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Full-Text Articles in Engineering

Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo Oct 2020

Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo

Theses and Dissertations

Structural health monitoring (SHM) and non-destructive evaluation (NDE) have been a significant research topic to help with damage detection in aerospace structures. SHM and NDE techniques are based on extracting damage sensitive features to determine the criticality of damage and lifetime of a structure. Acoustic emission (AE) signal detection is an important technique in SHM and NDE especially for fatigue crack growth. AE signals for thin aerospace structures consist of ultrasonic guided Lamb waves that propagate through the structure. This thesis focuses on AE signal repeatability, load at which AE signals occur, feature extraction, artificial intelligence and electro-mechanical impedance of …


Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri Jul 2020

Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri

Graduate Theses and Dissertations

This work employs tools and methods from computer science to study clusters comprising a small number N of interacting particles, which are of interest in science, engineering, and nanotechnology. Specifically, the thermodynamics of such clusters is studied using techniques from spectral graph theory (SGT) and machine learning (ML). SGT is used to define the structure of the clusters and ML is used on ensembles of cluster configurations to detect state variables that can be used to model the thermodynamic properties of the system. While the most fundamental description of a cluster is in 3N dimensions, i.e., the Cartesian coordinates of …


Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes Mar 2020

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …


An Augmented Framework For Validating Neural Network Predictions Based On Statistical Modeling, William Stonewall Monroe Jan 2020

An Augmented Framework For Validating Neural Network Predictions Based On Statistical Modeling, William Stonewall Monroe

All ETDs from UAB

The Parkinson's Progression Markers Initiative (PPMI) dataset (which includes several types of imaging datasets) was created to generate biomarkers for diagnosing the presence and severity of Parkinson’s Disease (PD). Unlike some conditions, PD is not intuitively diagnosed directly from brain imaging. As such, it is vital to understand as well as classify PD. Deep learning techniques are becoming increasingly prevalent in medical imaging workflows. In some cases, deep learning outperforms human diagnostic ability. However, the black-box nature of many machine learning models can be troubling, particularly in a medical context since the results could imply alternate treatments and a human …


Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis Jan 2020

Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis

Doctoral Dissertations

“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics …