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Articles 1 - 15 of 15
Full-Text Articles in Computational Engineering
Thermal Heterogeneity And Lithium Plating During Fast Charging Of Lithium-Ion Battery., Ayodeji Avis Adeniran
Thermal Heterogeneity And Lithium Plating During Fast Charging Of Lithium-Ion Battery., Ayodeji Avis Adeniran
Electronic Theses and Dissertations
Presently, widespread adoption of electric vehicles (EVs) is hindered by challenges associated with XFC, including heat generation, lithium plating, and accelerated battery degradation. This dissertation addresses these barriers through two coordinated themes—lithium plating and thermal management—spanning four manuscripts that integrate operando diagnostics, physics-based simulation, and module-level design optimization. The first theme seeks a fundamental investigation into the dynamic interplay between temperature heterogeneity and lithium plating during fast charging, a phenomenon that poses significant limitations on the performance of graphite-based anodes. Firstly, a custom operando cell with a sapphire window was designed to enable synchronized MWIR thermography and optical imaging of …
Methods To Improve The Computational Efficiency And Accuracy Of Second-Order Elastic Steel Frame Analyses, Nadine Faramawi
Methods To Improve The Computational Efficiency And Accuracy Of Second-Order Elastic Steel Frame Analyses, Nadine Faramawi
Electronic Theses and Dissertations
This paper investigates the derivation and performance of new stiffness coefficients. The estimated coefficients aimed to improve the geometric stiffness matrix representation and their application in elastic second-order analysis for steel frames. The newly developed (C1-C4) coefficients incorporate non-linear effects and reduce computational efforts to efficiently enhance the accuracy of second-order analysis. These coefficients are particularly beneficial for braced structures where they allow more refined analysis using fewer elements per member, especially as the load applied to the frame approaches the critical buckling load. However, for cases of unbraced frames, using these approximated coefficients showed no significant advantages in comparison …
Hidden Markov Model For Identifying Local Variants In Human Genomes Using Simulated Data, Scott Mccallum
Hidden Markov Model For Identifying Local Variants In Human Genomes Using Simulated Data, Scott Mccallum
Electronic Theses and Dissertations
Identifying adaptive mutations in genetic data is challenging due to the low frequency of occurrence of such events, and because signatures of selection are intertwined with the footprints of various other evolutionary forces that shape our genomes. Even when a larger region appears to be under selection, genomic sites that are linked to adaptive mutations have similar statistical signals, and thus can obfuscate the identification of the actual adaptive mutation. The new method described here uses a Hidden Markov Model that allows for classification of neutral, linked, and sweep (adaptive mutation) genomic sites. This model is general and can be …
Context Dependent Training Data Selection For Automatic Target Detection., Tylman Michael
Context Dependent Training Data Selection For Automatic Target Detection., Tylman Michael
Electronic Theses and Dissertations
An Automatic Target Detection (ATD) algorithm is capable of identifying the location of targets of interest captured by Infra-Red imagery in vastly different contexts. ATD is often a precursor in a 2-stage methodology in order to ascertain the location and nature of a target in both military and civilian applications. In order to train an ATD algorithm, a large amount of data from varied sources is required. One drawback of this requirement is that some sources of data may harm the performance of the method in different contexts. This thesis explores utilizing an unsupervised method to identify a subset of …
Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye
Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye
Electronic Theses and Dissertations
Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to …
Automated Usability Evaluation Utilizing Log Files And Data Mining Techniques., Sima Shafaei
Automated Usability Evaluation Utilizing Log Files And Data Mining Techniques., Sima Shafaei
Electronic Theses and Dissertations
Usability evaluation is one of the essential aspects of software production. This evaluation should be done during the entire life cycle of a software application, from pre-production to production and post-production. However, the collection and evaluation of usability data can be a very challenging, time-consuming, and expensive task to be conducted manually, particularly for certain types of products and working conditions. These challenges may include the need to recruit participants fully engage and motivate them during evaluation, and factor in environmental conditions. Other challenges may include collecting data in real-world environments, especially when the users are geographically dispersed, minimizing evaluator …
Digital Twins Of The Living Knee: From Measurements To Model, Thor Erik Andreassen
