Digital Commons Network^™

Dynamic Data-Driven Smart Proxy Modeling For Numerical Reservoir Simulation, Maher Jasim Alabboodi

Graduate Theses, Dissertations, and Problem Reports

A successful Geologic Carbon Dioxide (CO₂) Storage (GCS) operation requires the ability to make quick and reliable subsurface modeling decisions; such decisions must be made based on an accurate and realistic modeling of the reservoir. Numerical reservoir simulation is the most common tool used for predicting fluid flow behavior and analyzing uncertainties in the subsurface reservoirs.

In general, a numerical reservoir simulation model has tens of millions of grid blocks and requires intensive computations to be performed at each time-step of the simulation, therefore, they are computationally expensive and time-consuming. As a result, studies (such as uncertainty analysis …

Prediction Of Tensile Behaviors Of L-Ded 316 Stainless Steel Parts Using Machine Learning, Israt Zarin Era

Graduate Theses, Dissertations, and Problem Reports

Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed and specimen height, play a great deal in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, a data driven machine learning model XGBoost has been built and applied to predict the …

Next-Generation Re-Entry Aerothermodynamic Modeling Of Space Debris Using Machine Learning, Nicholas Sia

Graduate Theses, Dissertations, and Problem Reports

The number of resident space objects re-entering the atmosphere is expected to rise with increased space activity over recent years and future projections. Predicting the survival and impact location of the medium to large sized re-entering objects becomes important as they can cause on ground casualties and damage to property. Uncertainties associated with the re-entry process makes necessary a probabilistic approach, which can be computationally expensive when using high-fidelity numerical methods for estimating aerothermodynamic properties. To date, object-oriented analysis is the dominant tool used for atmospheric re-entry modeling and simulation, where aerothermodynamic coefficients are used to determine the risk a …

Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani

Graduate Theses, Dissertations, and Problem Reports

Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …

Association Of Incident Cancer To Low-Value Care And Healthcare Cost Burden Among Elderly Medicare Beneficiaries, Chibuzo Iloabuchi

Graduate Theses, Dissertations, and Problem Reports

In the United States (US), 25% of healthcare spending is considered wasteful because it is spent reimbursing low-value care. Low-value care is the utilization of healthcare services, medical tests, and procedures that have unclear or no clinical benefit to patients but still exposes them to risk. World-wide, low-value care imposes a significant economic burden on patients, payers, governments, and society. Cancer care among older adults > 65 years is one of the biggest drivers of healthcare expenditure in the US and accounts for nearly 40% of all spending, and low-value care among cancer patients is prevalent and contributes to the financial …

Economic Burden Of Low-Value Healthcare On Patients With Localized Prostate Cancer: Statistical & Machine Learning Approaches, Ryan Fiano

Graduate Theses, Dissertations, and Problem Reports

Adults with incident localized prostate cancer represent a large, medically complex population at risk for low-value care. Evidence-based guidelines recommend conservative management (CM) for localized prostate cancer patients with multimorbidity and limited life expectancy, however, 2 in 3 still choose treatment. This dissertation pursued three Aims to address research gaps related to healthcare practices associated with significant morbidity and economic burden on older men with incident localized prostate cancer: 1) examine the leading predictors of low-value healthcare practice of prostate cancer treatment for low-risk prostate cancer; 2) assess the role of patient‐reported experience with care on high-value prostate cancer management; …

Identification Of Moving Bottlenecks In Production Systems, Funmilayo Mofoluwasola Adeyinka

Graduate Theses, Dissertations, and Problem Reports

Manufacturing sector have been plagued by bottlenecks from time immemorial, leading to loss of productivity and profitability, various research effort has been expended towards identifying and mitigating the effects of bottlenecks on production lines. However, traditional approaches often fail in identifying moving bottlenecks. The current data boom and giant strides made in the machine learning field proffers an alternative means of using the large volume of data generated by machines in identifying bottlenecks. In this study, a hierarchical agglomerative clustering algorithm is used in identifying potential groups of bottlenecks within a serial production line.

A serial production line with five …