Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast simulations. Currently, Numerical Weather Prediction (NWP) is the premier method for weather prediction, which is used by the National Oceanic and Atmospheric Administration (NOAA). It takes the current observations from observed sites as the input for numeric computer models and then produces the final prediction. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying the parameter configuration that leads to most accurate prediction. In this thesis, we develop an Autonomic Workflow Performance Manager (AWPM) for Hurricane Integrated Modeling System (HIMS). AWPM is implemented on top of the Apache Storm and ZooKeeper to handle multiple real-time data streams for weather forecast. AWPM can automatically manage model initialization and execution workflow and achieve better performance and efficiency. In our experiments, AWPM achieves better performance and efficiency for the model initialization and execution processes, by utilizing automatic computing, distributed computing and component-based development. We reduced the timescale of the configuration search workflow by a factor of 10 by using 20 threads with the full search method, and a factor of 20 by with the roofline method when compared to serial workflow execution as it is typically performed by domain scientists.

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.

Contemporary and future relative sea-level (RSL) rise that can be attributed to anthropogenic climate change sees significant spatial variability as a result of the processes that underlie it. Some of the processes that contribute to RSL rise unrelated to anthropogenic climate change can and have had significant contributions. In this work, we examined the contributions of one of these processes, glacial isostatic adjustment (GIA), in the coastal regions of Atlantic Europe. These regions have seen significant RSL rise associated with a collapsing peripheral bulge throughout the Holocene and are expected to see more throughout the Anthropocene. Using the recently published paleo sea level database (García-Artola et al., 2018) which follows the HOLSEA RSL data assessment and reporting protocol (Khan et al., 2019) we determined optimal Earth model parameters for much of Atlantic Europe. These optimal parameters fit the data well and largely agree with values determined for previous works on peripheral bulges along the coasts of North America. We further used these results to perform a rudimentary sea-level budget analysis at 10 tide gauge stations, yielding results with high uncertainties and significant discrepancies between observed and projected rates of RSL change for half (5) of the tide gauge stations. Our results lead to the conclusion that GIA remains an important factor when predicting present and future RSL change.

The Master's thesis deals with the simulations kinematics and dynamics of experimental four-wheeled vehicle with all-wheel steering and all-wheel drive. Suggestion of vehicle stability systems ABS/ASR for traction control is included. There are several dynamics models with their comparison. The estimation of important vehicle parameters is implemented. Based on knowledge the simple vehicle stability system ABS/ASR is created.

A series of field, rainshelter, growth cabinet and modelling studies were conducted to investigate hot pepper response to different irrigation regimes and row spacings; to generate crop-specific model parameters; and to calibrate and validate the Soil Water Balance (SWB) model. Soil, climate and management data of five hot pepper growing regions of Ethiopia were identified to develop irrigation calendars and estimate water requirements of hot pepper under different growing conditions. High irrigation regimes increased fresh and dry fruit yield, fruit number, harvest index and top dry matter production. Yield loss could be prevented by irrigating at 20-25% depletion of plant available water, confirming the sensitivity of the crop to mild soil water stress. High plant density markedly increased fresh and dry fruit yield, water-use efficiency and dry matter production. Average fruit mass, succulence and specific leaf area were neither affected by row spacing nor by irrigation regimes. There were marked differences among the cultivars in fruit yields despite comparable top dry mass production. Average dry fruit mass, fruit number per plant and succulence were significantly affected by cultivar differences. The absence of interaction effects among cultivar and irrigation regimes, cultivars and row spacing, and irrigation regimes and row spacing for most parameters suggest that appropriate irrigation regimes and row spacing that maximize productivity of hot pepper can be devised across cultivars. To facilitate irrigation scheduling, a simple canopy cover based procedure was used to determine FAO-type crop factors and growth periods for different growth stages of five hot pepper cultivars. Growth analysis was done to calculate crop-specific model parameters for the SWB model and the model was successfully calibrated and validated for five hot pepper cultivars under different irrigation regimes or row spacings. FAO basal crop coefficients (Kcb) and crop-specific model parameters for new hot pepper cultivars can now be estimated from the database, using canopy characteristics, day degrees to maturity and dry matter production. Growth cabinet studies were used to determine cardinal temperatures, namely the base, optimum and cut-off temperatures for various developmental stages. Hot pepper cultivars were observed to require different cardinal temperatures for various developmental stages. Data on thermal time requirement for flowering and maturity between plants in growth cabinet and open field experiments matched closely. Simulated water requirements for hot pepper cultivar Mareko Fana production ranged between 517 mm at Melkassa and 775 mm at Alemaya. The simulated irrigation interval ranged between 9 days at Alemaya and 6 days at Bako, and the average irrigation amount per irrigation ranged between 27.9 mm at Bako and 35.0 mm at Zeway. / Thesis (PhD)--University of Pretoria, 2010. / Plant Production and Soil Science / unrestricted

