Engineering Commons^™

Computational Analysis Of Water Braking Phenomena For High-Speed Sled And Its Machine Learning Framework, Jose A. Terrazas

Open Access Theses & Dissertations

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses a process called "water braking" as a method to bring vehicles at the test track to a stop. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange. By studying water braking using Computational Fluid Dynamics (CFD), forces acting on track vehicles can be approximated and prepared for prior to the actual test. In this study, focus will be made on the brake component of the track sled that is responsible for interacting with the water for braking. By discretizing …

Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa

Open Access Theses & Dissertations

Communication networks play a large role in our everyday lives. COVID19 pandemic in 2020 highlighted their importance as most jobs had to be moved to remote work environments. It is possible that the spread of the virus, the death toll, and the economic consequences would have been much worse without communication networks. To remove sole dependence on one equipment vendor, networks are heterogeneous by design. Due to this, as well as their increasing size, network management has become overwhelming for network managers. For this reason, automating network management will have a significant positive impact. Machine learning and software defined networking …

Material Synthesis And Machine Learning For Additive Manufacturing, Jaime Eduardo Regis

Open Access Theses & Dissertations

The goal of this research was to address three key challenges in additive manufacturing (AM), the need for feedstock material, minimal end-use fabrication from lack of functionality in commercially available materials, and the need for qualification and property prediction in printed structures. The near ultraviolet-light assisted green reduction of graphene oxide through L-ascorbic acid was studied with to address the issue of low part strength in additively manufactured parts by providing a functional filler that can strengthen the polymer matrix. The synthesis of self-healing epoxy vitrimers was done to adapt high strength materials with recyclable properties for compatibility with AM …

Predicting Zero Bin In The Semiconductor Manufacturing Industry: Machine Learning Algorithms, Yazmin Montoya

Open Access Theses & Dissertations

The semiconductor industry has faced supply chain manufacturing shortages that ultimately led to a worldwide chip shortage during the COVID-19 pandemic. These chip manufacturers use sophisticated and advanced manufacturing machinery in their fabs to manufacture chips. As experienced during the pandemic, manufacturing unavailability is often due to the lack of critical manufacturing-related spare parts. This thesis evaluates the effectiveness of machine learning algorithms to identify significant factors contributing to manufacturing part outages (i.e., zero-bin) to keep manufacturing equipment running at total capacity within the organization. We propose clustering methods to segment the data and use logistic regression, logistic lasso regression, …

Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios

Open Access Theses & Dissertations

Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …

Application Of Machine Learning In Flood Depth Prediction, Armando Esquivel

Open Access Theses & Dissertations

Machine learning technologies have helped provide answers for problems with a high degree of complexity. Machine learning has been utilized by various disciplines within the Civil Engineering profession and has proven to be efficient in solving complex problems. Although machine learning is being used in the Civil Engineering profession, a formal framework on developing and integrating machine learning has not been developed for flood depth prediction. The proposed word uses machine learning to predict the depth of flood at Houston, TX, due to a 100-year 24-hour storm. The proposed work can be used to collect, store and analyze data to …

Emocolor : Fine-Grained Emotion Recognition From Skin Color Information, Maria Guadalupe Jimenez Velasco

Open Access Theses & Dissertations

In everyday human-to-human communication, emotions play a fundamental role. Emotions represent the affective behavior of humans that is multi-modal, subtle, and complex. Previous approaches based on conventional computer vision explicitly used shape information. Modern approaches based on deep learning implicitly exploit all information available in the image, but by their nature make it difficult to assess the contributions of each source of information. In addition, skin color as a unimodal technique to recognize emotions has been explored to recognize only three coarse-grained emotions in valence space.To the best of our knowledge, this work presents the first approach to fine-grained emotion …

Evaluating Flow Features For Network Application Classification, Carlos Alcantara

