between all these systems. This interface is tested and evaluated according to a study of the São Paulo´s metropolitan area, “Sistema Alto Tietê". The efficiency of this proposed model is presented having reductions in the electric energy costs.

O presente trabalho propõe um modelo de operação sustentado por um sistema de suporte à decisão para operar a distribuição de água em tempo real atendendo a condições / restrições hidráulicas com o mínimo custo de energia elétrica. O atendimento às condições / restrições hidráulicas são avaliadas por um modelo simulador hidráulico previamente montado e calibrado. O conjunto de resultados avaliados pelo modelo de simulação hidráulica é analisado por um modelo de otimização proposto com solução de programação linear. As condições de operação em tempo real geram a necessidade de alimentação de informações operacionais automáticas a qualquer momento e com curto espaço de tempo – menor que horário. Para uma operação otimizada, previamente analisada por um modelo de simulação hidráulica cria uma condição critérios para uma previsão do consumo a ser atendido nas próximas horas. Um refinamento desses critérios são utilizados em um modelo de previsão de demanda de água que prevê e checa seus resultados de forma dinâmica. O modelo de operação proposto cria uma interface entre todos esses sistemas. Essa interface é testada e avaliada a partir de um estudo de caso aplicado no Sistema Adutor Metropolitano de São Paulo. A eficiência do modelo de operação proposto é apresentada tendo como resultado uma redução no custo de energia elétrica. / This assignment considers an operation model supported by decision support systems to operates the water supply systems in real time, considering the hydraulical conditions while achieving some performance goals, in this case, reducing electricity costs (minimization of pumping costs) the attempt of the hydraulic constraints are evaluated by an hydraulical simulator previously calibrated. The set of results are analyzed by an optimization model which uses a linear programming. The operation conditions in real time requires automatic feeding operational information shortly at any time (less than an hour) for an optimized operation, previously analyzed by a hydraulic simulation model with creates condition criteria of consumption within following hours. These criteria are refined according to a demand prediction model that dynamically previews and checks the consumption results. This proposed model creates an interface between all these systems. This interface is tested and evaluated according to a study of the São Paulo´s metropolitan area, “Sistema Alto Tietê”. The efficiency of this proposed model is presented having reductions in the electric energy costs.

Two-way left-turn lane (TWLTL) is one of the common median treatments on the roadway. It is found that a number of crashes reported in Florida State are related to TWLTLs. This research focused on evaluating the effect of TWLTLs on these crashes by using the statistical crash prediction model that can estimate the expected number of crashes on TWLTLs. The crash database for analysis was extracted from the Florida Traffic Crash Database based on the TWLTL section list provided by FDOT and combined with some traffic characteristics. It consisted of totally 1688 sample sections within a three-year period from 1996 to 1998. Based on the crash database, distribution fittings for Poisson, Negative Binomial and Lognormal regression were conducted for average number of crashes. According to the results, statistical crash predictive model was developed to estimate the average number of crashes. Negative Binomial regression was applied with four variables, ADT, access density, posted speed and number of lanes for the TWLTL sections. The regression parameters were estimated by using maximum likelihood method with statistical software. The findings of the analysis indicated that all of the variables adopted in the predictive model significantly affect the occurrence of crashes. And the average number of crashes increases with the increase of ADT, access density and number of lanes, while with the decrease of posted speed. After that, the goodness-of-fit of developed model was performed in term of Pearson's R-square and likelihood ratio index. The results showed that the Negative Binomial regression model could explain the relationship between the variables and the crash occurrence In the third part, an approach was developed to identify the TWLTL sections with safety concern. For an undivided roadway, the approach can be carried out to judge if the TWLTL is appropriate to be selected as the median treatment. During the process, the whole database was divided into six categories according to the posted speed and number of lanes. By adopting the selected percentile value from the distribution of average number of crashes for each category in the predictive model, the critical ADT values according to specific access density, number of lane and posted speed level for each category were calculated and tabulated. With the comparison of the actual ADT value and the critical ADT value, if the actual ADT is higher than the critical value, the TWLTL section is determined as the critical section, which means the TWLTL is not appropriate to be selected as the median treatment in this roadway section.

Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind power forecasting is an important issue for deploying higher wind power penetrations on power grids. The existing work on power output forecasting for wind farms has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed using spatial modeling. In this dissertation, we propose a new approach to spatial prediction of wind farm outputs for grid integration based on Kriging techniques. First, we investigate the characteristics of wind farm outputs. Wind power is variable, uncontrollable, and uncertain compared to traditional generating resources. In order to understand the characteristics of wind power outputs, we study the variability of wind farm outputs using correlation analysis. We estimate the Power Spectrum Density (PSD) from empirical data. Following Apt[1], we classify the estimated PSD into four frequency ranges having different slopes. We subsequently focus on phenomena relating to the slope of the estimated PSD at a low frequency range because our spatial prediction is based on the period over daily to monthly timescales. Since most of the energy is in the lower frequency components (the second, third, and fourth slope regions have much lower spectral density than the first), the conclusion is that the dominant issues regarding energy will be captured by the low frequency behavior. Consequently, most of the issues regarding energy (at least at longer timescales) will be captured by the first slope, since relatively little energy is in the other regions. We propose the slope estimation model of new wind farm production. When the existing wind farms are highly correlated and the slope of each wind farm is estimated at a low frequency range, we can predict the slope with low frequency components of a new wind farm through the proposed spatial interpolation techniques. Second, we propose a new approach, based on Kriging techniques, to predict wind farm outputs. We introduce Kriging techniques for spatial prediction, modeling semivariograms for spatial correlation, and mathematical formulation of the Kriging system. The aim of spatial modeling is to calculate a target value of wind production at unmeasured or new locations based on the existing values that have already been measured at locations considering the spatial correlation relationship between measured values. We propose the multivariate spatial approach based on Co-Kriging to consider multiple variables for better prediction. Co-Kriging is a multivariate spatial technique to predict spatially distributed and correlated variables and it adds auxiliary variables to a single variable of interest at unmeasured locations. Third, we develop the Augmented Kriging-based Model, to predict power outputs at unmeasured or new wind farms that are geographically distributed in a region. The proposed spatial prediction model consists of three stages: collection of wind farm data for spatial analysis, performance of spatial analysis and prediction, and verification of the predicted wind farm outputs. The proposed spatial prediction model provides the univariate prediction based on Universal Kriging techniques and the multivariate prediction based on Universal and Co-Kriging techniques. The proposed multivariate prediction model considers multiple variables: the measured wind power output as a primary variable and the type or hub height of wind turbines, or the slope with low frequency components as a secondary variable. The multivariate problem is solved by Co-Kriging techniques. In addition, we propose $p$ indicator as a categorical variable considering the data configuration of wind farms connected to electrical power grids. Although the interconnection voltage does not influence the wind regime, it does affect transmission system issues such as the level of curtailments, which, in turn, affect power production. Voltage level is therefore used as a proxy to the effect of the transmission system on power output. The Augmented Kriging-based Model (AKM) is implemented in the R system environments and the latest Gstat library is used for the implementation of the AKM. Fourth, we demonstrate the performance of the proposed spatial prediction model based on Kriging techniques in the context of the McCamey and Central areas of ERCOT CREZ. Spatial prediction of ERCOT wind farms is performed in daily, weekly, and monthly time scales for January to September 2009. These time scales all correspond to the lowest frequency range of the estimated PSD. We propose a merit function to provide practical information to find optimal wind farm sites based on spatial wind farm output prediction, including correlation with other wind farms. Our approach can predict what will happen when a new wind farm is added at various locations. Fifth, we propose the Augmented Sequential Outage Checker (ASOC) as a possible approach to study the transmission system, including grid integration of wind-powered generation resources. We analyze cascading outages caused by a combination of thermal overloads, low voltages, and under-frequencies following an initial disturbance using the ASOC. / text

Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, forecasting the amount and timing of snowmelt runoff especially in the Euphrates Basin, where large dams are located, is an important task in order to use the water resources of the country in an optimum manner. The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km2 on the headwaters of Euphrates River for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in Upper Euphrates Basin operating in real-time. Since ground based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of MODIS optical satellite. Automatic model parameter estimation methods, GML and SCE_UA, are utilized to calibrate the HBV model parameters with a multi-objective criteria using runoff as well as snow covered area to ensure the internal validity of the model and to generate a Pareto front. Model simulations show that the choice of study years and timing of satellite images affect the results and further suggest that more study catchments and years should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from numerical weather prediction models of ECMWF and MM5 for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting using numerical weather prediction models in order to prevent or at least take precautions before flooding ahead of time.

