Engineering Commons^™

Ai Integration For Intellisar, Eric Lee

College of Engineering Summer Undergraduate Research Program

IntelliSAR aims to integrate AI techniques into Search and Rescue (SAR) operations, building on the foundation laid by previous SURP initiatives. IntelliSAR’s core elements include a front-end for SAR forms, a comprehensive command center dashboard, and AI-driven components designed to enhance SAR decision-making. During summer, our efforts focused on streamlining the user interface by integrating various machine learning models into a unified, interactive dashboard. Our models predict critical factors such as missing persons’ behavior, potential locations, and resource requirements, with the goal of optimizing response times and improving the effectiveness of SAR teams.

Computer Vision In A Robotic Arm, Jack Maxwell

College of Engineering Summer Undergraduate Research Program

We used a machine learning-based object detection algorithm to give a robotic arm the ability to "see" with its camera.

Deep-Learning Based Microstructure Reconstruction And Generation, Cameron J. Maloney, Lucas Taliaferro

College of Engineering Summer Undergraduate Research Program

Characterizing the microstructural behavior of materials is crucial for understanding their properties and performance. Traditional imaging methods, such as optical microscopy and electron microscopy, are effective but costly and time-consuming. Computational approaches can reduce costs and time while expanding the accessibility of microstructural analysis through the generation of new microstructure images. Traditional computational approaches, namely descriptor-based approaches, are slow but effective in low-data scenarios. Modern approaches use machine learning (ML), which is faster but often requires a lot of data to approach the performance of descriptor-based methods. This research leverages a special data-efficient Generative Adversarial Network (GAN) architecture to artificially …

Surrogate Models For Stress-Strain Mapping Of Microscale Physics, Samuel Roach, Dr. Eric Ocegueda

College of Engineering Summer Undergraduate Research Program

•Learn the difference between different neural networks within machine learning (ML) •Develop a working understanding of the ML tool Pytorch and machine learning operator: Recurrent Neural Operator •Use MATLAB to create and process time dependent stress/strain matrices to display the hyper-parameters for different RNOs •Apply RNO to train the strain-stress mapping of tri-laminate and granular cases

In-Situ Characterization And Machine Learning Applications For Composite Processing, Pragathi Agraharam Chan

Doctoral Dissertations and Master's Theses

Numerous aircraft and spacecrafts utilize carbon fiber-reinforced polymer structures to significantly enhance their operational efficiency and overall performance. Autoclave composite processing offers a solution for the intricate design of complex structures. However, the rapid temperature and strain fluctuations experienced during the processing gives rise to a multitude of defects and residual stresses in the composite. In this study, we have devised an in-situ methodology that leverages Digital Image Correlation (DIC) and Machine Learning Applications to effectively observe and track defect deformations occurring throughout the process. This process presents a dataset derived from the curing process of 40 carbon fiber-reinforced polymer …

Data-Driven Discovery Of Extreme Thermal Materials By High-Throughput Computation And Machine Learning, Joshua Ojih

Theses and Dissertations

The quest for materials with extraordinary properties has been a longstanding endeavor in material science and engineering, driving future technological advancement. However, the discovery of such materials is non-trivial. Recent advancements in computational methods, particularly the integration of machine learning (ML) techniques with density functional theory (DFT), have opened new avenues for accelerating the discovery of materials with exceptional and extreme properties. This dissertation focuses on developing a synergistic approach and workflow combining ML and DFT to identify materials with properties that are pushed beyond current limits, using lattice thermal conductivity (LTC) as a case study of the workflow.

We …

Redefining Gas Turbine Engine Development: A Digital Twin Framework Informed By Operational Dynamics And Numerical Analysis, Sowmya Raghu

Theses and Dissertations

Gas Turbine Engines (GTEs) serve as primary propulsion systems in aviation and are key for power generation units in various industrial applications. The conventional Gas Turbine Engine Development and Monitoring Lifecycle (EDML) typically encompasses six stages: preliminary design, numerical analysis, prototyping and testing, manufacturing, systems integration, and subsequent systematic monitoring processes. This dissertation redefines the gas turbine engine design and development process by synergistically integrating design capabilities, real-time operational data, and predictive maintenance through the implementation of digital twins. The primary objective is to establish a comprehensive framework for gas turbine engine design by utilizing thermodynamic and aerodynamic modeling, supported …

Unveiling The Risks Of Speeding Behavior By Investigating The Dynamics Of Driver Injury Severity Through Advanced Analytics., Mouyid Islam, Parisa Hosseini, Anahita Kakhani, Mohammad Jalayer, Deep Patel

Henry M. Rowan College of Engineering Departmental Research

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables …

Methods For Efficient Computation Of Neutron Multiplicity Distributions And Snm Sample Characterization, Jawad Ribhi Moussa