Open Access Theses & Dissertations

Communication networks provide the foundational services on which our modern economy depends. These services include data storage and transfer, video and voice telephony, gaming, multimedia streaming, remote invocation, and the world wide web. Communication networks are large-scale distributed systems composed of heterogeneous equipment. As a result of scale and heterogeneity, communication networks are cumbersome to manage (e.g., configure, assess performance, detect faults) by human operators. With the emergence of easily accessible network data and machine learning algorithms, there is a great opportunity to move network management towards increasing automation. Network management automation will allow for a reduced likelihood of human …

A Comparison Of Data-Driven And Process-Based Modeling For Nutrient Estimation In A Eutrophic Reservoir, Yohtaro Kobayashi

Open Access Theses & Dissertations

As land use around bodies of water changes, the need to model the body of water increases. Models help to educate, understand, and predict the state of water. Process-based models are commonly used in modelling bodies of water, but there are challenges with these kinds of models. They require data which can be difficult for certain communities to obtain due to logistics or cost, are computationally intensive, technically complicated, and require calibration. In contrast, a data-driven model simply connect relationships from the data, are not as computationally intensive nor technically complicated, and do not require calibration. This research compared a …

Uav Parameter Estimation Through Machine Learning, Andres Enriquez Fernandez

Open Access Theses & Dissertations

Parameter identification of Unmanned Aerial Vehicles (UAV) is very helpful for understanding cause-effect relationships of physical phenomenon, investigating system performance and characteristics, fault diagnostics, control development/tuning, and more. Traditional ways of performing parameter identification involve establishing a mathematical model that describes the system's behavior. The equations in the model contain parameters that are estimated indirectly from measured flight data. This parameter identification process requires knowledge of the physics involved. Also, it necessitates a careful consideration of the aircraft instrumentation for accurate measurements. It also requires careful design of the flight maneuvers to ensure thorough excitation of the flight dynamics involved. …

Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children, Nancy Selene Avila

Open Access Theses & Dissertations

Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.

This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, …

Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas

Open Access Theses & Dissertations

Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …

Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis

Open Access Theses & Dissertations

Artificial intelligence has come a very long way from being a mere spectacle on the silver screen in the 1920s [Hml18]. As artificial intelligence continues to evolve, and we begin to develop more sophisticated Artificial Neural Networks, the need for specialized and more efficient machines (less computational strain while maintaining the same performance results) becomes increasingly evident. Though these “new” techniques, such as Multilayer Perceptron’s, Convolutional Neural Networks and Recurrent Neural Networks, may seem as if they are on the cutting edge of technology, many of these ideas are over 60 years old! However, many of these earlier models, at …

Implementing Large Eddy Simulation To Numerical Simulation Of Optical Wave Propagation, Diego Alberto Lozano Jimenez

Open Access Theses & Dissertations

In this study, we want to simulate long-range laser propagation in atmospheric turbulence. The numerical simulations are carried out to study the impact of strong atmospheric turbulence in spatial, temporal, and related spectral domains. The first section of this study will be concerned with modeling this numerical simulation in Kolmogorov and non-Kolmogorov spectrum. To validate our numerical simulation, we will compare the statistical parameter to theoretical approximation in both Kolmogorov and non-Kolmogorov spectrums. Once the code is validated, we want to integrate Large Eddy Simulation (LES) turbulence modeling. LES simulations allowa us to study strong fluid turbulence and can predict …

Data-Driven Predictive Framework For Modeling Complex Multi-Physics Engineering Applications, Arturo Schiaffino Bustamante

Open Access Theses & Dissertations

Computational models are often encountered in multiple engineering application, such as structural design, material science, heat transfer and fluid dynamics. These simulations offer the engineers the capability of understanding complex physical situations before putting them to practice, either through experimentation or prototyping. The current advances in computational sciences, hardware architecture, software development and big data technology, have allowed the construction of sturdy predicting frameworks for analyzing a wide array of natural phenomena across different disciplines, either through the implementation of statistical methods, such as big data, and uncertainty quantification, or through high performance computing of a numerical model. The objective …

Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas

Open Access Theses & Dissertations

Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …