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Full-Text Articles in Engineering
Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez
Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez
Theses and Dissertations
Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand …
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Theses and Dissertations
The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …
Medical Image Segmentation With Deep Learning, Chuanbo Wang
Medical Image Segmentation With Deep Learning, Chuanbo Wang
Theses and Dissertations
Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and …
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Theses and Dissertations
While Convolutional Neural Networks (CNNs) can estimate frame-to-frame (F2F) motion even with monocular images, additional inputs can improve Visual Odometry (VO) predictions. In this thesis, a FlowNetS-based [1] CNN architecture estimates VO using sequential images from the KITTI Odometry dataset [2]. For each of three output types (full six degrees of freedom (6-DoF), Cartesian translation, and transitional scale), a baseline network with only image pair input is compared with a nearly identical architecture that is also given an additional rotation estimate such as from an Inertial Navigation System (INS). The inertially-aided networks show an order of magnitude improvement over the …
Semantic Segmentation Of Aerial Imagery Using U-Nets, Terence J. Yi
Semantic Segmentation Of Aerial Imagery Using U-Nets, Terence J. Yi
Theses and Dissertations
In situations where global positioning systems are unavailable, alternative methods of localization must be implemented. A potential step to achieving this is semantic segmentation, or the ability for a model to output class labels by pixel. This research aims to utilize datasets of varying spatial resolutions and locations to train a fully convolutional neural network architecture called the U-Net to perform segmentations of aerial images. Variations of the U-Net architecture are implemented and compared to other existing models in order to determine the best in detecting buildings and roads. A final dataset will also be created combining two datasets to …
Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou
Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou
Theses and Dissertations
Machine learning techniques are being applied to many data-intensive problems because they can accurately provide classification of complex data using appropriate training. Often, the performance of machine learning can exceed the performance of traditional techniques because machine learning can take advantage of higher dimensionality than traditional algorithms. In this work, acoustic data sets taken using a rapid scanning technique on concrete bridge decks provided an opportunity to both apply machine learning algorithms to improve detection performance and also to investigate the ways that training of neural networks can be aided by data augmentation approaches. Early detection and repair can enhance …