Dissertations / Theses on the topic 'Predictive mathematical model'
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Lee, Kai-Tien. "Predictive model for plume opacity." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53886.
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In recent years, as control systems for boiler emissions have been upgraded, some utility sources have experienced increased plume opacity. Cases of plume opacity exceeding in-stack opacity are due to 1) the aerosol formed by condensation of primary sulfuric acid and water vapor onto polydisperse plume particles and 2) the presence of fine particles which grow into the visual size range by heterogeneous condensation and coagulation processes as the plume is cooled and diluted by mixing with the ambient air.
In order to better understand the factors leading up to acid plume formation, a computer simulation model has been developed. This plume opacity model has been utilized to simulate sulfuric acid aerosol formation and growth. These processes result from homogeneous nucleation, condensation and coagulation which substantially increase the concentration of submicrometer sized aerosols. These phenomena bring about significant increases in plume opacity.
Theoretical relationships have been derived and transformed into 21 computer model to predict plume opacity at various downwind distances resulting from pulverized coal combustion operations. This model consists of relatively independent components-such as an optics module, a bimodal particle size distribution module, a polydisperse coagulation module, a vapor condensation and nucleation module and a plume dispersion module-which are linked together to relate specific flue gas emissions and meterological conditions to plume opacity.
This unique, near-stack, plume-opacity-model approach provides an excellent tool for understanding and dealing with such complex issues as:
• increasing plume opacity observed for emissions containing sulfuric acid aerosols,
• explaining the correlation between primary particle size distribution and light—scattering effects,
• predicting the opacity level resulting from combustion of various coal types,
• predicting control equipment effects on plume opacity.Ph. D.
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Buerger, Johannes Albert. "Fast model predictive control." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.
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This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.
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Yang, Xiaoke. "Fault-tolerant predictive control : a Gaussian process model based approach." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708784.
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Trapp, Donald R. "The Development of a Predictive Model of Pretrial Misconduct." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4574.
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The problem of jail overcrowding has forced corrections officials and jail administrators to examine ways in which to better manage available jail space. Pretrial release and detention policies have been a target of this examination as pretrial defendants typically account for 50% of a jail's population. Standards for pretrial release exist, but their administration varies by jurisdiction. The impact of jail overcrowding on pretrial release policies has been to decrease the time available to render a decision. Recent efforts to standardize pretrial release standards in Oregon have not addressed the issue of expediency. The current study examines pretrial misconduct (failure to appear in court and rearrest) with regard to information that is available to jail personnel and release office personnel at the time of arrest, with the specific intent to develop a predictive model of pretrial misconduct that will function as an initial risk assessment. Six hundred defendants arrested in Washington County, Oregon during 1991 served as subjects. The results indicated that 90.9% of all defendants arrested are released pending trial/ and that 22.7% of those released engaged in pretrial misconduct. The results of the loglinear model-building indicated that the variables prior failure-to-appears/ employment, and age were the best predictors of pretrial misconduct. The construction sample (n = 395) accurately predicted 94.5% of the observed pretrial misconduct compared to 90.7% for the validation sample (n = 150). The loglinear analysis yielded 16 typologies (based on the variables included in the model) by which defendants could be ranked as to their risk of pretrial misconduct. Spearman Rank Order coefficents for the construction and validation samples were .847 and .626 respectively. Data were also collected on detained subjects. A Chi-Square test using detained with released ?Ubjects by typology indicated that the categories are not independent (p < .01). Further examination indicated that the detained subjects did represent higher risks of pretrial misconduct as estimated by the typologies. The results also indicated that defendants currently on probation or parole were more likely to detained than other defendants. The results do not reject the assumptions by Sturz {1962), whose Manhattan Bail Project is the basis for pretrial release, that persons with strong ties to the community may pose the least risk of pretrial misconduct. The results also found sex and ethnic differences with regard to pretrial misconduct. The sex differences may have been confounded by age and crime type; however, the ethnic differences may reflect a systemic inability to communicate with Hispanic offenders.
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Rogalsky, Dennis Wayne. "Quantifying plant model parameter effects on controller performance /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9843.
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Wu, Sha. "Mathematical Model of Glucose-Insulin Metabolism and Model Predictive Glycemic Control for Critically Ill Patients Considering Time Variability of Insulin Sensitivity." Kyoto University, 2020. http://hdl.handle.net/2433/259047.
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Lakhanpal, Chetan. "Mathematical modelling of applied heat transfer in temperature sensitive packaging systems. Design, development and validation of a heat transfer model using lumped system approach that predicts the performance of cold chain packaging systems under dynamically changing environmental thermal conditions." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/5776.
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Development of temperature controlled packaging (TCP) systems involves a significant lead-time and cost as a result of the large number of tests that are carried out to understand system performance in different internal and external conditions.
This MPhil project aims at solving this problem through the development of a transient spreadsheet based model using lumped system approach that predicts the performance of packaging systems under a wide range of internal configurations and dynamically changing environmental thermal conditions.
Experimental tests are conducted with the aim of validating the predictive model. Testing includes monitoring system temperature in a wide range of internal configurations and external thermal environments.
A good comparison is seen between experimental and model predicted results; increasing the mass of the chilled phase change material (PCM) in a system reduces the damping in product performance thereby reducing the product fluctuations or amplitude of the product performance curve. Results show that the thermal mathematical model predicts duration to failure within an accuracy of +/- 15% for all conditions considered.
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Mirzaei, Hamid Reza. "Using variation in cattle growth to develop a predictive model of carcass quality /." Title page, table of contents and abstract only, 2004. http://web4.library.adelaide.edu.au/theses/09PH/09phm677.pdf.
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Zvaigzne, Anita Ilze. "Thermochemical investigations of crystalline solutes in non-electrolyte solutions: Mathematical representation of solubility data and the development of predictive solubility equations in systems with specific and non-specific interactions." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc28369/.
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Understanding the thermodynamic properties of multicomponent mixtures is of critical importance in many chemical and industrial applications. Experimental measurements become progressively difficult as the number of solution components increases -- producing the need for predictive models. Problems in development of predictive models arise if the mixture has one or more components that interact through molecular complexation or association. Experimental solubilities of anthracene and pyrene dissolved in binary systems containing one or more alcohols were measured in order to address this problem. Alcohols examined in this study were: 1-propanol, 2-propanol, 1-butanol, 2-butanol, 2-methyl-1-propanol, 3-methyl-1-butanol, and 1-octanol. In binary solvent mixtures containing only a single self-associating alcoholic solvent, the alkane cosolvents studied were: n-hexane, n-heptane, n-octane, 2,2,4-trimethylpentane, cyclohexane, methylcyclohexane, tert-butylcyclohexane. Predictive solubility equations were developed using mobile order theory. This approach differs from classical solution models by representing hydrogen bonding with a probability term rather than with expressions derived from stepwise equilibria or expressions to represent hypothetical solution aggregates. Results were compared with the predicted solubilities found from using expressions developed using the Kretschmer-Wiebe and Mecke-Kempter approaches for modeling associated solutions. It was found that the mobile order approach provided reasonably accurate predictions for the solute solubilities in the systems studied. The limitations and applications for mathematical methods of representing experimental isothermal solubility data were also studied for 72 systems. Two possible descriptive forms for this mathematical representation were suggested based on the various nearly ideal binary solvent (NIBS) and modified Wilson models.