As vias pavimentadas se deterioram com o passar do tempo, por ação das cargas do tráfego e das intempéries. Por isso necessitam de intervenções (atividades de Manutenção e Reabilitação - M&R). No Brasil é mais comum que sejam realizadas apenas operações tapa-buracos e recapeamentos. São soluções sem eficiência técnica ou econômica. Uma alternativa mais racional e já em uso em países desenvolvidos é um Sistema de Gerência de Pavimentos Urbanos - SGPU. Os objetivos desta tese são apresentar uma propostas para avaliação da condição de vias urbanas com base na identificação dos defeitos da superfície do revestimento, um modelo de previsão de desempenho de vias urbanas com base em séries históricas obtidas no Município de São Carlos e uma análise econômica com a simulação de diferentes cenários para diferentes opções de M&R. Ainda como objetivos desta tese estão a apresentação das vantagens de utilizar um Sistema de Informações Geográficas - SIG, em uso no SGPU do Distrito Federal. Com base nos resultados encontrados, pode-se inferir que a avaliação de campo pode ser mais eficiente se forem considerados menos defeitos e os critérios de severidade e extensão forem fixados, com menos critérios subjetivos do avaliador. Considerando-se as estratégias de M&R adotadas pela Prefeitura Municipal de São Carlos e, ainda, de acordo com as avaliações de campo, pôde-se verificar que os pavimentos necessitam de Reforço Estrutural após 6 anos da sua construção. Os custos de manutenção, ao longo do tempo, são influenciados pela escolha da M&R a ser aplicada na preservação das vias e, também, pela época em que são realizadas, sendo as manutenções preventivas menos onerosas. Nos Estados Unidos, por exemplo, particularmente na cidade de San Antonio, Texas, são realizadas manutenções preventivas e, nas corretivas, há rigoroso controle de qualidade, o que não se verifica nas cidades brasileiras. Um SGPU possui critérios de análise de dados que, aliados ao Sistema de Informações Geográficas - SIG, garantem clareza na apresentação dos resultados, tanto em mapas temáticos quanto em gráficos e planilhas. Os relatórios no padrão SGPU possibilitam solicitar recursos federais e internacionais para manutenção viária, como o Programa Asfalto Novo do DF, que obteve, no ano de 2013, R$565 milhões para a pavimentação urbana. Em simulação de diferentes cenários de M&R, pôde-se concluir que a opção por tapa-buracos e recapeamentos é mais de duas vezes mais onerosa e mantém o pavimento em condição inferior ao que é possível com base nas técnicas de SGPU. Portanto, um SGPU proporciona benefícios socioeconômicos para toda a sociedade, com vantagens no médio e longo prazo e, ainda, mantém as vias em melhor condição. / The paved roads deteriorate over time, due to traffic loads and bad weather. Therefore, they need interventions (Maintenance and Rehabilitation activities - M & R). In Brazil, it is more common to carry out only hole-hole operations and resurfacing. They are solutions without technical or economic efficiency. A more rational alternative already in use in developed countries is an Urban Pavement Management System - UPMS. In this thesis proposals are presented for the evaluation of urban road conditions based on defects of the surface of the coating, a performance prediction model and an economic analysis with the simulation of different scenarios for different M & R options. Data were analyzed from the Municipality of São Carlos, in the State of São Paulo and the Distrito Federal - DF. Based on the results, it can be inferred that the field evaluation can be more efficient if less defects are considered and the criteria of severity and extension are fixed, with less subjectivity on the part of the evaluator. Considering the M & R strategies adopted by the municipal government of São Carlos and also, according to the field evaluations, it was possible to verify that the pavements have a useful life of 6 years. The maintenance costs, over time, are strongly influenced by the choice of M & R to be applied in the preservation of roads and also by the time in which they are carried out. In the United States, for example, preventive maintenance is carried out and, in the correctives, there is strict quality control, which is not the case in Brazilian cities. An UPMS has data analysis criteria that, together with the Geographic Information System - GIS, guarantee clarity in the presentation of results, both in thematic maps and in charts and spreadsheets. The reports in the UPMS standard make it possible to request federal and international resources for road maintenance, such as the DF Asphalt New Program, which obtained R$ 565 million for urban paving. In the simulation of different M & R scenarios, it was concluded that the option to cover holes and resurfacing is more than twice as expensive and keeps the pavement in a lower condition than is possible based on SGPU techniques. Therefore, an SGPU confers socio-economic benefits for the whole society, with advantages in the medium and long term, and also keeps the roads in better condition.