This dissertation is devoted to studying a fast and analytic approximation method, called the variational Bayesian (VB) method, and aims to give insight into its general applicability and usefulness, and explore its applications to various real-world problems. This work has three main foci: 1) The general applicability and properties; 2) Diagnostics for VB approximations; 3) Variational applications. Generally, the variational inference has been developed in the context of the exponential family, which is open to further development. First, it usually consider the cases in the context of the conjugate exponential family. Second, the variational inferences are developed only with respect to natural parameters, which are often not the parameters of immediate interest. Moreover, the full factorization, which assumes all terms to be independent of one another, is the most commonly used scheme in the most of the variational applications. We show that VB inferences can be extended to a more general situation. We propose a special parameterization for a parametric family, and also propose a factorization scheme with a more general dependency structure than is traditional in VB. Based on these new frameworks, we develop a variational formalism, in which VB has a fast implementation, and not be limited to the conjugate exponential setting. We also investigate its local convergence property, the effects of choosing different priors, and the effects of choosing different factorization scheme. The essence of the VB method relies on making simplifying assumptions about the posterior dependence of a problem. By definition, the general posterior dependence structure is distorted. In addition, in the various applications, we observe that the posterior variances are often underestimated. We aim to develop diagnostics test to assess VB approximations, and these methods are expected to be quick and easy to use, and to require no sophisticated tuning expertise. We propose three methods to compute the actual posterior covariance matrix by only using the knowledge obtained from VB approximations: 1) To look at the joint posterior distribution and attempt to find an optimal affine transformation that links the VB and true posteriors; 2) Based on a marginal posterior density approximation to work in specific low dimensional directions to estimate true posterior variances and correlations; 3) Based on a stepwise conditional approach, to construct and solve a set of system of equations that lead to estimates of the true posterior variances and correlations. A key computation in the above methods is to calculate a uni-variate marginal or conditional variance. We propose a novel way, called the VB Adjusted Independent Metropolis-Hastings (VBAIMH) method, to compute these quantities. It uses an independent Metropolis-Hastings (IMH) algorithm with proposal distributions configured by VB approximations. The variance of the target distribution is obtained by monitoring the acceptance rate of the generated chain. One major question associated with the VB method is how well the approximations can work. We particularly study the mean structure approximations, and show how it is possible using VB approximations to approach model selection tasks such as determining the dimensionality of a model, or variable selection. We also consider the variational application in Bayesian nonparametric modeling, especially for the Dirichlet process (DP). The posterior inference for DP has been extensively studied in the context of MCMC methods. This work presents a a full variational solution for DP with non-conjugate settings. Our solution uses a truncated stick-breaking representation. We propose an empirical method to determine the number of distinct components in a finite dimensional DP. The posterior predictive distribution for DP is often not available in a closed form. We show how to use the variational techniques to approximate this quantity. As a concrete application study, we work through the VB method on regime-switching lognormal models and present solutions to quantify both the uncertainty in the parameters and model specification. Through a series numerical comparison studies with likelihood based methods and MCMC methods on the simulated and real data sets, we show that the VB method can recover exactly the model structure, gives the reasonable point estimates, and is very computationally efficient.

Les incertitudes épistémiques peuvent être réduites via des études supplémentaires (mesures labo, in situ, ou modélisations numériques, etc.). Nous nous concentrons ici sur celle "paramétrique" liée aux difficultés à évaluer quantitativement les paramètres d’entrée du modèle utilisé pour l’analyse des aléas géotechniques. Une stratégie de gestion possible est l’analyse de sensibilité, qui consiste à identifier la contribution (i.e. l’importance) des paramètres dans l’incertitude de l’évaluation de l’aléa. Des approches avancées existent pour conduire une telle analyse. Toutefois, leur application au domaine des aléas géotechniques se confronte à plusieurs contraintes : 1. le coût calculatoire des modèles numériques (plusieurs heures voire jours) ; 2. les paramètres sont souvent des fonctions complexes du temps et de l’espace ; 3. les données sont souvent limitées, imprécises voire vagues. Dans cette thèse, nous avons testé et adapté des outils statistiques pour surmonter ces limites. Une attention toute particulière a été portée sur le test de faisabilité de ces procédures et sur la confrontation à des cas réels (aléas naturels liés aux séismes, cavités et glissements de terrain) / Importance ranking of parameter uncertainties in geo-hazard assessments Epistemic uncertainty can be reduced via additional lab or in site measurements or additional numerical simulations. We focused here on parameter uncertainty: this corresponds to the incomplete knowledge of the correct setting of the input parameters (like values of soil properties) of the model supporting the geo-hazard assessment. A possible option tomanage it is via sensitivity analysis, which aims at identifying the contribution (i.e. the importance) of the different input parameters in the uncertainty on the final hazard outcome. For this purpose, advanced techniques exist, namely variance-basedglobal sensitivity analysis. Yet, their practical implementation faces three major limitations related to the specificities of the geo-hazard domain: 1. the large computation time cost (several hours if not days) of numerical models; 2. the parameters are complex functions of time and space; 3. data are often scarce, limited if not vague. In the present PhD thesis, statistical approaches were developed, tested and adapted to overcome those limits. A special attention was paid to test the feasibility of those statistical tools by confronting them to real cases (natural hazards related to earthquakes, cavities and landslides)