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Eifert, Joseph D. "Predictive modeling of the aerobic growth of Staphylococcus aureus 196E using a nonlinear model and response surface analysis." Diss., Virginia Tech, 1994. http://hdl.handle.net/10919/27970.
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Pathogenic bacteria in foods are affected by several factors which may interact to enhance or inhibit microbial growth. Staphylococcus aureus 196E was inoculated into Brain Heart Infusion broth formulated with either 0.5, 4.5 or 8.5% NaCI, adjusted to pH 5.0, 6.0 or 7.0, and incubated aerobically at 12, 20 or 28°C. Mathematical models to predict the growth of S. aureus 196E were developed using a modified Gompertz function and response surface methodology. Each predictive equation required the estimation of only 23 parameters with a biological meaning. These models determined the significance of time, incubation temperature, sodium chloride (NaCI) concentration, and either pH or the logₑ of the undissociated acid concentration and any interactions on growth kinetics. Separate models were developed for the cases where pH was altered with either acetic acid, acetic acid plus sodium hydroxide, lactic acid and hydrochloric acid.
All models adequately predicted the log growth of S. aureus 196E. Several interactive relationships between the independent variables upon population growth were significant. Predicted responses to multiple factor interactions were displayed with three-dimensional and contour plots. One model developed from a smaller subset of the growth data demonstrated that models could be produced with much less data collection. Generally, predictions of growth showed that acetic acid was more inhibitory to growth than lactic and hydrochloric acids. Furthermore, predicted and observed growth was slower or reduced when the undissociated acetic acid concentration was elevated at a specific pH.
This methodology can provide important information to food scientists about the growth kinetics of microorganisms and prediction ranges or confidence intervals for growth parameters. Consequently, the effects of food formulations and storage conditions on the growth kinetics of foodborne pathogens or spoilage microorganisms could be predicted.Ph. D.
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Shi, Zhenzhen. "A MARKOV DECISION PROCESS EMBEDDED WITH PREDICTIVE MODELING: A MODELING APPROACH FROM SYSTEM DYNAMICS MATHEMATICAL MODELS, AGENT-BASED MODELS TO A CLINICAL DECISION MAKING." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/20578.
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Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems Engineering
David H. Ben-Arieh
Chih-Hang Wu
Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis. The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis. With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis. The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering.
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Zhong, Yu Mechanical & Manufacturing Engineering Faculty of Engineering UNSW. "A study of the cutting performance in multipass abrasive waterjet machining of alumina ceramics with controlled nozzle oscillation." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2008. http://handle.unsw.edu.au/1959.4/41216.
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An experimental investigation has been undertaken to study the depth of cut in multipass abrasive waterjet (AWJ) cutting of an 87% alumina ceramic with controlled nozzle oscillation. The experimental data have been statistically analysed to study the trends of the depth of cut with respect to the process parameters. It has been found that multipass cutting with controlled nozzle oscillation can significantly increase the depth of cut. Within the same cutting time and using the same cutting parameters other than the jet traverse speed, it has been found that multipass cutting with nozzle oscillation can increase the depth of cut by an average of 74.6% as compared to single pass cutting without nozzle oscillation. Furthermore, a multipass cutting with higher nozzle traverse speeds can achieve a larger depth of cut than a single pass cutting at a low traverse speed within the same cutting time. A recommendation has been made for the selection of appropriate process parameters for multipass cutting with nozzle oscillation. In order to estimate the depth of cut on a mathematical basis, predictive models for the depth of cut in multipass cutting with and without nozzle oscillation have been developed using a dimensional analysis technique. The model development starts with the models for single pass cutting which are then extended to multipass cutting where considerations are given to the change of the actual standoff distance after each pass and the variation of kerf width. These predictive models has been numerically studied for their plausibility by assessing their predicted trends with respect to the various process variables, and verified qualitatively and quantitatively based on the experimental data. The model assessment reveals that the developed models correlate very well with the experimental results and can give adequate predictions of this cutting performance measure in process planning.
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Alvarado, Christiam Segundo Morales. "Estudo e implementação de métodos de validação de modelos matemáticos aplicados no desenvolvimento de sistemas de controle de processos industriais." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-05092017-092437/.
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A validação de modelos lineares é uma etapa importante em um projeto de Identificação de Sistemas, pois a escolha correta do modelo para representar a maior parte da dinâmica do processo, dentro de um número finito de técnicas de identificação e em torno de um ponto de operação, permite o sucesso no desenvolvimento de controladores preditivos e de controladores robustos. Por tal razão, o objetivo principal desta Tese é o desenvolvimento de um método de validação de modelos lineares, tendo como ferramentas de avaliação os métodos estatísticos, avaliações dinâmicas e análise da robustez do modelo. O componente principal do sistema de validação de modelos lineares proposto é o desenvolvimento de um sistema fuzzy para análise dos resultados obtidos pelas ferramentas utilizadas na etapa de validação. O projeto de Identificação de Sistemas é baseado em dados reais de operação de uma Planta-Piloto de Neutralização de pH, localizada no Laboratório de Controle de Processos Industriais da Escola Politécnica da USP. Para verificar o resultado da validação, todos os modelos são testados em um controlador preditivo do tipo QDMC (Quadratic Dynamic Matrix Control) para seguir uma trajetória de referência. Os critérios utilizados para avaliar o desempenho do controlador QDMC, para cada modelo utilizado, foram a velocidade de resposta do controlador e o índice da mínima variabilidade da variável de processo. Os resultados mostram que a confiabilidade do sistema de validação projetado para malhas com baixa e alta não-linearidade em um processo real, foram de 85,71% e 50%, respectivamente, com relação aos índices de desempenho obtidos pelo controlador QDMC.Linear model validation is the most important stage in System Identification Project because, the model correct selection to represent the most of process dynamic allows the success in the development of predictive and robust controllers, within identification technique finite number and around the operation point. For this reason, the development of linear model validation methods is the main objective in this Thesis, taking as a tools of assessing the statistical, dynamic and robustness methods. Fuzzy system is the main component of model linear validation system proposed to analyze the results obtained by the tools used in validation stage. System Identification project is performed through operation real data of a pH neutralization pilot plant, located at the Industrial Process Control Laboratory, IPCL, of the Escola Politécnica of the University of São Paulo, Brazil. In order to verify the validation results, all modes are used in QDMC type predictive controller, to follow a set point tracking. The criterions used to assess the QDMC controller performance were the speed response and the process variable minimum variance index, for each model used. The results show that the validation system reliability were 85.71% and 50% projected for low and high non-linearity in a real process, respectively, linking to the performance indexes obtained by the QDMC controller.
