LOUIS University of Alabama in Huntsville
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Simulating X-ray Observations of AGN Inflated Bubbles in High-Redshift Galaxy Clusters with AXIS
Get PDFReal-time Analysis of Photoplethysmogram and Electrocardiogram for Assessment of Physiological Correlates of Attention
Get PDFFolklore and Marvels in the Otia Imperialia
Get PDFhttps://louis.uah.edu/honors-399/1015/thumbnail.jp
Multilabel defect classification using graph neural networks for autonomous visual inspections
Get PDFVisual inspections of safety-critical systems are crucial in reducing the risk of equipment failures, downtime, and loss of life. This nondestructive testing (NDT) method uses a portable borescope or camera along with other sensors directly or mounted on robotic platforms to inspect difficult-to-access areas with ease, minimum time, and cost. Although state-of-the-art visual inspection platforms are equipped with sensors from multiple modalities, the inspection tasks still require human subject matter experts to identify defects and analyze them. This jeopardizes human safety in a hazardous work environment in energy industries, as well as the extended time for inspection and human error. Moreover, defect identification becomes much more challenging, especially in large machinery and structures, such as aircraft engines, concrete bridges, and buildings, because of differences in material appearance, changing lighting, different surface markings, and the possible overlap of varying defect types. In order to automate the process and address the inherent challenge of defect classification, it is imperative to employ a resilient deep-learning approach that can accurately identify the defects. In this thesis, a hybrid deep learning method for multilable defect classification from visual data by using graph neural networks (GNN), convolutional neural networks (CNN), and feedforward neural networks (FFN) is presented. The first part of the thesis provides a comprehensive review of state-of-the-art GNNs for machine vision to derive motivation for the research. The literature review describes various graph-learning approaches and the challenges associated with generating graph- structured datasets from images. The primary focus of the review is the application of GNNs in machine vision and their mathematical formulations. In the second part, the proposed defect classification methodology is presented, which diverges from conventional deep learning approaches for multilabel defect classification by harnessing the combined strengths of CNN and GNN algorithms. The core idea is to exploit CNNs for their prowess in recognizing the visual characteristics of defects and GNNs for their ability to capture the relational structures of defects, facilitating more precise differentiation. This multilabel hybrid vision GNN algorithm is validated using the open-source CODEBRIM dataset, which contains multilabel images of large-scale concrete structural defects. The model’s performance in image classification is validated using the CIFAR-10 dataset, achieving 86 % accuracy during testing. Experimental results demonstrate that the hybrid architectures developed have fewer overall parameters and achieve a 16% improvement in accuracy compared to popular neural architectures for defect classification
Never Let Me Go, Kazuo Ishiguro\u27s Time Capsule for Disability in 1990s Britain
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Understanding 3D Data Cubes From the Very Large Telescope
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Cystic fibrosis transition program to improve patient disease self-management and independent health care utilization
No full textMedical advances have improved the life expectancy among patients diagnosed with cystic fibrosis. More patients will now be transitioning from pediatric to adult healthcare, creating the need for more focus on the transition process. The DNP project implemented a formal transition program to improve the process of pediatric patients diagnosed with cystic fibrosis transitioning to adult care. The transition program included patients aged 10 to 18 and aimed to improve transition readiness by increasing patient disease self-management and healthcare utilization skills. The Transition Readiness Assessment Questionnaire (TRAQ) was utilized to measure transition readiness and the outcome of the transition program. The site for the DNP project had no formal transition program in place prior to the project implementation, which does not meet the healthcare protocols set by the Cystic Fibrosis Foundation. Implementing a formal transition program improved patient care at the DNP project site by providing the standard of care
UAH First Year Experience (FYE)
Get PDFThe Charger Success First Year Experience course is designed to assist new students in making a successful transition to the University of Alabama in Huntsville, both within and beyond the classroom. The goals of this course focus on developing a sense of community, promoting engagement in the academic life of the university, and articulating to students the expectations of the University. In addition, the course will assist students in understanding and applying critical thinking skills, as well as offer support to students in clarifying their academic interests and eventual career direction.https://louis.uah.edu/oer/1003/thumbnail.jp