This master thesis is part of a big project at Siemens Industrial Turbomachinery (SIT) in Finspång aimed to use the operation experience available at SIT to predict the state of the gas turbines in general and some mechanical components in particular. The objective of the thesis is to continue the development of a prediction model based on experience data for estimations of a components lifetime. In a previous master thesis by Alessandro Olivi statistical analysis of environmental attributes effect on the expected lifetime of components in a gas turbine was performed. Olivi’s thesis constitutes the starting point on which to keep building to create a reliable prediction model. In this thesis extensive validation tests have been performed in order to further quantify the reliability of the model. Investigations aimed towards finding ways to further develop and improve the prediction model are carried out. The relevant new findings are applied to the model and analysis concerning improvements in the prediction accuracy is carried out. It was revealed that the model is able to make accurate predictions for most of the validation points for each failure mode, but more research is needed to obtain a completely reliable prediction model.

Objectives: Cardiovascular disease is the leading cause of mortality and morbidity in the developed world. Surgery can improve prognosis and relieve symptoms. Risk prediction models are increasingly being used to inform clinicians and patients about the risks of surgery, to facilitate clinical decision making and for the risk-adjustment of surgical outcome data. The importance of risk prediction models in cardiovascular surgery has been highlighted by the publication of cardiovascular surgery outcome data and the need for risk-adjustment. The overall objective of this thesis is to advance risk prediction modelling in cardiovascular surgery with a focus on the development of models for elective AAA repair and assessment of models for cardiac surgery. Methods: Three large clinical databases (two elective AAA repair and one cardiac surgery) were utilised. Each database was cleaned prior to analysis. Logistic regression was used to develop both regional and national risk prediction models for mortality following elective AAA repair. A regional model to identify the risk of developing renal failure following elective AAA repair was also developed. The performance of a widely used cardiac surgery risk prediction model (the logistic EuroSCORE) over time was evaluated using a national cardiac database. In addition an updated model version (EuroSCORE II) was validated and both models’ performance in emergency cardiac surgery was evaluated. Results: Regional risk models for mortality following elective AAA repair (VGNW model) and a model to predict post-operative renal failure were developed. Validation of the model for mortality using a national dataset demonstrated good performance compared to other available risk models. To improve generalisability a national model (the BAR score) with better discriminatory ability was developed. In a prospective validation of both models using regional data, the BAR score demonstrated excellent discrimination overall and good discrimination in procedural sub-groups. The EuroSCORE was found to have lost calibration over time due to a fall in observed mortality despite an increase in the predicted mortality of patients undergoing cardiac surgery. The EuroSCORE II demonstrated good performance for contemporary cardiac surgery. Both EuroSCORE models demonstrated inadequate performance for emergency cardiac surgery. Conclusions: Risk prediction models play an important role in cardiovascular surgery. Two accurate risk prediction models for mortality following elective AAA repair have been developed and can be used to risk-adjust surgical outcomes and facilitate clinical decision making. As surgical practice changes over time risk prediction models may lose accuracy which has implications for their application. Cardiac risk models may not be sufficiently accurate for high-risk patient groups such as those undergoing emergency surgery and specific emergency models may be required. Continuing research into new risk factors and model outcomes is needed and risk prediction models may play an increasing role in clinical decision making in the future.

The thesis is focused at verifying the functionality of adaptive control of fictive water reservoir. Flow rates are predicted using zonal prediction model. Fictive reservoirs located on the same flow in the other profiles. Functionality is verified by the control in 2000 – 2006. Next functionality is verified by the control in selected years.

The diploma thesis is focused on creating a business plan for establishment a new restaurant that will dedicate itself to serve slovakian, hungarian and czech cuisine. Part of the thesis is to put together sales and costs prediction model in a pertinent subbranch. It also contains internal and external environment analysis, financial and business continuity plan.