Theses and Dissertations

Fall 2016

Kang, Chao

well with that from the numerical experiments. In order to use the new entrainment model into debris flow runout calculation, the new entrainment model has been incorporated in a runout model based on an energy approach. Entrainment calculation governed by a second order partial differential equation

failure mechanism. A new analytical model is proposed to calculate entrainment in debris flow analysis by considering both rolling and shearing motion. Newton’s Law of Motion is used to calculate accelerations, velocities, and displacements of granular particles. To study the entrainment process inside

granular flow and to verify the new entrainment model, numerical experiments have been carried out using the Discrete Element Method (DEM). Velocities, including translational velocity, rotational velocity and average velocity, total volume, shear stresses are monitored using measurement circles in the

Fall 2019

Ma, Yanjun

thesis forms a transition function for the constrained latent features. As a hierarchical extension of the hidden Markov model, it describes a dynamic model for the probabilities of discrete variables. By using the Beta distribution to replace the Gaussian distribution, the novel transition function

. Besides several probability models, novel inferencing algorithms are elaborated for different application scenarios. In most chemical processes, features with large inertia and small varying velocity are believed to be more informative. By imposing this modelling preferences as prior distributions of

model parameters, the first contribution of this thesis builds the dynamic latent features under a fully Bayesian framework. The preference for large inertia is implemented through a constraint and a prior distribution for the dynamic model of latent features, namely the transition function. The

Spring 2014

Duan, Ping

industrial process data. A new information theory-based distribution-free measure, transfer 0-entropy, is proposed for causality analysis based on the definitions of 0-entropy and 0-information without assuming a probability space. For the cases of more than two variables, a direct transfer 0-entropy concept

method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. A key assumption for the transfer entropy method is that the sampled data should follow a well-defined probability distribution; yet this assumption may not hold for all types of

detection plays a significant and central role. This thesis focuses mainly on information theory-based approaches for causality analysis that are suitable for both linear and nonlinear process relationships. Previous studies have shown that the transfer entropy approach is a very useful tool in quantifying

Spring 2011

Zhao, Cailu

Unit (GPU) on graphic cards has enabled us to develop real-time interactive simulators of complex physical phenomenon. In this thesis, two GPU-based implementations of interactive physical simulations are presented: (1) visualization of the electron probability distribution of a hydrogen atom, (2

) visualization and simulation of particle based fluid dynamic model using smoothed particle hydrodynamics. These simulations were developed in the context of the Microscopic and Subatomic Visualization (MASAV) project as a demonstration of the capabilities of the GPU to create realistic interactive physical

Fall 2011

Qi, Fei

The large number of control loops in a modern industrial plant poses a serious challenge for operators and engineers to monitor these loops to maintain them at optimal conditions continuously. Much research has been done on control loop performance assessment and monitoring of individual components

missing pattern concept is introduced. The incomplete evidence problems are categorized into single missing pattern ones and multiple missing pattern ones. A novel method based on marginalization over an underlying complete evidence matrix (UCEM) is proposed to include the incomplete evidences into the

under the Bayesian framework. An approach to estimate the distributions of monitor readings with sparse historical samples is proposed to alleviate the intensive requirement of historical data. The statistical distribution functions for several monitoring algorithm outputs are analytically derived. A

Fall 2018

Chen, Chunlin

any attempts on the theoretical analysis of the underlying point process model. In addition, extensions of the analytical methodology used in Poisson models to more general point process models are often hindered due to the lack of closed-form empty space function and the probability generating

studied the distributional properties of the empty space distances in the Matérn hard core point process of Type II, and proposed a piecewise probability density function for the empty space distance, including an exact expression and a heuristic formula, which can be fitted by aWeibull-like function

Stochastic geometry provides a way of defining and computing macroscopic properties of large scale wireless networks, by averaging over all possible spatial patterns of the network nodes. It abstracts the network as realizations of point process models, and analyzes the network performance in a

Fall 2018

Li, Ruoxia

develop an estimator for the state PDF of arbitrary distribution. In this work, we develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter (EnKF) specifically for estimation with multimodal state distributions.The second problem is that the conventional

a Gaussian distribution. This presents a challenge for Kalman-based state estimators such as the extended Kalman filter, since they model the state PDF as Gaussian. In order to achieve more accurate estimation, the modeling of the state distribution needs to be improved. The first problem is to

work, we develop a novel state estimation technique to incorporate inequality constraints for the case of Gaussian filters. Furthermore, we consider the constrained estimation for the case where the state PDF cannot be approximated with a Gaussian distribution. To this end, we develop a framework to

Spring 2011

Wang, Chonghai

mechanisms investigated for what are called uncertain databases or probabilistic databases, where a tuple is associated with a membership probability indicating the level of confidence on the stored information. In this thesis, we study top-k ranking with uncertain data in two general areas. The first is on

show experimentally that pruning can generate orders of magnitude performance gains. In the second area of our investigation, we study the problem of top-k ranking for objects with multiple attributes whose values are modeled by probability distributions and constraints. We formulate a theory of top-k

The goal of top-k ranking is to rank individuals so that the best k of them can be determined. Depending on the application domain, an individual can be a person, a product, an event, or just a collection of data or information for which an ordering makes sense. In the context of databases, top-k

Spring 2019

Prybysh, Robert

to establish a cumulative distribution function of the direct comparison of long-term costs between two systems. This allows the evaluator to not only establish the probability that one system will have a lower life cycle cost over another system, but also the degree of savings.

residential buildings of various sizes. Although the technique has been utilized for many years, the performance and efficiency, the effects of using potable water as a hydronic medium on water quality, and the long-term operational cost implications have yet to be explored through dedicated research. This

building. This involves establishing the building efficiency as steady state efficiency and a standby loss, a methodology previously presented for individual appliances, but not explored for both the heating and cooling performances of complete building systems. The impact on the palatability of the water

Fall 2009

Hosseini, Amir Hossein

uncertainty band that meets the requirements of unbiasedness and fairness of the calibrated probabilities. The second development in this thesis is related to a probabilistic model for characterization of uncertainty in the 3D localized distribution of residual NAPL in a real site. A categorical variable is

source geometry and hydraulic conductivity distribution. The central idea in this thesis is to develop a flexible modeling approach for characterization of uncertainty in residual NAPL dissolution rate and first-order biodegradation rate by tailoring the estimation of these parameters to distributions of

defined based on the available CPT-UVIF data, while secondary data based on soil texture and groundwater table elevation are also incorporated into the model. A cross-validation study shows the importance of incorporation of secondary data in improving the prediction of contaminated and uncontaminated