The leading-edge vortex (LEV) is a structure found in unsteady aerodynamics that can alter the forces induced on wings and other rotating structures. This thesis presents an experimental study on LEV development on low aspect-ratio wing rolling in a uniform flow at high angles of attack. The flow structure dynamics of rotating wings in the presence of a free stream are not well understood due to the limited studies under these conditions. In this study, a broad parameter space with varying advance ratio and wing radius of gyration are analyzed using dye-visualizations. In most cases, either a conical LEV structure developed on the inboard part of the wing and persisted to a significant roll angle, as well as the arch structure. Plenoptic PIV was used to validate observations in flow visualizations as well as identify finer structures. A binary classification criterion was defined based on the formation and persistence of the inboard conical LEV structure. This criterion identified the LEV as either conical ,non-conical or transitional. Previous studies inspired the proposal of a ”rotation parameter” ,ΠRot, that was a based on a non-dimensional velocity gradient. A value of ΠRot = 0.17 was found to separate conical and non-conical LEV parameter, suggesting the fundamental importance of this parameter to LEV dynamics. Furthermore, the forces were analyzed to understand the impact of the flow structure on the forces. The conical LEVs had a transient peak followed by irregular udulations while the non-conical LEVs produced high frequency oscillations. In both cases, the force could be understood based on the time-evolution of the LEVs. Passive bleeding was considered within this study to perturb the flow. Four passive bleed configurations were experimented with at different hole locations and sizes. It was found that a hole applied near the wing root with a large diameter perturbed the flow and transformed the structure from conical to non-conical classifications. This provides a platform to further understand the flow mechanisms that govern LEV formation and evolution by drastically changing flow structures and maintaining the same geometric and kinematic parameters. Additional studies were done analyzing the changes on the forces on the wing. The lift on the passive bleeding did not seem to be affected however, the thrust was decreased to nearly 0.

Es werden die Parameter Summe-G2/GF und G0/G1 der hochauflösenden, zweiparametrigen Zellzyklusanalyse von Lymphozyten bei Fanconi-Anämie-Patienten, bei denen mehrere Meßwerte vorliegen, im Hinblick auf Schwankungen untersucht. Nach Auswertung der Daten stellen die Werte keine konstanten Parameter für den einzelnen Patienten dar. Die Langzeitanalyse des Zellzyklusverhaltens peripherer Blutlymphozyten reflektiert jedoch weitgehend die klinische Situation der Patienten. / Two Parameter - Sum of G2/GF and G0/G1 - of twoparametric, high definition Cellcycle Analyses from Lymphocytes of Fanconi-Anemia-Patients were examined over the time. After Evaluation of Data it became clear, that Sum G2/GF and G0/G1-Parameter are not constant for each Patient over the time. But the longtime Analyses of peripheric Lymphocytes showed that they almost represent the clinical Situation of a Patient.

In finance, various stochastic models have been used to describe the price movements of financial instruments. After Merton's [38] seminal work, several jump diffusion models for option pricing and risk management have been proposed. In this dissertation, we add alpha-stable Levy motion to the process related to dynamics of log-returns in the Black-Scholes model where the volatility is assumed to be constant. We use the sample characteristic function approach in order to study parameter estimation for discretely observed stochastic differential equations driven by Levy noises. We also discuss the consistency and asymptotic properties of the proposed estimators. Simulation results of the model are also presented to show the validity of the estimators. We then propose a new model where the volatility is not a constant. We consider generalized alpha-stable geometric Levy processes where the stochastic volatility follows the Cox-Ingersoll-Ross (CIR) model in Cox et al. [9]. A number of methods have been proposed for estimating parameters for stable laws. However, a complication arises in estimation of the parameters in our model because of the presence of the unobservable stochastic volatility. To combat this complication we use the sample characteristic function method proposed by Press [48] and the conditional least squares method as mentioned in Overbeck and Ryden [47] to estimate all the parameters. We then discuss the consistency and asymptotic properties of the proposed estimators and establish a Central Limit Theorem. We perform simulations to assess the validity of the estimators. We also present several tables to show the comparison of estimators using different choices of arguments ui's. We conclude that all the estimators converge as expected regardless of the choice of ui's. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Analytical performance analysis on Direction-Of-Arrival (DOA) estimation algorithms has attracted much excellent research in recent years, various statistical properties have been revealed. However, in most of these analyses, insights of the performance were masked because of the involvement of singular values and singular vectors which depend on the character of the algorithms and data structures in a complex and nonlinear manner.

The estimation of sinewave parameters has many practical applications in test and data processing systems. Measuring the effective bits of an analog-to-digital converter and linear circuit identification are some typical examples. If a sinew ave's frequency is known, there is an established linear method to estimate the other parameters. But when none of the parameters are known (which is usually the case in practical situations), the estimation problem becomes more difficult. Traditional approaches to this task applied an iterative, sinewave curve-fit algorithm. Two problems with this technique are that convergence is often slow and not always guaranteed and the results of different trials may be inconsistent due to trapping at a local minimum. Recently, a non-iterative algorithm has been developed which computes all four sine wave parameters directly. The algorithm combines a nonlinear technique and windowing to compute the estimates. Although this method is faster and more consistent than the curve-fit approach, one disadvantage is that the accuracy of some estimates tends to deteriorate rapidly if the sinusoid is corrupted by a high level of noise distortion. This study presents an improved algorithm to extract the four parameters of an unknown sinusoid from a sampled data record even though the samples may be distorted by a high level of noise. Given this record, the proposed method first computes the FFT (Fast Fourier Transform) of the data. Analysis of the resulting frequency spectrum provides a rough estimate of the sinewave's fundamental frequency. Next, a bandpass filter designed around this frequency is used to eliminate much of the noise from the samples. Applying the existing four-parameter estimation algorithm to the filtered data, yields a more accurate frequency estimate. Finally, this new value, together with the original (noisy) data record is input to the three-parameter estimation algorithm to determine the remaining sinewave parameters. Simulation results indicate this proposed (new) algorithm not only shows substantial improvement in the accuracy of parameter estimates, but also produces consistent results for higher levels of noise distortion than previous methods have achieved.

If either the arrival rate or the service rate in an M/M/S queue exhibit variability over time, then no steady state solution is available for examining the system behavior. The arrival and service rates can be represented through Fourier series approximations. This permits numerical approximation of the system characteristics over time. An example of an M/M/S representation of the operations of emergency treatment at Logan Regional hospital is presented. It requires numerical integration of the differential equation for L(t), the expected number of customers in the system at time t.

Some highway agencies in the United States are experiencing frequent distresses in asphalt pavements on bridge approaches/departures. Commonly observed distresses include alligator cracking and rutting, which reduce roadway smoothness and safety. To lessen the distresses in pavements it is needed to investigate the extent and root causes of the problem. Based on Florida highway conditions, this research study mainly focused on1. Literature review and identification of the extent of the problem; 2. Collection of relevant pavement condition data and descriptive analysis; 3. Development of statistical models to determine factors influencing the distresses in asphalt pavements on bridge approaches/departures. To the best of my knowledge, this is the first study that uses a statistical model to determine the factors that are responsible for causing asphalt pavement distresses on bridge approaches/departures. As part of the literature review, a nationwide questionnaire survey was targeted towards U.S state DOTs. The data collection and analysis specific to the Florida highways found that in 2015 on Florida Interstate highways, about 27% bridges with asphalt pavements on their approaches/departures showed signs of cracking, and about 20% bridges have noticeable rutting in their approach or departure pavements. A random parameter linear regression model was applied to examine the factors that may influence distresses in asphalt pavements in Florida. Pavement condition was evaluated based on the Florida Department of Transportation (FDOT) 2015 pavement condition data and video log images, and other relevant data were collected from various sources such as FDOT Roadway Characteristics Inventory (RCI) database, FDOT pavement management reports, and FDOT Ground Penetrating Radar (GPR) survey reports. A constraint existed in the availability of the GPR data that can give pavement layer thickness, which limited the number of bridge approach pavement sections included in the statistical modeling. Based on the limited data, the estimated results from the random parameter linear regression model showed that the variables influencing distresses in asphalt pavements on bridge approaches/departures, in terms of rutting and roughness, may include pavement age, annual average daily truck traffic, and surface friction course.

This thesis focusses on the development and testing of optimization methods for parameter identification in cardiac electrophysiology models. Cardiac electrophysiology models are systems of differential equations representing the evolution of the trans-membrane potential of cardiac cells. The Mitchell-Schaeffer model is chosen for this thesis. The parameters included in the Mitchell-Schaeffer model are optimally adjusted so that the solution of the model has desired properties. Two optimization problems are formulated using least-square functions to identify parameters that match phase durations and parameters that fit entire potential recordings of swine heart tissue acquired via optical imaging techniques at different stimulation frequencies. The non-differentiable optimization methods (Compass Search and three other variants) are applied to solving both optimization problems for two reasons; First, the methods are studied to evaluate performance and second, the optimization process is evaluated to confirm its ability to identify parameters for the Mitchell-Schaeffer model.

This thesis is mainly concerned with the estimation of parameters of a first-order Smooth Threshold Autoregressive (STAR) model with delay parameter one. The estimation procedures include classical and Bayesian methods from a parametric and a semiparametric point of view.As the theoretical importance of stationarity is a primary concern in estimation of time series models, we begin the thesis with a thorough investigation of necessary or sufficient conditions for ergodicity of a first-order STAR process followed by the necessary and sufficient conditions for recurrence and classification for null-recurrence and transience.The estimation procedure is started by using Bayesian analysis which derives posterior distributions of parameters with a noninformative prior for the STAR models of order p. The predictive performance of the STAR models using the exact one-step-ahead predictions along with an approximation to multi-step-ahead predictive density are considered. The theoretical results are then illustrated by simulated data sets and the well- known Canadian lynx data set.The parameter estimation obtained by conditional least squares, maximum likelihood, M-estimator and estimating functions are reviewed together with their asymptotic properties and presented under the classical and parametric approaches. These estimators are then used as preliminary estimators for obtaining adaptive estimates in a semiparametric setting. The adaptive estimates for a first-order STAR model with delay parameter one exist only for the class of symmetric error densities. At the end, the numerical results are presented to compare the parametric and semiparametric estimates of this model.

<p>To determine parameter values for models of reactions in the human body, like the glycolysis, good methods of parameter estimation are needed. Those models are often non-linear and estimation of the parameters can be very time consuming if it is possible at all. The goal of this work is to test different methods to improve the calculation speed of the parameter estimation of an example system. If the parameter estimation speed for the example system can be improved it is likely that the method could also be useful for systems similar to the example system.</p><p>One approach to improve the calculation speed is to construct a new cost function whose evaluation does not require any simulation of the system. Simulation free parameter estimation can be much quicker than using simulations to evaluate the cost function since the cost function is evaluated many times. Also a modication of the simulated annealing optimization method has been implemented and tested.</p><p>It turns out that some of the methods significantly reduced the time needed for the parameter estimations. However the quick methods have disadvantages in the form of reduced robustness. The most successful method was using a spline approximation together with a separation of the model into several submodels, and repeated use of the simulated annealing optimization algorithm to estimate the parameters.</p>