Theses and Dissertations

Fall 2012

Lin, Feng

potentials were effectively uniform. These two effects contributed to a more random distribution of zeta potentials on the bitumen droplet surfaces. Ultimately, it is the randomness of zeta potential, together with its average value (measured, for example, by electrophoresis), that determine the probability

) sliding along the surface of a much larger droplet (effectively a flat surface). Procedures were developed to allow direct quantification of the probabilities of coalescence between the two oil drops. The experimental parameters include: zeta potential of the bitumen drops (through manipulation of

solution pH and calcium ion concentration in the electrolyte), distance of shear contact, and shear speed. These parameters were varied to observe their effects on the probabilities of drop coalescence. Contrary to traditional DLVO theory, it was demonstrated that the coalescence of bitumen droplets was

Spring 2016

Rogeau, Marie-Pierre

583 fire scars were identified. Results showed natural subregions had different fire interval distributions before 1948 and some level of FRI variance was also observed within a subregion. The median FRI for the Montane and Foothills sampling units ranged from 26 to 39 years, while the sampling unit

located in the most rugged portion of the Subalpine had a median FRI of 85 years. Other aspects of the fire regime were also documented for the three natural subregions including: severity, seasonality and cause. These results revealed an important anthropogenic influence on the amount and spatial

distribution of burning prior to 1948. In contrast, the effective fire suppression measures taken since 1948 resulted in a substantial departure of 167% to 223% (median FRI = 84 to-104 years) for the Montane and Foothills, while the rugged Subalpine was found to be within its natural range of variation with a

Fall 2018

Huang, Jiarao

conditional probability distribution of the state and energy is obtained based on the event-triggered information received at the remote estimator under the energy-dependent measurement transmission policy. The robust state estimation problems are investigated for linear Gaussian systems with event

resources of cyber-physical systems, two event-based state estimation problems are formulated and solved for systems described by hidden Markov models utilizing a new reference measure approach with the change of probability measure. For a linear Gaussian system with an energy harvesting sensor, the joint

-triggered scheduling and systems with unknown exogenous inputs utilizing the risk-sensitive approach, where closed-form risk-sensitive state estimates are derived. A fully distributed robust consensus-based filtering algorithm for systems measured by a sensor network is proposed with stability analysis on

Spring 2022

Tschritter, Dana

, and the corresponding concentrations per bird. Changes to prevalence and concentration at each node were modeled from peer-reviewed literature and federal surveillance data. The final estimate was the probability distribution of consuming a serving of broiler meat with Campylobacter or CIPR

a serving. Multiplying this probability of illness per serving by the total number of servings consumed annually and scaling by Canadian population yielded the estimated incidence illnesses. Lastly, sensitivity and scenario analyses were performed on the model described above. The sensitivity

probabilities of CIPS Campylobacter versus CIPR Campylobacter was used to predict if an infection would be resistant to ciprofloxacin. A conditional probability was used to further estimate the probability of symptomatic illness given an infection. The risk characterization had two major metrics, estimated for

Spring 2024

Mao, Yiren

locations. A binary relevance 3D CNN-LSTM autoencoder, employing different loss functions, showed marginal improvement but struggled to predict probability locations over a large horizon. Models trained on principal component analysis (PCA)-transformed and dynamic PCA (DPCA)-transformed data showed promise

in training but failed in testing. Models trained on PDFs without "dead voxels" (zero probability voxels independent of time) and atomic Cartesian coordinates perform well during training but encounter challenges in testing due to teacher forcing. Teacher forcing is a training method that can

. This work focuses on the development of machine learning (ML) models as proxy models for Car-Parrinello molecular dynamics (CPMD) metadynamics simulations in condensed-phase biomass reactions. Explicit solvation CPMD metadynamics simulation data of HMF undergoing protonation in a solution of dimethyl

Spring 2012

Rivasplata, Omar D

In this thesis probability estimates on the smallest singular value of random matrices with independent entries are extended to a class of sparse random matrices. We show that one can relax a previously used condition of uniform boundedness of the variances from below. This allows us to consider

matrices with null entries or, more generally, with entries having small variances. Our results do not assume identical distribution of the entries of a random matrix, and help to clarify the role of the variances in the corresponding estimates. We also show that it is enough to require boundedness from

Fall 2014

Gonzalez, Ruben T

another technique is a modification on Dempster-Shafer Theory. Evidence in previous work was considered to be a vector of discrete variables, and the resulting probability estimates consisted of discrete categorical distributions. However, most monitors have continuous outputs that are only discretized

While there has been much literature in the area of system monitoring and diagnosis, most of these techniques have a relatively small scope in terms of the faults and performance issues that they are built to detect. When implementing several monitors simultaneously on a single process, a single

problem can result in multiple alarms, making it difficult to single out the underlying cause. Recent work has been done on incorporating information from multiple monitoring systems by means of Bayesian diagnosis; however, work so far is still in its infancy. This thesis focuses on a number of techniques

Fall 2022

Ma,Chengran

detect the presence of FDIAs, a novel FDIA detection scheme is proposed in this thesis for three-phase distribution systems based on zero-sequence voltage (ZSV). From the voltage and power measurements, the bus voltages are estimated, and then the estimated ZSV is calculated as the sum of the estimated

bus voltages on the three phases to represent the degree of unbalance of the distribution system. Via mathematical analysis of the linear distribution system state estimation (DSSE) model, the distribution of the estimated ZSV under the normal condition is derived, based on which a whitening process

is adopted on the estimated ZSV to weaken the effect of measurement noises. The L2-norm of the whitened ZSV vector is then compared with a predefined threshold for the FDIA detection. Moreover, the probability of false alarm of the proposed scheme is derived, which can be utilized to determine the

Fall 2021

Kor, Yashar

Weather-related power outages in the distribution grid have a significant impact on the grid reliability - they impose a high cost on power utilities and considerable inconvenience to customers. Improvements in monitoring and data collection practices, as well as advanced data processing methods

based on Dempster-Shafer theory (DST), as well as Knowledge Graph-based representation of distribution grid topology (GridKG) suitable for integration of data characterizing different aspects of the distribution system. Three different architectures of a system for predicting types of weather-related

outages are proposed and evaluated. Weather and outage data are utilized for model development and evaluation of their performances. The developed system is capable of identifying the probability of outage occurrences with a focus on identifying outages caused by extreme wind, wet snow, and icing. An

Spring 2010

Shi, Jichuan

. Shape attribute distributions are learned from typical objects in ground truth images. Local threshold for each object in an image to be segmented is chosen to maximize probabilities in these shape attributes distributions. Then for the application of the oil sand image segmentation, a supervised

This thesis is concerned with a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most local threshold algorithms are based only on intensity analysis. In many applications where an image contains objects with a similar shape, in

addition to the intensity information, some prior known shape attributes could be exploited to improve the segmentation. The goal of this work is to design a local threshold algorithm that includes shape information to enhance the segmentation quality. The algorithm adaptively selects a local threshold