Engineering Commons^™

Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii

Theses and Dissertations

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Asset Cueing Nuclear Radiation Anomaly Detection Using An Embedded Neural Network Resource, April Inamura

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Nuclear radiation detection is inherently a challenging task, coupled with a high background variation or increase in anomalies, the accuracy for detection can plummet. A key factor in the success of nuclear detection hinges on the sensor’s ability to generalize its model and directly leads to the model’s robustness. The goal of this project is to develop algorithms suitable for use on the University of Nebraska-Lincoln’s Pingora chip, a low-power, system-on-chip device with an active neural processing unit (NPU) made for nuclear radiation detection. The thesis aims to improve Pingora’s overall generalization ability in nuclear radiation source detection. A multiphase …

Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla

Electrical and Computer Engineering ETDs

Research on video activity detection has mainly focused on identifying well-defined human activities in short video segments, often requiring large-parameter systems and extensive training datasets. This dissertation introduces a low-parameter, modular system with rapid inference capabilities, capable of being trained on limited datasets without transfer learning from large-parameter systems. The system accurately detects specific activities and associates them with students in real-life classroom videos. Additionally, an interactive web-based application is developed to visualize human activity maps over long classroom videos.

Long-term video activity detection in classrooms presents challenges, such as multiple simultaneous activities, rapid transitions, long-term occlusions, duration exceeding 15 …

Toward Generating Efficient Deep Neural Networks, Chengcheng Li

Doctoral Dissertations

Recent advances in deep neural networks have led to tremendous applications in various tasks, such as object classification and detection, image synthesis, natural language processing, game playing, and biological imaging. However, deploying these pre-trained networks on resource-limited devices poses a challenge, as most state-of- the-art networks contain millions of parameters, making them cumbersome and slow in real-world applications. To address this problem, numerous network compression and acceleration approaches, also known as efficient deep neural networks or efficient deep learning, have been investigated, in terms of hardware and software (algorithms), training, and inference. The aim of this dissertation is to study …

Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman

Graduate Theses and Dissertations

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …

Getting A Handle On Floor Plan Analysis - Door Classification In Floor Plans And A Survey On Existing Datasets, João David, António Leitão

Architecture and Planning Journal (APJ)

Floor plan interpretation and reconstruction is crucial to enable the transformation of drawings to 3D models or different digital formats. It has recently taken advantage of neural-based architectures, especially in the semantic segmentation field. These techniques perform better than traditional methods, but the results depend mainly on the data used to train the networks, which is often crafted for the specific task being performed, making it hard to reuse for different purposes. In this paper, we conduct a literature survey on the existing datasets for floor plan analysis, and we explore how information regarding door placement and orientation can be …

Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn

SMU Data Science Review

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …

Recurrent Neural Networks For Flash Gdp Estimates In Ireland: A Comparison With Traditional Econometric Methods, Justin Flannery

ICT

GDP is the single most important barometer for the health of an economy. It’s an important input into the decision making processes of government, industry and state institutions such as central banks. To be useful as an indicator, GDP estimates need to be both timely and accurate. To meet the needs of users, many national statistical institutes publish early or flash estimates of GDP which are produced within 30 days after the end of a quarter. Given the long lags involved in the data collection processes which feed into GDP estimates, these flash estimates are often largely model based. Within …

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Application Of Shallow Neural Networks To Retail Intermittent Demand Time Series, Urko Allende

Dissertations

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting …

Structural Health Monitoring Using Machine Learning And Synthetic Data, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports

Structural health monitoring spans many decades of research across multiple engineering fields. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques. This dissertation explores the applicability of Machine Learning as a …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Hybrid Model For Making Decision Methods In Wireless Sensor Networks Through Neuro-Fuzzy Inference System, Martha Lucia Torres

Open Access Theses & Dissertations

Considering the complexity and multiple alternatives for technology decisions in Wireless Sensor Networks (WSNs), a multicriteria selection method (MCDM) is an appropriate approach for choosing the best option in technical projects. Purely quantitative decision-making procedures have currently been created based on client requirements and recommendations from industry professionals in many domains. In this context, implementation and operational costs could be increasing due to technical problems and additional processes. In order to prevent future difficulties and obtain a more accurate technology selection, a new method was being developed to involve qualitative and quantitative parameters taken from real scenarios and technical literature …

Artificial Intelligence And Applications, Sanjay Singh Dr.