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li, yiwen. "Predicting Hearing Loss Using Auditory Steady-State Responses." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/84.
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Auditory Steady-State Response (ASSR) is a promising tool for detecting hearing loss. In this project, we analyzed hearing threshold data obtained from two ASSR methods and a gold standard, pure tone audiometry, applied to both normal and hearing-impaired subjects. We constructed a repeated measures linear model to identify factors that show significant differences in the mean response. The analysis shows that there are significant differences due to hearing status (normal or impaired) and ASSR method, and that there is a significant interaction between hearing status and test signal frequency. The second task of this project was to predict the PTA threshold (gold standard) from the ASSR-A and ASSR-B thresholds separately at each frequency, in order to measure how accurate the ASSR measurements are and to obtain a ¡°correction function¡± to correct the bias in the ASSR measurements. We used two approaches. In the first, we modeled the relation of the PTA responses to the ASSR values for the two hearing status groups as a mixture model and tried two prediction methods. The mixture modeling was successful, but the predictions gave disappointing results. A second approach, using logistic regression to predict group membership based on ASSR value and then using those predictions to obtain a predictor of the PTA value, gave successful results.
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Saiyasombati, Penpan. "Mathematical model for predicting percutaneous absorption of fragrance raw materials." Cincinnati, Ohio : University of Cincinnati, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1061561348.
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Sun, Wei. "Mathematical Model for Predicting Trace Organic Compounds in Anaerobic Digesters." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197057.
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Wang, Hao. "Incremental sheet forming process : control and modelling." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a80370f5-2287-4c6b-b7a4-44f06211564f.
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Incremental Sheet Forming (ISF) is a progressive metal forming process, where the deformation occurs locally around the point of contact between a tool and the metal sheet. The final work-piece is formed cumulatively by the movements of the tool, which is usually attached to a CNC milling machine. The ISF process is dieless in nature and capable of producing different parts of geometries with a universal tool. The tooling cost of ISF can be as low as 5–10% compared to the conventional sheet metal forming processes. On the laboratory scale, the accuracy of the parts created by ISF is between ±1.5 mm and ±3mm. However, in order for ISF to be competitive with a stamping process, an accuracy of below ±1.0 mm and more realistically below ±0.2 mm would be needed. In this work, we first studied the ISF deformation process by a simplified phenomenal linear model and employed a predictive controller to obtain an optimised tool trajectory in the sense of minimising the geometrical deviations between the targeted shape and the shape made by the ISF process. The algorithm is implemented at a rig in Cambridge University and the experimental results demonstrate the ability of the model predictive controller (MPC) strategy. We can achieve the deviation errors around ±0.2 mm for a number of simple geometrical shapes with our controller. The limitations of the underlying linear model for a highly nonlinear problem lead us to study the ISF process by a physics based model. We use the elastoplastic constitutive relation to model the material law and the contact mechanics with Signorini’s type of boundary conditions to model the process, resulting in an infinite dimensional system described by a partial differential equation. We further developed the computational method to solve the proposed mathematical model by using an augmented Lagrangian method in function space and discretising by finite element method. The preliminary results demonstrate the possibility of using this model for optimal controller design.
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Chowdhury, Sohini Roy. "Mathematical models for prediction and optimal mitigation of epidemics." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3874.
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SAIYASOMBATI, PENPAN. "MATHEMATICAL MODEL FOR PREDICTING THE PERCUTANEOUS ABSORPTION OF FRANGRANCE RAW MATERIALS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1061561348.
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Mwale, Adolph Ntaja. "A mathematical model for predicting classification performance in wet fine screens." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/20122.
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Screening is a well-known classification process in the minerals processing industry. The process involves separation of fine particles from coarse particles based on size and is applicable to both dry and fine screening. Fine screening is normally carried out wet. Until recently, fine wet screening had been limited to relatively low throughput applications. Developments in the recent past have seen the evolution of fine screening to high capacity applications. It has found application in operations such as closed circuits with a mill in place of hydrocyclones. However, even though developments are increasing, there has been a process model developmental lag. A fine wet screen model that can be used for unit simulation purposes to predict screen performance outcomes or integration into other models to simulate and predict process performance is necessary. Most existing screen models are for dry and coarse screening applications. This thesis is aimed at developing a fine wet screen process model for predicting wet screening performance in the 45 - 150 μm range. Pilot plant testwork was conducted using a UG2-Chrome ore blend as feed.
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Paulus, Amanda. "A Model-Predictive-Control Based Smart-Grid Aggregator." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230958.
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Intermittent energy source usage, such as solar and wind power, is continuously increasing. Intermittent energy sources are highly dependent on prevailing weather conditions, resulting in stochastic electricity generation. The expected stochasticity in electricity generation will cause issues for the current power grid. Moreover, an expected issue for the Swedish power grid is higher peak loads. Thus, there is an emerging need for novel and smart power systems capable of shifting peak loads in the future electricity grid. Model Predictive Control (MPC) is a sophisticated control method that is suitable for smart-grid aggregators. Hence, MPC can be used to optimally control the efficiency of energy use in a smart grid and shift peak loads. The purpose of this thesis is to investigate optimal peak load-shifting and efficiency of electrical substation operation in a smart grid in Ramsjöåsen, Sweden, using an MPC based smart-grid aggregator. Furthermore, the purpose is also to contribute to the theoretical foundation for future peak load-shifting in smart grids. Within the thesis project a mathematical model for the smart grid in Ramsjöåsen is developed, which is then used to simulate different scenarios. The simulated results indicate that an MPC based smart-grid aggregator improves the performance of the smart grid in Ramsjöåsen, as regards to both peak load-shifting and efficiency of electrical substation operation.Användningen av intermittenta energikällor, såsom sol och vindkraft, ökar ständigt. Intermittenta energikällor är starkt beroende av rådande väderförhållanden, vilket resulterar i stokastisk elproduktion. Den förväntade stokasticiteten i elproduktion kommer att orsaka problem för det nuvarande elnätet. Dessutom förväntas högre toppbelastningar för det svenska elnätet. Således finns ett växande behov av nya och smarta kraftsystem som kan reducera toppbelastningar i det framtida elnätet. Model Predictive Control (MPC) är en sofistikerad styrningsmetod som är lämplig för smart-näts aggregatorer. Därav kan MPC användas för att optimalt styra effektivitet av energianvändning i ett smart nät och minska toppbelastningar. Syftet med detta examensarbete är att undersöka optimal reducering av toppbelastningar och drift-effektivitet av transformatorstationen i ett smart nät i Ramsjöåsen, Sverige, med hjälp av en MPC baserad smart-näts aggregator. Dessutom är syftet att bidra till den teoretiska grunden för framtida topplastskapning i smarta nät. Inom examensarbetsprojektet utvecklas en matematisk modell för smart nätet i Ramsjöåsen, som sedan används för att simulera olika scenarier. De simulerade resultaten indikerar att en MPC baserad smart-näts aggregator förbättrar smart nätets prestanda i Ramsjöåsen, vad gäller både topplastsreducering och drifteffektivitet av transformatorstationen.