Digital Twins Of The Living Knee: From Measurements To Model, Thor Erik Andreassen
Electronic Theses and Dissertations
Modern medicine has dramatically improved the lives of many. In orthopaedics, robotic surgery has given clinicians superior accuracy when performing interventions over conventional methods. Nevertheless, while these and many other methods are available to ensure treatments are performed successfully, far fewer methods exist to predict the proper treatment option for a given person. Clinicians are forced to categorize individuals, choosing the best treatment on “average.” However, many individuals differ significantly from the “average” person, for which many of these treatments are designed. Going forward, a method of testing, evaluating, and predicting different treatment options' short- and long-term effects on an …
Multilateration Index., Chip Lynch
Multilateration Index., Chip Lynch
Electronic Theses and Dissertations
We present an alternative method for pre-processing and storing point data, particularly for Geospatial points, by storing multilateration distances to fixed points rather than coordinates such as Latitude and Longitude. We explore the use of this data to improve query performance for some distance related queries such as nearest neighbor and query-within-radius (i.e. “find all points in a set P within distance d of query point q”). Further, we discuss the problem of “Network Adequacy” common to medical and communications businesses, to analyze questions such as “are at least 90% of patients living within 50 miles of a covered emergency …
Multiphysics Computational Model Of Fluid Flow And Mass Transport In Aneurysm, Tanja Cupac
Multiphysics Computational Model Of Fluid Flow And Mass Transport In Aneurysm, Tanja Cupac
Electronic Theses and Dissertations
The abdominal aortic aneurysm is progressive, asymptomatic, and can eventually lead to rupture which is a catastrophic event leading to massive internal bleeding and possibly death. AAA cases have been characterized by the development of an intraluminal thrombus (ILT). The ILT correlates with the progression of hypoxia in the arterial wall. The extent that ILT presence reduces oxygen flux to the wall has not been quantified and there is rather a poor understanding of key parameters that can affect thrombus-mediated oxygen transport in AAA. The purpose of this study is to address this gap and to assess the effects of …
Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi
Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi
Electronic Theses and Dissertations
Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown …
Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde
Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde
Electronic Theses and Dissertations
In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …
Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene
Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene
Electronic Theses and Dissertations
Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous …
Landmine Detection Using Semi-Supervised Learning., Graham Reid
Landmine Detection Using Semi-Supervised Learning., Graham Reid
Electronic Theses and Dissertations
Landmine detection is imperative for the preservation of both military and civilian lives. While landmines are easy to place, they are relatively difficult to remove. The classic method of detecting landmines was by using metal-detectors. However, many present-day landmines are composed of little to no metal, necessitating the use of additional technologies. One of the most successful and widely employed technologies is Ground Penetrating Radar (GPR). In order to maximize efficiency of GPR-based landmine detection and minimize wasted effort caused by false alarms, intelligent detection methods such as machine learning are used. Many sophisticated algorithms are developed and employed to …
Re-Evaluating Performance Measurement: New Mathematical Methods To Address Common Performance Measurement Challenges, Jordan David Benis
Re-Evaluating Performance Measurement: New Mathematical Methods To Address Common Performance Measurement Challenges, Jordan David Benis
Electronic Theses and Dissertations
Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the …
Multi Self-Adapting Particle Swarm Optimization Algorithm (Msapso)., Gerhard Koch
Multi Self-Adapting Particle Swarm Optimization Algorithm (Msapso)., Gerhard Koch
Electronic Theses and Dissertations
The performance and stability of the Particle Swarm Optimization algorithm depends on parameters that are typically tuned manually or adapted based on knowledge from empirical parameter studies. Such parameter selection is ineffectual when faced with a broad range of problem types, which often hinders the adoption of PSO to real world problems. This dissertation develops a dynamic self-optimization approach for the respective parameters (inertia weight, social and cognition). The effects of self-adaption for the optimal balance between superior performance (convergence) and the robustness (divergence) of the algorithm with regard to both simple and complex benchmark functions is investigated. This work …