Automatic detection of space objects in optical images is important to close proximity operations, relative navigation, and situational awareness. To better protect space assets, it is very important not only to know where a space object is, but also what the object is. In this dissertation, a method for detecting multiple 1U, 2U, 3U, and 6U CubeSats based on the faster region-based convolutional neural network (Faster R-CNN) is described. CubeSats detection models are developed using Web-searched and computer-aided design images. In addition, a two-step method is presented for detecting a rotating CubeSat in close proximity from a sequence of images without the use of intrinsic or external camera parameters. First, a Faster R-CNN trained on synthetic images of 1U, 2U, 3U, and 6U CubeSats locates the CubeSat in each image and assigns a weight to each CubeSat class. Then, these classification results are combined using Dempster's rule. The method is tested on simulated scenarios where the rotating 3U and 6U CubeSats are in unfavorable views or in dark environments. Faster R-CNN detection results contain useful information for tracking, navigation, pose estimation, and simultaneous localization and mapping. A coarse single-point attitude estimation method is proposed utilizing the centroids of the bounding boxes surrounding the CubeSats in the image. The centroids define the line-of-sight (LOS) vectors to the detected CubeSats in the camera frame, and the LOS vectors in the reference frame are assumed to be obtained from global positioning system (GPS). The three-axis attitude is determined from the vector observations by solving Wahba's problem. The attitude estimation concept is tested on simulated scenarios using Autodesk Maya.

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Gait recognition is important for identifying suspects in criminal investigations. This study will study the potential of using models based on transfer learning for this purpose. Both supervised and unsupervised learning will be examined. For the supervised learning part, the data is labeled and we investigate how accurate the models can be, and the impact of different walking conditions. Unsupervised learning is when the data is unlabeled and this part will determine if clustering can be used to identify groups of individuals without knowing who it is. Two deep learning models, the InceptionV3 model and the ResNet50V2, model are utilized, and the Gait Energy image method is used as gait representation. After optimization analysis, the models achieved the highest prediction accuracy of 100 percent when only including normal walking conditions and 99.25 percent when including different walking conditions such as carrying a backpack and wearing a coat, making them applicable for use in real-world investigations, provided that the data is labeled. Due to the apparent sensitivity of the models to varying camera angles, the clustering part resulted in an accuracy of approximately 30 percent. For unsupervised learning on gait recognition to be applicable in the real world, additional enhancements are required.

This thesis presents machine and statistical learning approaches for sustainable planning in infrastructure and mobility systems. First, I have developed a convolutional neural network (CNN) to predict tree failure likelihood. Such assessments have traditionally been performed manually. I conduct a visual analysis of the predictions, indicating an approach for incorporating interpretability into model selection. Benchmarking the results against those produced by state-of-the-art CNNs, I show that a relatively simple model produces better results in a computational time that is three times faster. Via this novel framework, I demonstrate the potential of machine learning to automate and consequently reduce the costs of tree failure likelihood assessments in proximity to power lines, thereby promoting sustainable infrastructure. Secondly, I examine the effects of COVID-19 on mobility, segmented by transportation type, as well as social activity such as workplaces and residential, and their interdependencies. Using time series data across five continents, I estimate a Bayesian global vector autoregression model which explains patterns in activity and mobility trends and analyze their relationship with COVID-19 spread. I expect that the model framework and outcomes will guide policymakers to adopt appropriate measures to mitigate and safely recover from future disease outbreaks.