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Orzechowska, J. E. "A mathematically reduced approach to predictive control of perishable inventory systems." Thesis, Coventry University, 2014. http://curve.coventry.ac.uk/open/items/df30d207-00e9-4fae-a86d-9fc2871f0539/1.
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The design and optimisation of inventory replenishment systems has already been exhaustively studied by the operational research community. Many classical mathematical methods and simulation techniques have been developed and introduced in the literature. However, what can be observed is the fact that in a real case scenario the lead-time, deterioration of goods and demand for product are likely to be time-varying and uncertain, which traditionally have not necessarily been reflected in the model formulations. Therefore, in response to the dynamical nature of inventory systems, the potential of algorithms based on control theory to reduce the undesirable influences of system uncertainties on inventory level stability, have been investigated /proposed. Consequently, the mapping of the inventory problem into the control theory domain, for cost-benefit inventory trade-off achievement has been realised. Although, the application of control theory in inventory optimisation appears to be beneficial, there are certain reasons why the approach has gained yet little attention among the operational research community. One reason is that it cannot be adopted easily by researchers who are unfamiliar with control theory and another is due to a communication gap which exists between the control theory and operational research communities. Prompted by these observations, the thesis presents a novel, systematic mathematical approach for finding the optimal order quantities. The proposed approach has been mathematically demonstrated to be equivalent in study-sate to model-based predictive control, which is one of the more well-established productive control techniques with industrial application today. The mathematically reduced approach attempts to bridge the identified gap to fulfil the lacking dual perceptions of both communities. It enables the straightforward benefits afforded by predictive control without the necessity to become familiarised with principles of control theory. The method is shown to be applicable for both perishable and non-perishable inventory. Although the novel technique was inspired by MPC and noticing the MPC patterns in the mathematical description, the resulting proposal is no longer MPC. It is in fact a minimum variance approach, or dear beat controller, with an incorporated Smith predictor. Therefore using the adjective ‘predictive’ in the title of the thesis refers to both, the inspiration of MPC and the predictive nature of the minimum variance controller to accommodate lead time, being incorporated within an inherent Smith predictor. The developed approach is considered to be transferable to other applications, where similar model formulations may be applicable.
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Gao, Zhiyuan, and Likai Qi. "Predicting Stock Price Index." Thesis, Halmstad University, Applied Mathematics and Physics (CAMP), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-3784.
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This study is based on three models, Markov model, Hidden Markov model and the Radial basis function neural network. A number of work has been done before about application of these three models to the stock market. Though, individual researchers have developed their own techniques to design and test the Radial basis function neural network. This paper aims to show the different ways and precision of applying these three models to predict price processes of the stock market. By comparing the same group of data, authors get different results. Based on Markov model, authors find a tendency of stock market in future and, the Hidden Markov model behaves better in the financial market. When the fluctuation of the stock price index is not drastic, the Radial basis function neural network has a nice prediction.
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Thomas, Kerry J. "Teaching Mathematical Modelling to Tomorrow's Mathematicians or, You too can make a million dollars predicting football results." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83131.
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Duclos, Gosselin Louis. "How Managers Can Use Predictive Analysis and Mathematical Models as Decision Making Tools." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/26771/26771.pdf.
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Friedbaum, Jesse Robert. "Model Predictive Linear Control with Successive Linearization." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7063.
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Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a system which guarantee local stability for this control algorithm.
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Huang, Yenwen. "Predictive equations for bolted connections." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/41995.
Full textAbstract:
A FORTRAN computer program applying the ultimate strength/instantaneous center
of rotation method was written to generate the eccentricity coefficients used for the
study of this project.
For single line bolted connections, the value of the eccentricity coefficient is determined
by several independent variables: NR (number of rows in the bolted connection), B
(distance between two adjacent bolts in a vertical column), Xo (horizontal distance from
centroid to applied load), and 0 (the load angle). From the relationships between the
eccentricity coefficient and the independent variables, it was observed that a mathematical
model of the eccentricity coefficient with respect to the independent variables is hard
to determine. Hence, statistical equations for predicting the eccentricity coefficients were
developed by using the Buckingham's PI-Theorem and regression analysis. The precision
of the statistical equations is discussed, and several ways to improve the precision are
presented in this paper.
Master of Science
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Thomas, Kerry J. "Teaching Mathematical Modelling to Tomorrow''s Mathematicians or, You too can make a million dollars predicting football results." Turning dreams into reality: transformations and paradigm shifts in mathematics education. - Grahamstown: Rhodes University, 2011. - S. 334 - 339, 2012. https://slub.qucosa.de/id/qucosa%3A1949.
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Bourg, Brandi Marie. "Evaluation of a mathematical model in predicting intake of growing and finishing cattle." Thesis, Texas A&M University, 2007. http://hdl.handle.net/1969.1/85777.
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The Cattle Value Discovery System (CVDS) was developed to predict growth and feed requirements of individual cattle fed in groups based on animal, diet, and environment information (Tedeschi et al., 2006). Evaluations of the CVDS using several databases of finishing cattle were conducted to determine the accuracy and precision of the model in predicted dry matter required (DMR) of pen-fed cattle. As well, the sensitivity of the model's predictions to deviations from actual ration metabolizable energy (ME) value was conducted. A meta-analysis of growing and finishing steers evaluated to model's accuracy in predicting DMR of individually fed steers, and the relationships between several model-predicted variables and actual performance and efficiency measures. Results for the first CVDS model evaluation involving pen-fed Santa Gertrudis cattle fed finishing diets revealed that accurate predictions of DMR are possible. The average mean bias for both steers and heifers was 2.43%. The sensitivity analysis of dietary ME values revealed that the model tends to consistently over- and under-predict DMR when the ME values are under- and over-estimated, respectively. However the ranking of pens was not affected by this mis-estimation of diet ME. In the second evaluations, both methods (mean body weight; MBW, dynamic iterative model; DIM) of CVDS were highly accurate and precise in allocating feed to pens of steers fed diverse types of diets and environmental conditions, with both models having a mean bias under 4%. The DIM model was slightly more accurate than the MBW model in predicting DMR. An evaluation of sources of variation revealed that for both models a large portion of the error was random, indicating that further work is needed to account for this variation. The meta-analysis study revealed that the model was able to account for 64% and 67% of the variation in observed dry matter intake (DMI) for growing and finishing steers, respectively. The two model-predicted efficiency measures, the ratio of DMR to average daily gain (ADG) and predicted intake difference (PID), were strongly to moderately correlated with their observed efficiency counterparts. In growing and finishing steers, DMR: ADG was able to account for 76% and 64% of the variation in observed feed conversion ratio (FCR) in growing and finishing studies, respectively. Strong correlations were also found between residual feed intake (RFI) and PID, suggesting that there may also be some similarity on these two measurements.
