Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (2)
- ANN (1)
- Akaike information criterion (1)
- Anomaly Detection (1)
- Bender Element (1)
-
- Bianisotropy Metamaterial Vector Spherical Harmonics (1)
- Bombardment (1)
- Carbon nanotubes (1)
- Characterization (1)
- Chemical Graph Theory (1)
- Condition Monitoring (CM) (1)
- Containerization (1)
- Covalent functionalization (1)
- Dams (1)
- Deep Deterministic Policy Gradient (1)
- Deep Learning (DL) (1)
- Deviance (1)
- Digital editions (1)
- Discrete Element Method (1)
- Distributed Acoustic Sensing (DAS) (1)
- Docker (1)
- Ecoregion (1)
- Finite element analysis (FEA) (1)
- Flow regime (1)
- Generative Adversarial Networks (1)
- Granular Material (1)
- High Tonnage Loop (HTL) (1)
- Holt-winters (1)
- Hosoya Index (1)
- Image generation models (1)
Articles 1 - 13 of 13
Full-Text Articles in Entire DC Network
Problems In Chemical Graph Theory Related To The Merrifield-Simmons And Hosoya Topological Indices, William B. O'Reilly
Problems In Chemical Graph Theory Related To The Merrifield-Simmons And Hosoya Topological Indices, William B. O'Reilly
Electronic Theses and Dissertations
In some sense, chemical graph theory applies graph theory to various physical sciences. This interdisciplinary field has significant applications to structure property relationships, as well as mathematical modeling. In particular, we focus on two important indices widely used in chemical graph theory, the Merrifield-Simmons index and Hosoya index. The Merrifield-Simmons index and the Hosoya index are two well-known topological indices used in mathematical chemistry for characterizing specific properties of chemical compounds. Substantial research has been done on the two indices in terms of enumerative problems and extremal questions. In this thesis, we survey known extremal results and consider the generalized …
Enhancing Flight Delay Predictions Using Network Centrality Measures, Joseph Ajayi
Enhancing Flight Delay Predictions Using Network Centrality Measures, Joseph Ajayi
Electronic Theses and Dissertations
Accurate prediction of flight delays remains a formidable challenge within the aviation industry, owing to its inherent complexity and the interconnectivity of its operations. Traditional flight prediction methods frequently utilize meteorological conditions—such as temperature, humidity, and dew point—alongside flight-specific data like departure and arrival times. However, these predictors often fall short of capturing the nuanced dynamics that lead to delays. This thesis introduces network centrality measures as novel predictors for enhancing the binary classification of flight arrival delays. Furthermore, it emphasizes the application of tree-based ensemble models, which are recognized for their superior ability to model complex relationships compared to …
Characterization Of Carbon Nanotube/Polystyrene Composites Prepared Via Microwave-Induced Polymerization, Hubert Agamasu
Characterization Of Carbon Nanotube/Polystyrene Composites Prepared Via Microwave-Induced Polymerization, Hubert Agamasu
Electronic Theses and Dissertations
Carbon nanotubes (CNTs), with their excellent mechanical properties, are well suited to the production of composites with good strength-to-weight ratio. However, the use of nanotubes as reinforcement is hampered by the lack of interaction between their inert surfaces and matrices. This, coupled with the van der Waal’s interactions between individual nanotubes, leads to agglomeration of CNTs in media. Functionalization is an efficient means to disperse nanotubes as well as increase their reactivity. The irradiation of CNTs with microwaves generates instantaneous, localized heating, being used in this work to trigger the rapid, thermal, free radical polymerization of styrene. Under microwaves, olefin-functionalized …
Bringing Gans To Medieval Times: Manuscript Translation Models, Tonilynn M. Holtz
Bringing Gans To Medieval Times: Manuscript Translation Models, Tonilynn M. Holtz
Electronic Theses and Dissertations
The Generative Adversarial Networks (GAN) recently emerged as a powerful framework for producing new knowledge from existing knowledge. These models aim to learn patterns from input data then use that knowledge to generate output data samples that plausibly appear to belong to the same set as the input data. Medieval manuscripts study has been an important research area in the humanities field for many decades. These rare manuscripts are often times inaccessible to the general public, including students in scholars, and it is of a great interest to provide digital support (including, but not limited to translation and search) for …
Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev
Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev
Electronic Theses and Dissertations
Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …
Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis, Daniel E. Ukene
Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis, Daniel E. Ukene
Electronic Theses and Dissertations
This thesis comprises three distinct, yet interconnected studies addressing critical aspects of web infrastructure management. We begin by studying containerization via Docker and its impact on web server performance, focusing on Apache and Nginx hosted on virtualized environments. Through meticulous load testing and analysis, we provide insights into the comparative performance of these servers, adding users of this technology know which webservers to leverage when hosting their webservice along alongside the infrastructure to host it on. Next, we expand our focus to examine the performance of caching systems, namely Redis and Memcached, across traditional VMs and Docker containers. By comparing …
Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman
Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman
Electronic Theses and Dissertations
Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …
Exploring The Consistency Of Flow Regimes Within And Among Ecoregions Of The Southeastern United States, Frank Paul Braun Iv
Exploring The Consistency Of Flow Regimes Within And Among Ecoregions Of The Southeastern United States, Frank Paul Braun Iv
Electronic Theses and Dissertations
Human manipulation of river systems has long been a known contributor to the loss of freshwater biodiversity. By accounting for environmental causes of hydrologic variation among rivers, we can better understand how ecoregion mediates flow regimes and forecast species that may be at risk. Presumably, natural variation associated with ecoregion boundaries exerts strong influence on flow regimes, and may mediate relationships between other features (e.g., land use, dam operations) and hydrology. However, such between-ecoregion variation is poorly investigated, particularly at fine spatial and temporal scales. I characterized 10 hydrologic metrics, representing the five key dimensions of the flow regime (magnitude, …
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Electronic Theses and Dissertations
In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …
The Distribution Of The Significance Level, Paul O. Monnu
The Distribution Of The Significance Level, Paul O. Monnu
Electronic Theses and Dissertations
Reporting the p-value is customary when conducting a test of hypothesis or significance. The likelihood of getting a fictitious second sample and presuming the null hypothesis is correct is the p-value. The significance level is a statistic that interests us to investigate. Being a statistic, it has a distribution. For the F-test in a one-way ANOVA and the t-tests for population means, we define the significance level, its observed value, and the observed significance level. It is possible to derive the significance level distribution. The t-test and the F-test are not without controversy. Specifically, we demonstrate that as sample size …
Scattering Of Electromagnetic Radiation By Bianisotropic Spheres, Maxwell A. Wallace
Scattering Of Electromagnetic Radiation By Bianisotropic Spheres, Maxwell A. Wallace
Electronic Theses and Dissertations
Modern developments in materials science have led to the increased demand for the ability to control electromagnetic radiation at scales smaller than ever. One of the most important areas of research for controlling the manipulation of electromagnetic radiation, has been the studying of novel optical metamaterials, including the most general and complex form, bianisotropic metamaterials (BAMs). With modern developments in nano- engineering, paired with the advancement of more robust theoretical studies of BAMs, the demand for more novel BAM technologies has increased. With the advent of research of unbounded BAM media, as well as the recent extensions of Mie theory …
Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole
Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole
Electronic Theses and Dissertations
We show how to create Artificial Neural Network based models for performing the well- known Holt-Winters time series analysis. Our work fares well compared to the well-known Holt-Winter time series prediction method while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models
Simulation Of Wave Propagation In Granular Particles Using A Discrete Element Model, Syed Tahmid Hussan
Simulation Of Wave Propagation In Granular Particles Using A Discrete Element Model, Syed Tahmid Hussan
Electronic Theses and Dissertations
The understanding of Bender Element mechanism and utilization of Particle Flow Code (PFC) to simulate the seismic wave behavior is important to test the dynamic behavior of soil particles. Both discrete and finite element methods can be used to simulate wave behavior. However, Discrete Element Method (DEM) is mostly suitable, as the micro scaled soil particle cannot be fully considered as continuous specimen like a piece of rod or aluminum. Recently DEM has been widely used to study mechanical properties of soils at particle level considering the particles as balls. This study represents a comparative analysis of Voigt and Best …