Physical Sciences and Mathematics Commons^™

An Investigation Into Applications Of Canonical Polyadic Decomposition & Ensemble Learning In Forecasting Thermal Data Streams In Direct Laser Deposition Processes, Jonathan Storey

Theses and Dissertations

Additive manufacturing (AM) is a process of creating objects from 3D model data by adding layers of material. AM technologies present several advantages compared to traditional manufacturing technologies, such as producing less material waste and being capable of producing parts with greater geometric complexity. However, deficiencies in the printing process due to high process uncertainty can affect the microstructural properties of a fabricated part leading to defects. In metal AM, previous studies have linked defects in parts with melt pool temperature fluctuations, with the size of the melt pool and the scan pattern being key factors associated with part defects. …

Direct Measurement Of The 114cd(��, ��)115cd Cross Section In The 1 Ev To 300 Kev Energy Range, Kofi Tutu Addo Assumin-Gyimah

Theses and Dissertations

The large thermal cross section of cadmium makes it ideal for many practical applications where screening of thermal neutrons is desired. For example, in non-destructive assay techniques, or for astrophysical studies of the s-process. All such applications require precise knowledge of the neutron-capture cross section on cadmium. Although there are some data on neutron-capture cross sections particularly at thermal energies and at energies relevant for astrophysics, there is very little data at most other energies. Further, the evaluated cross sections from the ENDF and JENDL databases disagree at high energies. Therefore, there is a critical need for precise knowledge of …

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

Towards Structured Planning And Learning At The State Fisheries Agency Scale, Caleb A. Aldridge

Theses and Dissertations

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can …

Three Case Studies Of Using Hybrid Model Machine Learning Techniques In Educational Data Mining To Improve The Classification Accuracies, Sujan Poudyal

Theses and Dissertations

A multitude of data is being produced from the increase in instructional technology, e-learning resources, and online courses. This data could be used by educators to analyze and extract useful information which could be beneficial to both instructors and students. Educational Data Mining (EDM) extracts hidden information from data contained within the educational domain. In data mining, hybrid method is the combination of various machine learning techniques. Through this dissertation, the novel use of machine learning hybrid techniques was explored in EDM using three educational case studies. First, in consideration for the importance of students’ attention, on and off-task data …

Impact Of Climate Oscillations/Indices On Hydrological Variables In The Mississippi River Valley Alluvial Aquifer., Meena Raju

Theses and Dissertations

The Mississippi River Valley Alluvial Aquifer (MRVAA) is one of the most productive agricultural regions in the United States. The main objectives of this research are to identify long term trends and change points in hydrological variables (streamflow and rainfall), to assess the relationship between hydrological variables, and to evaluate the influence of global climate indices on hydrological variables. Non-parametric tests, MMK and Pettitt’s tests were used to analyze trend and change points. PCC and Streamflow elasticity analysis were used to analyze the relationship between streamflow and rainfall and the sensitivity of streamflow to rainfall changes. PCC and MLR analysis …

A Modular Synthesis Of Processable And Thermally Stable Semi-Fluorinated Aryl Ether Polymers Via Step-Growth Polymerization Of Fluoroalkenes, Ketki Eknath Shelar

Theses and Dissertations

Tailored fluoropolymers remain the leading choice for a wide variety of advanced high-performance applications, including electronic/optical and energy conversion, owing to their unique blend of complementary high-performance properties. Amorphous semi-fluorinated polymers exhibit improved solubility and melt processability when compared to traditional perfluoropolymers. A leading class of semi-fluorinated aryl ether polymers includes perfluorocyclobutyl (PFCB), perfluorocycloalkenyl (PFCA), and fluoroarylene vinylene ether (FAVE) polymers. Monomers containing aromatic trifluorovinyl ethers (TFVE) are used to synthesize PFCB polymers via radical-mediated [2+2] cyclodimerization. On the other hand, FAVE and PFCA polymers are polymerized via base-mediated nucleophilic addition/elimination of bisphenols with TFVE monomers and decafluorocyclohexene respectively. The …

Photocatalytic Degradation Of Organic Contaminants By Titania Particles Produced By Flame Spray Pyrolysis, Noah Babik

Theses and Dissertations

Advanced oxidation of organic pollutants with TiO2 photocatalysts is limited due to the wide bandgap of TiO2, 3.2 eV, which requires ultraviolet (UV) radiation. When nanosized TiO2 is modified by carbon doping, charge recombination is inhibited and the bandgap is narrowed, allowing for efficient photodegradation under visible light. Here, we propose a flame spray pyrolysis (FSP) technique to create TiO2. The facile process of FSP has been successful in preparing highly crystalline TiO2 nanoparticles. Using the same procedure to deposit TiO2 onto biochar, the photocatalyst was doped by the carbonaceous material. The morphology, crystalline and electronic structure of the FSP …

Direct Simulation And Reduced-Order Modeling Of Premixed Flame Response To Acoustic Modulation, Zheng Qiao

Theses and Dissertations

This dissertation introduces a general, predictive and cost-efficient reduced-order modeling (ROM) technique for characterization of flame response under acoustic modulation. The model is built upon the kinematic flame model–G-equation to describe the flame topology and dynamics, and the novelties of the ROM lie in i) a procedure to create the compatible base flow that can reproduce the correct flame geometry and ii) the use of a physically-consistent acoustic modulation field for the characterization of flame response. This ROM addresses the significant limitations of the classical kinematic model, which is only applicable to simple flame configurations and relies on ad-hoc models …

A Novel Method For Sensitivity Analysis Of Time-Averaged Chaotic System Solutions, Christian A. Spencer-Coker

Theses and Dissertations

The direct and adjoint methods are to linearize the time-averaged solution of bounded dynamical systems about one or more design parameters. Hence, such methods are one way to obtain the gradient necessary in locally optimizing a dynamical system’s time-averaged behavior over those design parameters. However, when analyzing nonlinear systems whose solutions exhibit chaos, standard direct and adjoint sensitivity methods yield meaningless results due to time-local instability of the system. The present work proposes a new method of solving the direct and adjoint linear systems in time, then tests that method’s ability to solve instances of the Lorenz system that exhibit …

A Longitudinal Analysis Of Pathways To Computing Careers: Defining Broadening Participation In Computing (Bpc) Success With A Rearview Lens, Mercy Jaiyeola

Theses and Dissertations

Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim.

Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the …

Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius

Theses and Dissertations

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …

The Development Of Authentic Virtual Reality Scenarios To Measure Individuals’ Level Of Systems Thinking Skills And Learning Abilities, Vidanelage L. Dayarathna

Theses and Dissertations

This dissertation develops virtual reality modules to capture individuals’ learning abilities and systems thinking skills in dynamic environments. In the first chapter, an immersive queuing theory teaching module is developed using virtual reality technology. The objective of the study is to present systems engineering concepts in a more sophisticated environment and measure students learning abilities. Furthermore, the study explores the performance gaps between male and female students in manufacturing systems concepts. To investigate the gender biases toward the performance of developed VR module, three efficacy measures (simulation sickness questionnaire, systems usability scale, and presence questionnaire) and two effectiveness measures (NASA …