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Stocco, Aaron B. "Predicting Democratic Peace (DP) Breakdown, a new game-theoretic model of democratic crisis behavior." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0023/MQ50575.pdf.
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Terblanche, Luther. "The prediction of flow through two-dimensional porous media." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/1722.
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Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006.When considering flow through porous media, different flow regimes may be identified. At very small Reynolds numbers the relation between the pressure gradient and the velocity of the fluid is linear. This flow regime ...
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Jones, Julie Elizabeth. "A series of mathematical models of the life-cycle of the nematode Ostertagia ostertagia." Thesis, University of Exeter, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328834.
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Abdel-Ghaly, A. A. "Analysis of predictive quality of software reliability models." Thesis, City University London, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370836.
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Campbell, Alyce. "An empirical study of a financial signalling model." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26969.
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Brennan and Kraus (1982,1986) developed a costless signalling model which can explain why managers issue hybrid securities—convertibles(CB's) or bond-warrant packages(BW's). The model predicts that when the true standard deviation (σ) of the distribution of future firm value is unknown to the market, the firm's managers will issue a hybrid with specific characteristics such that the security's full information value is at a minimum at the firm's true σ. In this fully revealing equilibrium market price is equal to this minimum value.
In this study, first the mathematical properties of the hypothesized bond-valuation model were examined to see if specific functions could have a minimum not at σ = 0 or σ = ∞ as required for signalling. The Black-Scholes-Merton model was the valuation model chosen because of ease of use, supporting empirical evidence, and compatibility with the Brennan-Kraus model. Three different variations, developed from Ingersoll(1977a); Geske( 1977,1979) and Geske and Johnson(1984); and Brennan and Schwartz(1977,1978), were examined. For all hybrids except senior CB's, pricing functions with a minimum can be found for plausible input parameters. However, functions with an interior maximum are also plausible. A function with a maximum cannot be used for signalling.
Second, bond pricing functions for 105 hybrids were studied. The two main hypotheses were: (1) most hybrids have functions with an interior minimum; (2) market price equals minimum theoretical value. The results do not support the signalling model, although the evidence is ambiguous. For the σ range 0.05-0.70, for CB's (BW's) 15(8) Brennan-Schwartz functions were everywhere positively sloping, 11(2) had an interior minimum, 22(0) were everywhere negatively sloping, and 35(12) had an interior maximum. Market prices did lie closer to minima than maxima from the Brennan-Schwartz solutions, but the results suggest that the solution as implemented overpriced the CB's. BW's were unambiguously overpriced. With consistent overpricing, market prices would naturally lie closer to minima. Average variation in theoretical values was, however, only about 5 percent for CB's and about 10 percent for BW's. This, coupled with the shape data, suggests that firms were choosing securities with theoretical values relatively insensitive to a rather than choosing securities to signal σ unambiguously.Business, Sauder School of
Graduate
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Silva, Djany Souza. "Evaluation of mathematical models to prediction the dynamic viscosity of fruit juices." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14440.
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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel SuperiorO consumo de sucos de frutas tem crescido, devido a comodidade e praticidade gerada pelos produtos prontos. Segundo a AssociaÃÃo Brasileira das IndÃstrias de Refrigerantes, em 2012, a produÃÃo anual foi de 987 milhÃes de litros de sucos de frutas no Brasil. No entanto, para alcanÃar maior eficiÃncia e rendimento, torna-se necessÃrio o conhecimento do comportamento reolÃgico das matÃrias-primas. A viscosidade à uma das propriedades reolÃgicas usada em diversas aplicaÃÃes, tais como: parÃmetro para o cÃlculo de coeficientes de transferÃncia de calor e massa; dimensionamento de equipamentos; avaliaÃÃo de custos; projetos de processos; controle de qualidade do produto; alÃm de possibilitar a compreensÃo da estrutura quÃmica das matÃrias-primas. Durante o processamento industrial dos sucos de frutas, a matÃria-prima à submetida à variaÃÃes de temperaturas e concentraÃÃes de sÃlidos que alteram sua viscosidade. Por esse motivo, o conhecimento dos efeitos combinados desses dois parÃmetros na viscosidade à essencial para a indÃstria de sucos. Nesse trabalho, dados experimentais da literatura para onze sucos clarificados de frutas (manga, cereja, maÃÃ, pÃssego, groselha, romÃ, pÃra, limÃo, tangerina, limÃo-galego e uva) em concentraÃÃes e temperaturas de 15,0 a 74,0 ÂBrix, e 278,15 a 393,15 K, respectivamente, foram modelados utilizando correlaÃÃes empÃricas e semi-empÃricas oriundas da literatura. ParÃmetros globais e especÃficos, respectivamente, em funÃÃo da temperatura e concentraÃÃo de sÃlidos solÃveis totais (SST), foram mantidos nos modelos. Quatro equaÃÃes foram avaliadas no cÃlculo da energia de ativaÃÃo (equaÃÃo da reta, exponencial, polinomial de 2 e 3 ordem) nos modelos. E trÃs estratÃgias de modelagem foram realizadas: ajuste para todas as concentraÃÃes de SST e temperaturas; em diferentes faixas de concentraÃÃes de SST; e, diferentes faixas de temperaturas. A estratÃgia de otimizaÃÃo por faixas de concentraÃÃes de SST mostrou-se a mais adequada. Duas relaÃÃes matemÃticas exponenciais, baseadas na correlaÃÃo de Arrhenius, obtiveram bons resultados na prediÃÃo da viscosidade dinÃmica de sucos de frutas clarificados entre as concentraÃÃes de 17,0 a 50,1 ÂBrix para todas as temperaturas de estudo. Enquanto que o uso da equaÃÃo de Vogel obteve bons resultados para concentraÃÃes de 51,0 a 66,0 ÂBrix na prediÃÃo da viscosidade dinÃmica dos sucos de frutas. Os modelos foram validados com dados experimentais para suco clarificado de laranja em baixas (30,7 a 50,5 ÂBrix) e altas concentraÃÃes (54,1 a 63,5 ÂBrix) de SST, com excelente prediÃÃo da viscosidade dinÃmica.
