Browsing OU - Theses by Author "McGovern, Amy"
Now showing items 1-20 of 21
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Applied High-Order Singular Value Decomposition for Weight Compression and Expansion in Deep Neural Networks
Graham, Austin (2019-08)Complex deep learning objectives such as object detection and saliency, semantic segmentation, sequence-to-sequence translation, and others have given rise to training processes requiring increasing amounts of time and ... -
Automated Detection of Bird Roosts Using NEXRAD Radar Data and Convolutional Neural Networks
Chilson, Carmen (2017-12-15)NEXRAD radars have proven to be an effective tool for detecting bird roosts for several species or birds, however manually locating these roosts in radar images is a time consuming process. We introduce a Convolutional ... -
Automated Location of Bird Roosts Using NEXRAD Data and Image Segmentation
Avery, Katherine (2020)Weather surveillance radars can effectively detect flying animals, such as groups of birds, bats, and insects. Further, these radars are demonstrably useful for detecting the existence of certain bird roosting locations, ... -
A Constraint Driven Approach to Neural and Muscle Recruitment in Wrist Motor Tasks
Lopez - Santillana, Manuel (2020-07-30)The transformation from visual stimulus to muscle recruitment is a non- linear one: it must take into account the configuration of the body and the lines of action of the muscles. The primary cortex plays a role in the ... -
Correcting, Improving, and Verifying Automated Guidance in a New Warning Paradigm
Harrison, David (2018-05-11)The prototype Probabilistic Hazards Information (PHI) system allows forecasters to experimentally issue dynamically evolving severe weather warning and advisory products in a testbed environment, providing hypothetical end ... -
Data-based Stochastic Network Mitigation
Rodriguez Castillo, Alexander D. (2018-05)Current decision-support frameworks to assist mitigation planning do not include uncertainty and complexity of network failures, either one or both. To close this research gap, this thesis walks through a demonstration of ... -
Evaluating GAM-Like Neural Network Architectures for Interpretable Machine Learning
Booker, William (2019-05)In many machine learning applications, interpretability is of the utmost importance. Artificial intelligence is proliferating, but before you entrust your finances, your well-being, or even your life to a machine, you’d ... -
Explainable Frontal Boundary Predictions for Applications in Operational Environments
Justin, Andrew (2024-05-10)Frontal boundaries drive many high-impact weather events around the globe. Identifying fronts through various thermodynamic fields increases predictability of hazardous weather phenomena. Frontal analysis is still primarily ... -
Flow Dependent Evaluation and Training of Random Forest based Probabilistic Forecasts of Severe Weather Hazards
Shearer, Andrew (2023-12-15)There has been an increasing interest over the past ten years in the use of Machine Learning (ML) algorithms such as Random Forests (RF) in the context of severe weather prediction. RF-based methods have even been shown ... -
Gridded Hail Nowcasting using UNets, Lightning Observations, and the Warn-on-Forecast System
Schmidt, Tobias (2023-08-04)Hailstorms cause around 1 billion dollars in damage across the United States each year. At least a portion of this cost is associated with the inability to protect personal assets from damage in the short window of time ... -
Nurturing as Safe Exploration Promotes the Evolution of Generalized Supervised Learning
Hoke, Bryan (2017-08-01)The ability to learn is often a desirable property of intelligent systems which can make them more adaptive. However, it is difficult to develop sophisticated learning algorithms that are effective. One approach to the ... -
Question-Answering for Segment Retrieval on Podcast Transcripts
Elaryan, Andrew (2022-08)Podcasting has rapidly ascended as one of the primary forms of spoken-word media in the 21st century. The Spotify Podcast Dataset has compiled transcripts of over 100,000 podcast episodes, making it one of the largest ... -
Real-Time Gesture Recognition with Mini Drones
Davis, Taner (2018-12-14)Drones are being used worldwide to perform many functions - medical deliveries, video recording, and surveying to name a few. Their growing presence is paralleled by the growing world of Machine Learning (ML). This thesis ... -
SEVERE HAIL DETECTION USING REMOTE SENSING OBSERVATIONS AND AN EVALUATION OF THE ABOVE ANVIL CIRRUS PLUME FOR SEVERE WEATHER DETECTION
Murillo, Elisa M. (2018-12-14)This thesis undergoes a comprehensive comparative analysis to address two main objectives: a) Thoroughly assess available radar, satellite, and lightning based products’ ability to identify hail events and size, and b) ... -
Spatiotemporal gap-filling of NASA Deep Blue aerosol optical depth over CONUS using the UNet 3+ architecture
Lee, Jeffrey (2024-05-10)Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete over clouds, deserts, and other bright surfaces. These gaps in satellite AOD datasets represent a significant ... -
Using Machine Learning Applications and HREFv2 to Enhance Hail Prediction for Operations
Burke, Amanda (2019-08)In this thesis, I demonstrate how hail prediction can be improved through post-processing numerical weather prediction (NWP) data from the new High-Resolution Ensemble Forecast system version 2 (HREFv2) with machine learning ... -
Using Machine Learning to Improve the NSSL's Warn-On-Forecast System's Prediction of Thunderstorm Location
Wiley, Chad (2023-08-04)Deep learning (DL) models have become immensely popular in recent years, with many models creating accurate and high-skill predictions for a wide range of atmospheric phenomena. Using DL models for predicting convection ... -
Using Machine Learning to Predict Damaging Straight-line Convective Winds
Lagerquist, Ryan (2016-08-12)Thunderstorms, including straight-line (non-tornadic) winds, cause an average of over 100 deaths and $10 billion of insured damage per year in the United States. In the past decade machine learning has led to significant ... -
Using Novelty Seeking Reward Evolution Strategies to Train Generative Adversarial Networks
Jabr, Khaled (2018-12-14)Generative Adversarial Networks (GANs) are a subclass of deep generative models that aim to implicitly learn to model a data distribution. While GANs have gained wide research attention, and achieved much success, when ... -
Visibility Estimation from Camera Images Using Deep Learning
Wilson Reyes, Melissa (2023-08-04)Atmospheric visibility is an important and complex meteorological variable that directly affects safe and reliable transportation. Specifically, declining visibility can pose an increased risk to automotive, aviation, and ...