Theses and Dissertations

Spring 2019

Gallardo Vizcaino, Enrique

. This requires transferring the geological uncertainty to probability distributions of a response variable suitable for decision-making and use of a decision criterion that considers the reservoir manager’s preferences toward the project’s return-risk trade-off. This is challenging in petroleum

transfer concepts and ideas from discrete simulation. It works on homogeneous and heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations. A case study performed with a realistic multi-realization geological model validates the predictive

making under geological uncertainty. MVC-SDR does not rely on a specific utility function and leads to decisions that are considered rational to risk-averse reservoir managers. The shortcoming is a reduced ability to rank projects with very similar value. Two examples illustrate the use of MVC-SDR, the

Fall 2016

Sparkes, Shantel N

-1990 and inside the Monument from 2000-2010. We discussed trends in estimates of elk carrying capacity to trends in the elk summer distribution, body condition, probability of pregnancy, and overwinter elk mortality across a portion of the study area and found a general correspondence. Results from

/ha) within a core area of the Mount St. Helens elk population since the 1980 eruption based on digestible energy of preferred forage species using the Forage Resource Evaluation System for Habitat model (FRESH). I constrained estimates of NCC by considering only areas with a minimum amount of

indicated elk selection was most strongly influence by available digestible energy, followed by distance to forage-cover edge, distance to a public road and slope. Constraining the NCC by relative use resulted in 2-49% decrease across study years with the greatest declines on industrial lands from 1980

Fall 2020

Fan, Lei

features can be extracted and the impact of outliers is alleviated by the latent variance scale. The next contribution of this thesis is to develop a semi-supervised model based on probability slow feature analysis to include the information from quality variables in the extracted latent features while

information of the process. With a probabilistic formulation, dynamic latent variable models, based on extracting slowly varying features, are developed in this thesis to address the aforementioned data irregularities, thus give reliable prediction results of quality variables that are otherwise difficult to

-distribution that has heavier tails, more weights can be assigned to the outliers thus they can be properly accounted for during modeling process. In feature extraction phase, a weighted Kalman gain is proposed since it violates the Gaussian assumption of the traditional Kalman filter. Smoother and slower

Spring 2014

Hadiuzzaman, Md

(CLs) and a predictive VSL control with different CLs to improve traffic flows. Several CL-to-VSL strategies are modeled with a fixed co-efficient of variance of speeds obtained from static speed limit on WMD. The CLs include speed distributions for aggressive, compliant, and defensive drivers. It is

data confirmed that, compared to the existing models, the proposed model better simulates traffic flow. With the validated model, this research investigates the impact of control parameters and demand levels on total travel time and throughput under the coordinated VSL control and determined a range of

control strategy on safety constraints and VSL update frequencies demonstrates promising results to support practical implementation. Considering its flexible use in macroscopic simulation, a 1st order traffic flow model, CTM-VSL, is proposed. Unlike the 2nd order models, it is parsimonious: it only

Fall 2020

Peng Zhuang

Battery energy storage systems (BESSs) are vital for improving the sustainability, efficiency, and resiliency of smart distribution systems (SDSs). With the proper energy management, BESSs can provide a wide range of applications for both demand-side and grid-scale services in SDSs. However, there

BESSs are included for the sustainable charging with reduced costs. By treating EBCSs as energy prosumers, the day-ahead dynamic prices are used to mitigate charging impacts. This problem is formulated as a distributionally robust MDP (DRMDP) to address the inaccuracies of probability density function

against BESSs in SDSs are analyzed. More specifically, a numerical model of false data injection attacks (FDIAs) against distribution system states estimation (DSSE) of SDSs is developed, which is used to construct stealthy cyber attacks targeting system information integrity of SDSs. In the developed

Fall 2015

DeMars, Craig A

The boreal ecotype of woodland caribou (Rangifer tarandus caribou) is federally listed as Threatened due to population declines throughout its distribution. High mortality rates of neonate calves (≤ 4 weeks old) due to predation are a key demographic factor contributing to population declines and

of boreal caribou with parturient females dispersing widely on the landscape, a behaviour hypothesized to reduce predation risk. I assessed potential evolutionary drivers of dispersion using simulation analyses that tracked caribou-wolf encounters during the calving season. I specifically assessed

relating maternal selection and use of resources to the probability of neonate survival. These analyses included spatially explicit covariates of predator-specific risk. Surprisingly, variation in landscape disturbance had minimal effect on calf survival; rather, survival was best explained by predation

Spring 2022

Puliyanda, Anjana Thimmaiah

from the reactant to the product configurations. A self-supervised 3D convolutional neural network autoencoder is trained to extract features from the reactant and product simulation trajectories, the probability distributions across the difference between which is used to assess if the solvent

Processing of complex feedstocks for the production of value-added chemicals and fuels is industrially important. The lack of a priori knowledge of the innumerable species and the reaction pathways governing their conversion, has posed challenges to monitoring these processes. Although, data-driven

models have been used, their lack of interpretability and an end-to-end modeling framework has limited the efficiency of diagnostic decisions in process monitoring. On the other hand, systems where the mechanistic knowledge of the species and their reactions are arrived at from first-principles

Fall 2021

Mohamed, Emad

) developing an integrated simulation approach for assessing risks in onshore wind projects that considers both the cost and time impact of risks; (5) proposing a method for deriving probability distributions of a risk factor’s impact using fuzzy logic and multivariate analysis to enhance input modelling for

Wind energy is emerging as a primary source of renewable energy in Canada, attracting over $23 billion in investment. Steadily increasing, a total capacity of 31,640 MW of wind energy must be installed by 2040 to meet the requirements of the Paris Agreement on Climate, requiring the construction of

projects—particularly in the Canadian wind energy sector. In particular, the identification of project-specific (i.e., contextual) risk factors still relies heavily on traditional risk identification techniques that are demanding in terms of time and effort. This, together with a lack of historical data

Fall 2019

Kammammettu, Sanjula

uncertainty realized and presents a conservative solution to the problem that would be valid for any realization of uncertainty it was solved for. In contrast, stochastic optimization deals with uncertainty in an optimization problem by assuming that the probability distribution of the uncertainty is known

presents an additional layer of complexity owing to the presence of uncertainty in the operation of the system. This uncertainty may come from a variety of sources, such as effluent flow rate, contaminant concentration, and treatment unit removal efficiency. Therefore, the need to focus on developing a

The optimal design and operation of effluent treatment system networks poses a significantcant challenge in the present time, with the imposition of stricter environmental regulations and an increased demand for resources exacerbated by a diminishing resource pool. In practice, this problem

Fall 2015

Hameed, Zohaib

leakage current to prevent the loss of previously stored energy. Design strategy to optimize the impedance matching circuit of an RF energy harvester to maximize the harvested power for a range of received power levels with known probability density distributions is presented. Optimization of the RF

wireless sensors. This research is focused on the design of an integrated energy harvesting system in CMOS technology. The main objective of the research is to improve the sensitivity and RF-to-DC power conversion efficiency (PCE) of the RF rectifiers while providing a large output voltage from low

implemented on a customized printed circuit board (PCB). The proposed adaptive threshold voltage compensated rectifier circuit achieves a maximum PCE of 32% at an input power of -15 dBm (32 µW) with an output DC voltage of 3.2 V for a 1 MΩ load. At a remarkably low input power of -21.6 dBm (6.9 µW) for a 1 MΩ