Technical Collection

I work in the broad areas of computational intelligence, artificial intelligence, neural networks, machine learning, deep learning, game theory, mathematical logic, and natural language processing. I am also actively working in the area of algorithmic fairness and explainable AI (XAI). Currently, we are developing neuro-symbolic logic learning systems for common sense reasoning, which aims to augment the existing conventional artificial intelligence, which is logically based. The neuro-symbolic logic-based systems will provide more accurate results than their GOAI (Good Old Artificial Intelligence) version. We are also working on the area of abstractive summarization methods. We intend to develop an efficient abstractive …

Development Of Flood Prediction Models Using Machine Learning Techniques, Bhanu Kanwar

Doctoral Dissertations

"Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research …

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Impact Of Dedicated Bus Lanes On Intersection Operations And Travel Time Model Development, Stephen Arhin, Babin Manandhar, Kevin Obike, Melissa Anderson

Mineta Transportation Institute Publications

Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, …

Optimization Of Orbital Trajectories Using Neuroevolution Of Augmenting Topologies, Nathan Wetherell

University Scholar Projects

This project aims to determine the feasibility of using NeuroEvolution of Augmenting Topologies (NEAT), an advanced neural network evolution scheme, to optimize orbital transfer trajectories. More specifically, this project compares a genetically evolved neural network to a standard Hohmann transfer between Earth and Mars. To test these two methods, an N-body simulation environment was created to accurately determine the result of gravitational interactions on a theoretical spacecraft when combined with planned engine burns. Once created, this simulation environment was used to train the neural networks created using the NEAT Python module. A genetic algorithm was used to modify the topology …

Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor

Senior Theses

Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …

Autonomous Rock Segmentation From Lidar Point Clouds Using Machine Learning Approaches, Lauren E. Flanagan

Electronic Thesis and Dissertation Repository

Rover navigation on planetary surfaces currently uses a method called blind drive which requires a navigation goal as input from operators on Earth and uses camera images to autonomously detect obstacles. Images can be affected by lighting conditions, are not highly accurate from far distances, and will not work in the dark; these factors negatively impact the autonomous capabilities of rovers. By improving a rover's ability to autonomously detect obstacles, the capabilities of rovers in future missions would improve; for example, enabling exploration of permanently shadowed regions, and allowing faster driving speeds and farther travel distances. This thesis demonstrates how …

Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a …

Machine Learning Based Real-Time Quantification Of Production From Individual Clusters In Shale Wells, Ayodeji Luke Aboaba

Graduate Theses, Dissertations, and Problem Reports

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore.

Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

Development Of A Machine Learning Model To Characterize The Performance Of A Selective Catalytic Reduction On Filter After-Treatment System For A Heavy-Duty Diesel Engine, Samuel A. Okeleye

Graduate Theses, Dissertations, and Problem Reports

Particulate matter (PM) and Oxides of Nitrogen (NOx) are the major pollutants in diesel engines, an attempt to control one leads to an increase in the other, a phenomenon known as PM-NOx trade-off in diesel engine emission control. Currently, these two pollutants are controlled by the Diesel Particulate Filter (DPF) and the Selective Catalytic Reduction (SCR) after-treatment system respectively, in addition to the Diesel Oxidation Catalyst (DOC) which helps to provide 1:1 split of NO/NO₂ and helps with raising exhaust gas temperatures. Today, heavy-duty diesel engines feature a DPF, a primary SCR and a secondary SCR. Despite this complex …

Deepfakes Generated By Generative Adversarial Networks, Olympia A. Paul

Honors College Theses

Deep learning is a type of Artificial Intelligence (AI) that mimics the workings of the human brain in processing data such as speech recognition, visual object recognition, object detection, language translation, and making decisions. A Generative adversarial network (GAN) is a special type of deep learning, designed by Goodfellow et al. (2014), which is what we call convolution neural networks (CNN). How a GAN works is that when given a training set, they can generate new data with the same information as the training set, and this is often what we refer to as deep fakes. CNN takes an input …

Adaptive Mobile Eeg Noise Cancellation Using 2d Convolutional Autoencoders For Bci Authentication, Tyree Lewis

USF Tampa Graduate Theses and Dissertations

Electroencephalography (EEG) signals can be used for many purposes and has the potential to be adapted to various systems. When EEG is recorded from users, these studies are performed primarily in an indoor environment, while the user is stationary. This is due to the levels of noise that are experienced when recording EEG data, to minimize errors in the data. This thesis aims to adapt tasks that are performed indoors to an external environment by removing both noise and artefacts in EEG, using a 2D Convolutional Autoencoder (CAE). The data is recorded from subjects is passed into the 2D CAE …