The comsumption of fruit juices has grown due to co nvenience and practicality generated by the finished products. According to the AssociaÃÃo Brasileira das IndÃstrias de Refrigerantes, in 2012 the annual production was 987 million liter s of fruit juices in Brazil. However, to achieve greater efficiency and performance, it is n ecessary to know the rheological behavior of the raw materials. Among rheological properties, viscosity is widely used in industrial and academic applications such as: a parameter for the calculation of heat and mass transfer coefficients; equipment design; cost assessment; de sign processes; quality control of the product; and enable an understanding of the chemica l structure of raw materials. During industrial processing of fruit juices, the raw mate rials are submitted to temperatures and concentrations of solids variations that altering i ts viscosity. Therefore, the knowledge of the combined effect of temperature and concentration of solids on viscosity are essential for the juice processing. In this work, literature data fro m eleven clarified juices of fruit (mango, cherry, apple, peach, blackcurrant, pomegranate, pe ar, lemon, tangerine, lime and grape) at concentrations and temperatures from 15.0 to 74.0 Â Brix and from 278.15 to 393.15 K, respectively, were modeled using empirical and semi -empirical correlations derived from the literature. Global and specific parameters for all studied models been obtained in function of temperature and total soluble solids (TSS) concentr ation. Four equations were evaluated to calculate the activation energy in each model (line ar equation, exponential, polynomial of 2nd and 3rd order) using activation energy as specific parameter, and three different modeling strategies were conducted: for all TSS concentratio ns and temperatures; two ranges concentrations of TSS; and, two ranges of temperatu res. The optimization strategy for the concentrations TSS range proved the most suitable. Two exponential mathematical relations based on correlation of Arrhenius have been success ful in predicting the dynamic viscosity of clarified fruit juices at concentrations from 17.0 to 50.1 ÂBrix for all temperatures studied. While Vogel's equation obtained good results for co ncentrations of 51.0 to 66.0 ÂBrix in predicting the dynamic viscosity of fruit juices. T he models were validated using experimental data to clarified orange juices at low (30.7 to 50.5 ÂBrix) and high concentrations (54.1 to 63.5 ÂBrix) of TSS, with ex cellent prediction of dynamic viscosity
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Williams, Robert C. "The Development of Mathematical Models for Preliminary Prediction of Highway Construction Duration." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/29483.
Full textAbstract:
Knowledge of construction duration is pertinent to a number of project planning functions prior to detailed design development. Funding, financing, and resource allocation decisions take place early in project design development and are significantly influenced by the construction duration. Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration.
This research identifies the project factors, known early in design development, which influence highway construction duration. The factors identified are specific to their respective project types and are those factors which demonstrate a statistically-significant relationship with construction duration. This work also quantifies the relationship between the duration-influential factors and highway construction duration. The quantity, magnitude, and sign of the factor coefficient yields evidence regarding the importance of the project factor to highway construction duration. Finally, the research incorporates the duration-influential project factors and their relationship with highway construction duration into mathematical models which assist in the prediction of construction duration. Full and condensed models are presented for Full-Depth Section and Highway Improvement project types. This research uses statistical regression analysis to identify, quantify, and model these early-known, duration-influential project factors.
The results of this research contribute to the body of knowledge of the sponsoring organization (Virginia Department of Transportation), the highway construction industry, and the general construction industry at large.Ph. D.
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Meeuwig, Jessica Jane. "All water is wet : predicting eutrophication in lakes and estuaries." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=35918.
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Coastal eutrophication, defined as an increase in algal biomass (as chlorophyll (Chl)) is of increasing international concern. Although coastal eutrophication will likely increase as coastal populations grow, few models exist to support its management. Lake eutrophication has also long been recognized as an important environmental concern. However, effective lake eutrophication management exists, supported by regression and mass-balance models. Traditionally, these "Vollenweider" models link land-use to Chl via total phosphorus (TP), the nutrient considered to be limiting Chl. However, based on a data set of 63 lakes, Chl was more accurately predicted by models based on land-use than by those based on TP. This result provided the rationale to build Chl:land-use models for estuaries where the Chl:nutrient relations are unclear. Chl:land-use models were developed for 15 estuaries in PEI, 19 estuaries in Finland and 26 US estuaries. Land-use models predicted Chl more accurately than TP in the US estuaries and in some of the Finnish estuaries. In the Finnish estuaries, Chl was best predicted by a land-use model in estuaries dominated by nonpoint source loading whereas Chl was most accurately predicted by the Vollenweider approach in estuaries dominated by point-source loading. In the PEI estuaries, the accuracy of the land-use model was comparable to the accuracy of the TP model. The PEI estuaries had much lower yields of Chl per unit nutrient than lakes suggesting differences among systems. This Chl deficit (expected-observed Chl) was accounted for by herbivory and turbidity, neither of which factors are exclusive to estuaries. The comparison of Chl response to nutrients and land-use across lakes and estuaries demonstrated no systematic differences as a function of tidal energy, openness or salinity. The regression models based on the combined data accurately predicted Chl as a function of TP and percentage of the catchment forested and mean depth. These results sug
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Bengtsson, Ivar. "Autonomous Overtaking with Learning Model Predictive Control." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.
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We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas.Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.
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Dixon, Angela. "Designing Predictive Mathematical Models for the Metabolic Pathways Associated with Polyhydroxybutyrate Synthesis in Escherichia coli." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1098.
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Polyhydroxybutyrate (PHB) is a polyhydroxyalkanoate that has been extensively studied as a potential biodegradable replacement for petrochemically derived plastics. The synthesis pathway of PHB is native to Ralstonia eutropha, but the genes for the PHB pathway have successfully been introduced into Escherichia coli through plasmids such as the pBHR68 plasmid. However, the production of PHB needs to be more cost-effective before it can be commercially produced.
A mathematical model for PHB synthesis was developed to identify target genes that could be genetically engineered to increase PHB production. The major metabolic pathways included in the model were glycolysis, acetyl coenzyme A (acetyl-CoA) synthesis, tricarboxylic acid (TCA) cycle, glyoxylate bypass, and PHB synthesis. Each reaction in the selected metabolic pathways was modeled using the kinetic mechanism identified for the associated enzyme. The promoters and transcription factors for each enzyme were incorporated into the model. The model was validated through comparison with other published models and experimental PHB production data. The predictive model identified 16 enzymes as having no effect on PHB production, 5 enzymes with a slight effect on PHB production, and 9 enzymes with large effects on PHB production. Decreasing the substrate affinity of the enzyme citrate synthase resulted in the largest increase in PHB synthesis. The second largest increase was observed from lowering the substrate affinity of glyceraldehyde-3-phosphate dehydrogenase. The predictive model also indicated that increasing the activity of the lac promoter in the pBHR68 plasmid resulted in the largest increase in the rate of PHB production.