This thesis deals with design of electronic laboratory model for teaching mechatronic subjects. The main part of the model consists of a RLC-circuit embedded in PCB. Other parts of PCB and data acquisition card mediate communication with Matlab environment. In the thesis the progress of design process, simulation, manufacture and model testing is described. The results are functioning educational model and several educational tasks, for which the solution are presented.

Les travaux de thèse portent sur l'identification paramétrique en dynamique transitoire à partir des mesures fortement bruitées, l'un des objectifs à long terme étant de proposer une méthode d’identification peu intrusive afin de pouvoir être implémentée dans des codes de calcul éléments finis commerciaux. Dans ce travail, le concept de l'erreur en relation de comportement modifiée a été retenu pour traiter le problème d’identification des paramètres matériau. La minimisation de la fonctionnelle coût sous contraintes débouche, dans le cas de la dynamique transitoire, sur un problème dit « aux deux bouts » dans lequel il s’agit de résoudre un problème différentiel spatio-temporel avec des conditions à la fois initiales et finales en temps. Il en résulte un problème couplé entre les champs direct et adjoint dont le traitement est délicat. Dans un premier temps, des méthodes précédemment développées telles que la « méthode de Riccati » et la « méthode de tirs » ont été étudiées. Il est montré que l’identification par ces méthodes est robuste même pour des mesures fortement corrompues, mais qu’elles sont limitées par la complexité d’implémentation dans un code industriel, des problèmes de conditionnement ou de coût de calcul. Dans un second temps, une approche itérative basée sur une méthode de sur-relaxation a été développée et comparée à celles précédemment mentionnées sur des exemples académiques, validant l’intérêt de cette nouvelle approche. Enfin, des comparaisons ont été menées entre cette technique et une variante « discrétisée » de la formulation introduite par Bonnet et Aquino [Inverse Problems, vol. 31, 2015]. / This thesis deals with parameters identification in transient dynamic in case of highly noisy experimental data. One long-term goal is the derivation of a non-intrusive method dedicated to the implementation in a commercial finite element code.In this work, the modified error in the constitutive relation framework is used to treat the identification of material parameters. The minimization of the cost function under constraints leads, in the case of transient dynamics, to a « two points boundary value problem » in which the differential space-time problem involves both initial and final time conditions. This results in a problem coupling the direct and adjoint fields, whose treatment is difficult.In the first part, methods such as those based on the « Riccati equations » and the « shooting methods » have been studied. It is shown that the identification is robust even in the case of highly corrupted measures, but these methods are limited either by the implementation intrusiveness, conditioning problems or the numerical cost.In the second part, an iterative over-relaxation approach is developed and compared to the aforementioned approaches on academic problems in order to validate the interest of the method. Finally, comparisons are carried out between this approach and a « discretized » variation of the formulation introduced by Bonnet and Aquino [Inverse Problems, vol. 31, 2015].

This thesis aims to solve the mathematical inverse problem of characterizing sea waves based on the responses obtained from a marine vessel sailing under certain sea conditions. By researching this problem the thesis contributes to the marine industry by improving products that are using ocean behavior for controlling ship's dynamics. Knowledge about the current state of the sea, such as the wave frequency and height, is important for navigation, control, and for the safety of a vessel. This information can be retrieved from specialized weather reports. However, such information is not at all time possible to obtain during a voyage, and if so usually comes with a certain delay. Therefore this thesis seeks solutions that can estimate on-line the waves' state using methods in the field of Artificial Intelligence. The specific investigation methods are Transfer Functions augmented with Genetic Algorithm, Artificial Neural Networks and Case-Based Reasoning. These methods have been configured and validated using the n-fold cross validation method. All the methods have been tested with an actual implementation. The algorithms have been trained with data acquired from a marine simulation program developed in Simulink. The methods have also been trained and tested using monitored data acquired from an actual ship sailing on the Baltic Sea as well as wave data obtained from a buoy located nearby the vessel's route. The proposed methods have been compared with state-of-the art reports in order evaluate the novelty of the research and its potential applications in industry. The results in this thesis show that the proposed methods can in fact be used for solving the inverse problem. It was also found that among the investigated methods it is the Transfer Function augmented with Genetic Algorithm which yields best results. This Master Thesis is conducted under the Master of Engineering Program in Robotics at Mälardalens högskola in Västerås, Sweden. The thesis was proposed by Q-TAGG R&D AB in Västerås, Sweden, a company which specializes in marine vessel dynamics research.