This dissertation studies the micromechanical behavior of high-performance carbon fiber-reinforced polymer (CFRP) composites through high-fidelity numerical simulations. We investigated multiple transverse cracking of cross-ply CFRP laminates on the microstructure level through simulating large numerical models. Such an investigation demands an efficient numerical framework along with significant computational power. Hence, an efficient numerical framework was developed for simulating 2-D representations of CFRP composites' microstructure. The framework utilizes a nonlinear interface-enriched generalized finite element method (IGFEM) scheme which significantly decreases the computational cost. The framework was also designed to be fast and memory-efficient to enable simulating large numerical models. By utilizing the developed framework, the impacts of a few parameters on the evolution of transverse crack density in cross-ply CFRP laminates were studied. The considered parameters were characteristics of fiber/matrix cohesive interfaces, matrix stiffness, $0^{circ}$~plies longitudinal stiffness. We also developed a micromechanical framework for efficient and accurate simulation of damage propagation and failure in aligned discontinuous carbon fiber-reinforced composites under loading along the fibers' direction. The framework was validated based on the experimental results of a recently developed 3-D printed aligned discontinuous carbon fiber-reinforced composite as the composite of interest. The framework was then utilized to investigate the impacts of a few parameters of the constitutive equations on the strength and failure pattern of the composites of interest. This dissertation also contributes towards improving the computational efficiency of CFRP composites' simulations. We exhaustively investigated the cause of a convergence difficulty in finite element analyses caused by cohesive zone models (CZMs) which are commonly used to simulate fiber/matrix interfaces in CFRP composites. The CZMs' convergence difficulty significantly increases the computational burden. For the first time, we explained the root of the convergence difficulty and proposed a simple technique to overcome the convergence issue. The proposed technique outperformed the existing methods in terms of accuracy and computational cost. We also proposed a deep learning framework for predicting full-field distributions of mechanical responses in 2-D representations of CFRP composites based on the geometry of the microstructures. The deep learning framework can be used as a surrogate to the expensive and time-consuming finite element simulations. The proposed framework was able to accurately predict the stress distribution at an early stage of damage initiation and the failure pattern in representations of CFRP composites microstructure under transverse tension. / Doctor of Philosophy / Carbon fiber-reinforced polymers (CFRPs) are materials that are lightweight with excellent mechanical performance. Hence, these materials have a wide range of applications in various industries such as aerospace, automotive, and civil engineering. The extensive use of CFRPs has made them an active area of research and there have been great efforts to better understand and improve the mechanical properties of these materials over the past few decades. Therefore, CFRP materials and their manufacturing process are constantly changing and new types of CFRPs are kept being developed. As a result, the mechanical behavior of CFRPs needs to be exhaustively investigated to provide guidelines for their optimal engineering design and indicate the future direction of manufacturing improvements. This dissertation studied the mechanical behavior of CFRPs through high-fidelity simulations. Two types of CFRP were investigated: laminates and 3-D printed CFRPs. Laminates are the most popular type of CFRPs which are commonly used to construct the body of aircraft. 3-D printed CFRPs are new types of material that are gaining traction due to their ability to construct structures with complex geometries at high speed and without direct human supervision. The numerical simulations of CFRPs under mechanical loading are time-consuming and require significant computational power even when run on a supercomputer. Hence, this dissertation also contributes to improving the computational efficiency of numerical simulations. To decrease the computational cost, we proposed a technique that can significantly speed up the numerical simulations of CFRPs. Moreover, we utilized artificial intelligence to develop a new framework that can be substituted for the expensive and time-consuming conventional numerical simulations to quickly predict specific mechanical responses of CFRPs.

The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction. In the Plinko strategy, each network takes advantage of different word usage patterns corresponding to different levels of evolutionary divergence. Plinko has the advantages of relatively low storage, GPGPU parallel training and inference, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional,alignment-free approach to taxonomy prediction. / Master of Science / Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. Given species diversity on Earth, taxonomy prediction gets challenging with (i) increasing number of species (labels) to classify and (ii) decreasing input (DNA) size. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Three major challenges in taxonomy prediction are (i) large dataset sizes (order of 109 sequences) (ii) large label spaces (order of 103 labels) and (iii) low resolution inputs (100 base pairs or less). Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction for hard to classify sequences under the three conditions stated above. Plinko has the advantage of relatively low storage footprint, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional, alignment-free approach to taxonomy prediction.

Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using computational fluid dynamics (CFD) models come at a significant computational cost where simulation times are often measured in hours, days, or even weeks. A new simulation method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make predictions of new inputs. The field of image analysis has seen significant advancements in ANN performance by using feature based layers in the network architecture. Inspired by these advancements, a generalized procedure to design ANNs to make spatially resolved predictions in multi-physics applications is presented and applied to different fire applications. A deep convolutional inverse graphics network (DCIGN) was developed to predict the two-dimensional spatially resolved spread of a wildland fire. The network uses an image stack corresponding to the spatially resolved landscape, weather, and current fire perimeter (which can be obtained from measurements) to predict the fire perimeter six hours in the future. A transpose convolutional neural network (TCNN) was developed to predict the spatially resolved thermal flow field in a compartment fire from coarse zone fire model predictions. The network uses thirty-five parameters describing the geometry of the room and the ventilation conditions to predict the full-field temperature and velocity throughout the room. The data for use in training and testing both networks was generated using high-fidelity CFD fire simulations. Overall, the ANN predictions in each network agree with simulation predictions for validation scenarios. The computational time to evaluate the ANNs is 10,000x faster than the high-fidelity fire simulations. This work represents a first step in developing super real-time full-field fire predictions for different applications. / Ph. D. / The National Fire Protection Agency estimates the total cost of fire in the United States at $300 billion annually. In 2017 alone, there were 3,400 civilian fire fatalities, 14,670 civilian fire injuries, and an estimated $23 billion direct property loss in the United States. Large scale fires in the wildland urban interface (WUI) and in large buildings still represent a significant hazard to life, property, and the environment. Researchers and fire safety engineers often use computer simulations to predict the behavior of a fire to assist in reducing the hazard of fire. Unfortunately, typical simulations of fire scenarios may take hours, days, or even weeks to run which limits their use to small areas or sections of buildings. A new method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make new predictions of fire behavior. Inspired by advancements in the field of image processing, this research developed a procedure to use machine learning to make rapid high resolution predictions of fire behavior. An ANN was developed to predict the perimeter of a wildland fire six hours in the future based on a set of images corresponding to the landscape, weather, and current fire perimeter, all of which can be obtained directly from measurements (US Geological Survey, Automated Surface Observation System, and satellites). In addition, an ANN was developed to predict high-resolution temperature and velocity fields within a floor of a building based on predictions from a coarse model. The data for use in training and testing these networks was generated using high-resolution fire simulations. Overall, the network predictions agree well with simulation predictions for new scenarios. In addition, the time to run the model is 10,000x faster than the typical simulations. The work presented herein represents a first step in developing high resolution computer simulations for different fire scenarios that run very quickly.

Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model. / Master of Science / Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at only a single home or an aggregate load of a collection of homes. This study aims to address this gap and serve as an analysis on short term load forecasting on 3 communities of residential buildings. Detailed analysis on the model performances across all homes have been studied. Deep learning models have been used in this study and their efficacy is measured compared to a simple ANN model.

In this paper we explore various approaches to using deep neural networks to per- form cryptanalysis, with the ultimate goal of having a deep neural network deci- pher encrypted data. We use long short-term memory networks to try to decipher encrypted text and we use a convolutional neural network to perform classification tasks on encrypted MNIST images. We find that although the network is unable to decipher encrypted data, it is able to perform classification on encrypted data. We also find that the networks performance is depending on what key were used to en- crypt the data. These findings could be valuable for further research into the topic of cryptanalysis using deep neural networks.

The advancement in the field of artificial intelligence has been rapid in recent years and has revolutionized various industries. For example, convolutional neural networks (CNNs) perform image classification at a level equivalent to that of humans on many image datasets. These state-of-the-art networks reached unprecedented success using complex architectures with billions of parameters, numerous kernel configurations, weight initialization and regularization methods. This transitioned the models into black-box entities with little to no information on the decision-making process. This lack of transparency in decision making and started raising concerns amongst some sectors of user community such as the sectors, amongst others healthcare, finance and justice. This challenge motivated our research where we successfully produced human interpretable influential features from CNN for image classification and captured the interactions between these features by producing a concise decision tree making accurate classification decisions. The proposed methodology made use of pre-trained VGG16 with finetuning to extract feature maps produced by learnt filters. A decision tree was then induced on these extracted features that captured important interactions between the features. On the CelebA image dataset, we successfully produced human interpretable rules capturing the main facial landmarks responsible for segmenting males from females with the use of a decision tree which achieved 89.57% accuracy, while on the Cats vs Dogs dataset 87.55% accuracy was achieved.