The predictive model successfully identified two genes and one promoter as targets for genetic engineering to create an optimized strain of E. coli for PHB production. The substrate-binding sites for the genes gltA (citrate synthase) and gapA (glyceraldehyde-3-phosphate dehydrogenase) should be genetically engineered to be less effective at binding the substrates. The lac promoter in the pBHR68 plasmid should be genetically engineered to more closely match the consensus sequence for binding to RNA polymerase. The model predicts that an optimized strain of E. coli for PHB production could be achieved by genetically altering gltA, gapA, and the lac promoter.
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Smoler, Eliezer. "Mathematical models to predict milk protein concentration from dietary components fed to dairy cows." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308060.
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Numrich, Joanne L. "Predicting CLA production in the rumen/duodenum by the use of mathematical models /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1075690481&sid=7&Fmt=2&clientId=1509&RQT=309&VName=PQD.
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CONTI, ALEXANDRE. "DEVELOPMENT OF A TRANSIENT MATHEMATICAL MODEL FOR THE PREDICTION OF PLANAR LANDSLIDES IN NATURAL SLOPES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34998@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esse trabalho tem como objetivo desenvolver um modelo determinístico transiente de previsão de escorregamentos planares em encostas, para escalas em nível de bacia hidrográfica (1:2000 a 1:5000). No modelo são aplicadas as teorias de Green-Ampt (1934) e de O Loughlin (1986), essa última utilizada no programa SHALSTAB (MONTGOMERY e DIETRICH, 1994), além da teoria talude infinito 2D e 3D. Também são considerados nas análises a não saturação do solo e os efeitos da vegetação. O evento estudado para aplicação e teste do modelo refere-se ao ocorrido em 1996, nas bacias do Quitite e Papagaio em Jacarepaguá, Zona Oeste do Rio de Janeiro. Além do mapeamento do fator de segurança nas bacias, também são gerados mapas com o escoamento superficial acumulado, e tenta-se correlacionar ambos com as cicatrizes que ocorreram no caso de estudo.
The aim of this work is to develop a physically-based transient model for the prediction of planar landslides in natural slopes. The application scale of the model is for a hydrographic basin (1:2000 to 1:5000). The theories of Green-Ampt (1934) and O Loughlin (1986), the second one used in the SHALSTAB program (MONTGOMERY e DIETRICH, 1994), and the infinite slope 2D and 3D are used in the model. The effect of the unsaturation and the vegetation is also considered in the analysis. The case study for the test of the model is the 1996 event that took place in the Quitite and Papagaio basins, in Jacarepaguá, Zona Oeste of Rio de Janeiro. Besides mapping the safety factor in the basins, maps of the accumulated runoff were also generated. This work also tries to correlate the runoff as another factor that caused the landslides.
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Abbas, Kaja Moinudeen. "Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5302/.
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Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.
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Ahlin, Mikael, and Felix Ranby. "Predicting Marketing Churn Using Machine Learning Models." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161408.
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For any organisation that engages in marketing actions there is a need to understand how people react to communication messages that are sent. Since the introduction of General Data Protection Regulation, the requirements for personal data usage have increased and people are able to effect the way their personal information is used by companies. For instance people have the possibility to unsubscribe from communication that is sent, this is called Opt-Out and can be viewed as churning from communication channels. When a customer Opt-Out the organisation loses the opportunity to send personalised marketing to that individual which in turn result in lost revenue. The aim with this thesis is to investigate the Opt-Out phenomena and build a model that is able to predict the risk of losing a customer from the communication channels. The risk of losing a customer is measured as the estimated probability that a specic individual will Opt-Out in the near future. To predict future Opt-Outs the project uses machine learning algorithms on aggregated communication and customer data. Of the algorithms that were tested the best and most stable performance was achieved by an Extreme Gradient Boosting algorithm that used simulated variables. The performance of the model is best described by an AUC score of 0.71 and a lift score of 2.21, with an adjusted threshold on data two months into the future from when the model was trained. With a model that uses simulated variables the computational cost goes up. However, the increase in performance is signicant and it can be concluded that the choice to include information about specic communications is considered relevant for the outcome of the predictions. A boosted method such as the Extreme Gradient Boosting algorithm generates stable results which lead to a longer time between model retraining sessions.
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Banerjee, Soumitra. "Development of a procedure for predicting daylighting in square type atrium." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80103.
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The popularity of the atria after its revival since the 1970's has caught the attention of designers and opened ways for new strategies of energy conservation for large buildings. Early atria were visual statements rather than integrated energy systems. But a growing recognition of the contribution of daylight for energy conservation in atria requires study in greater depth to exploit the potential of the atrium in the use of daylight for energy conservation. Present methods for calculating daylight distribution in conventionally designed buildings are not presently configured to deal with atria.
This study takes advantage of scale model simulation process to develop a mathematical model which will predict daylight distribution in a square atrium under an overcast sky. Data generated from twelve model studies representing thirty six cases were analyzed using statistical methods as a measure to develop the mathematical model.
The mathematical model developed has the ability to predict illumination level on the vertical surface at different floor locations in a square type atrium within the specified limitations. This model is reliable, as the predicted illumination levels have been found to have strong correlation with the values obtained from scale model studies. The mathematical model can be effectively used to assist designers in estimating illumination levels in an atrium and to provide opportunity to test design alternatives while the design is in the preliminary design stage.Master of Architecture
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Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.
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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and propose the task of predicting future states of the chaotic system from limited observations. We explore two directions, answering two distinct fundamental questions of the system based on how informed we are about the underlying model. When we know the data is generated by the Lorenz System with unknown parameters, our task becomes parameter estimation (a white-box problem), or the ``inverse'' problem. When we know nothing about the underlying model (a black-box problem), our task becomes sequence prediction. We propose two algorithms for the white-box problem: Markov-Chain-Monte-Carlo (MCMC) and a Multi-Layer-Perceptron (MLP). Specially, we propose to use the Metropolis-Hastings (MH) algorithm with an additional random walk to avoid the sampler being trapped into local energy wells. The MH algorithm achieves moderate success in predicting the $\rho$ value from the data, but fails at the other two parameters. Our simple MLP model is able to attain high accuracy in terms of the $l_2$ distance between the prediction and ground truth for $\rho$ as well, but also fails to converge satisfactorily for the remaining parameters. We use a Recurrent Neural Network (RNN) to tackle the black-box problem. We implement and experiment with several RNN architectures including Elman RNN, LSTM, and GRU and demonstrate the relative strengths and weaknesses of each of these methods. Our results demonstrate the promising role of machine learning and modern statistical data science methods in the study of chaotic dynamic systems. The code for all of our experiments can be found on \url{https://github.com/Yajing-Zhao/}
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Flöjs, Amanda, and Alexandra Hägg. "Churn Prediction : Predicting User Churn for a Subscription-based Service using Statistical Analysis and Machine Learning Models." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171678.
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Subscription-based services are becoming more popular in today’s society. Therefore, any company that engages in the subscription-based business needs to understand the user behavior and minimize the number of users canceling their subscription, i.e. minimize churn. According to marketing metrics, the probability of selling to an existing user is markedly higher than selling to a brand new user. Nonetheless, it is of great importance that more focus is directed towards preventing users from leaving the service, in other words preventing user churn. To be able to prevent user churn the company needs to identify the users in the risk zone of churning. Therefore, this thesis project will treat this as a classification problem. The objective of the thesis project was to develop a statistical model to predict churn for a subscription-based service. Various statistical methods were used in order to identify patterns in user behavior using activity and engagement data including variables describing recency, frequency, and volume. The best performing statistical model for predicting churn was achieved by the Random Forest algorithm. The selected model is able to separate the two classes of churning users and the non-churning users with 73% probability and has a fairly low missclassification rate of 35%. The results show that it is possible to predict user churn using statistical models. Although, there are indications that it is difficult for the model to generalize a specific behavioral pattern for user churn. This is understandable since human behavior is hard to predict. The results show that variables describing how frequent the user is interacting with the service are explaining the most whether a user is likely to churn or not.Prenumerationstjänster blir alltmer populära i dagens samhälle. Därför är det viktigt för ett företag med en prenumerationsbaserad verksamhet att ha en god förståelse för sina användares beteendemönster på tjänsten, samt att de minskar antalet användare som avslutar sin prenumeration. Enligt marknads-föringsstatistik är sannolikheten att sälja till en redan existerande användare betydligt högre än att sälja till en helt ny. Av den anledningen, är det viktigt att ett stort fokus riktas mot att förebygga att användare lämnar tjänsten. För att förebygga att användare lämnar tjänsten måste företaget identifiera vilka användare som är i riskzonen att lämna. Därför har detta examensarbete behandlats som ett klassifikations problem. Syftet med arbetet var att utveckla en statistisk modell för att förutspå vilka användare som sannolikt kommer att lämna prenumerationstjänsten inom nästa månad. Olika statistiska metoder har prövats för att identifiera användares beteendemönster i aktivitet- och engagemangsdata, data som inkluderar variabler som beskriver senaste interaktion, frekvens och volym. Bäst prestanda för att förutspå om en användare kommer att lämna tjänsten gavs av Random Forest algoritmen. Den valda modellen kan separera de två klasserna av användare som lämnar tjänsten och de användare som stannar med 73% sannolikhet och har en relativt låg missfrekvens på 35%. Resultatet av arbetet visar att det går att förutspå vilka användare som befinner sig i riskzonen för att lämna tjänsten med hjälp av statistiska modeller, även om det är svårt för modellen att generalisera ett specifikt beteendemönster för de olika grupperna. Detta är dock förståeligt då det är mänskligt beteende som modellen försöker att förutspå. Resultatet av arbetet pekar mot att variabler som beskriver frekvensen av användandet av tjänsten beskriver mer om en användare är påväg att lämna tjänsten än variabler som beskriver användarens aktivitet i volym.
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Pemberton, W. Patrick. "Predictive relationships in friction stir processing of nickel-aluminum bronze." Thesis, Monterey, Calif. : Naval Postgraduate School, 2005. http://handle.dtic.mil/100.2/ADA441369.
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Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, September 2005.Thesis Advisor(s): Terry R. McNelley. "September 2005." Includes bibliographical references (p. 45-47). Also available in print.
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Kim, Changkyun. "Development and evaluation of traffic prediction systems." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164007/.
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Xu, Peng School of Mathematics UNSW. "A computational model for the assessment and prediction of salinisation in irrigated areas." Awarded by:University of New South Wales. School of Mathematics, 2003. http://handle.unsw.edu.au/1959.4/23342.
Full textAbstract:
This thesis presents the results of a computational study on salt transport and accumulation in crop root zone. The main objective of this study is to examine the impacts of past land use on the environment and to examine the effect of irrigation water on the rising of groundwater level and the subsequent salinity problem in rice growing area under given climatic conditions. A special focus has been such impacts in the Wakool irrigation area, NSW, Australia. To this end, a computational model for the assessment and prediction of salinisation in agricultural areas has been developed. This modelling system consists of a land surface scheme (ALSIS) for simulating unsaturated soil moisture and moisture flux, a groundwater flow model (MODFLOW) for estimating the spatial and temporal variations of groundwatertable, a surface flow model (DAFLOW) for calculating water flow in river networks, a module for calculating solute transport at unsaturated zone and a 3-D model (MOC3D) for simulating solute transport in groundwater as well as a module for calculating the spatial and temporal distributions of overland flow depth during wet seasons. The modelling system uses a finite difference linked technique to form a quasi three dimensional model. The land surface scheme is coupled with the groundwater flow model to account for the interactions between the saturated and unsaturated zones. On the land surface, the modelling system incorporates a surface runoff model and detailed treatments of surface energy balance, which is important in es-timating the evapotranspiration, a crucial quantity in calculating the moisture and moisture fluxes in the root zone. Vertical heterogeneity of soil hydraulic properties in the soil profile has been considered. The modelling system has the flexibility of using either Clapp and Hornberger (1978), Broadbridge and White (1988), van Genuchten (1980) or Brooks and Corey (1966) soil water retention models. Deep in the soil, the impact of groundwater table fluctuation on soil moisture and salinity in the unsaturated soil is also included. The calibration and validation for the system have been partially performed with observed groundwater levels in the Wakool irrigation area. The applications of the model to theWakool region are made in two steps. Firstly, a one-dimensional simulation to a selected site in the Wakool irrigation area is carried out to study the possible impact of ponded irrigation on salinisation and the general features of salt movement. Secondly, a more realistic three-dimensional simulation for the entire Wakool region is performed to study the spatial and temporal variations of root zone soil salinity under the influence of past land use from 1975 to 1994. To allow the assessment and prediction of the effects of ponded rice irrigation water (which contains salt) on soil salinity in the area, several hypothetical scenarios using different qualities of water for rice irrigation are tested. To facilitate comparative analysis of different scenarios, a base case is defined, for which irrigation water is assumed to be free of salt. The simulated results show that irrigation increases overall recharge to groundwater in the Wakool irrigation area. The use of ponded irrigation for rice growing has a substantial effect on salt accumulation in the root zone and the rising of groundwater level, indicating that irrigation at rice bay is a major budget item for controlling soil salinity problem in the local area.