Nuclear Engineering ETDs

This dissertation advances neutron multiplicity counting (NMC) by developing a hierarchy of computational methods that combine deterministic and stochastic techniques with modern machine learning for improved special nuclear material (SNM) characterization. A system state-updating Monte Carlo method is introduced in addition to a dynamic point kinetic model based on a backward Master equation (BME) formulation. These models, along with newly developed distribution reconstruction methods, enable more efficient computation of neutron count distributions in a fixed detector time-gate. NMC limitations associated with finite-size and neutron phase effects are also addressed by extending the BME model to account for time-gated neutron count …

Resting-State Eeg Microstate Features For Major Depressive Disorder Classification, George M. V. Quinn

Dissertations, Theses, and Capstone Projects

Neuroimaging studies have revealed consistent abnormalities in functional connectivity within specific neural networks that may serve as biomarkers for major depressive disorder (MDD). It is important to find inexpensive, non-invasive techniques that target these biomarkers to make diagnosis easier and more objective. EEG microstates are quasi-stable potential topographies that are thought to reflect the quasi-stable network activity of the underlying neural generators. MDD has been shown to alter features of the four canonical EEG microstates (A, B, C, D) with some conflicting results. The most consistent network abnormalities in MDD are found in the anterior default mode network, and this …

Seal Counting On Our Plages (S.C.O.O.P.), Kaanan Kharwa

Master's Theses

The Vertebrate Integrative Physiology (VIP) lab monitors the population of northern elephant seals at the largest mainland breeding colony, located at Piedras Blancas (San Simeon, CA). As the population expands, more human-seal interactions and conflicts over land use occur. The VIP lab's work informs California State Parks and helps with the management of the rookery. Currently, members of the VIP lab fly a drone over the beaches, capture multiple images, and manually count the seals, which takes around 14 to 21 hours of analysis per survey. Machine learning methods such as Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks …

An Adaptive Hybrid Deep Learning Architecture For Providing Guaranteed Qos In 5g Cellular Networks, Rajilal Mv Ms

Theses and Dissertations

Wireless network systems must have effective resource allocation, particularly in the context of 5G networks when flexibility is needed to meet a range of network requirements. Resource allocation is essential in cellular network contexts to guarantee equitable access to customers, partners, and cellular service users. Since resource distribution determines network performance, it offers significant advantages when executed well. One of the biggest issues with 5G technology is resource allocation, particularly when it comes to the Quality of Service (QoS) for various applications. Resources in wireless networks include items like channels, power, and spectrum; these must all be apportioned according to …

Metabolite Biomarker Discovery For Lung Cancer Using Machine Learning, Ariski Fajarido, Linda Erlina, Aryo Tedjo, Fadilah Fadilah, Wawaimuli Arozal

Indonesian Journal of Medical Chemistry and Bioinformatics

Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of …

Design And Development Of A Clinical Decision Support System For The Diagnosis Of Parkinson’S Disease Using Artificial Intelligence, Saravanan S

Theses and Dissertations

Parkinson’s disease (PD) is a degenerative neurological condition marked by motor symptoms like tremors, bradykinesia, and stiffness. It is observed that early and precise diagnosis of this disease is crucial, as that will have a significant impact on effective disease management and intervention. This thesis explores the technical feasibility of applying AI techniques to recognize patterns from spiral and wave drawings, which are usually a unique signature type for PD patients.

The focus of the work is to diagnose the disease through novel deep transfer learning techniques, to diagnose the severity of the disease, and also to develop effective model …

Enhancing Fundraising Strategies In Higher Education Through Machine Learning, Laith Alatwah

Electrical Engineering Theses

This thesis presents a comprehensive application of machine learning techniques, namely Fine Gaussian SVM and RUS Boosted Trees, to enhance fundraising strategies in higher education institutions. Analyzing a rich dataset from Blackbaud Raiser's Edge NXT, spanning 2012 to 2022, the study focuses on donor profiles, including demographics, donation history, and engagement patterns. Key demographic insights include the increasing engagement of younger donors (20-29 age group) and significant contributions from older donors (70-99 age group). Geographical trends are also examined, revealing distinct patterns based on donors' city, state, and ZIP code. The Fine Gaussian SVM model demonstrates moderate discriminatory power, with …

Natural Language Processing For Automated Sysml Diagram Generation, Joshua Andre Ontiveros

Theses and Dissertations

This thesis explores the applications of natural language processing (NLP) techniques in model-based system engineering (MBSE) to help generate System Modeling Language (SysML) diagrams. MBSE is a method that aids in enhancing traditional engineering practices by modeling to help improve understanding and communication in systems development. SysML, one of the modeling languages for MBSE, helps represent a system's architecture, behavior, and information flow. Translating systems requirements and specifications into SysML models can be time-consuming and can lead to errors when created manually. Automating the creation of SysML diagrams from textual descriptions with the help of NLP techniques can aid in …

Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay, S M Atikur Rahman

Open Access Theses & Dissertations

Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) …

Feature Extraction From Vibration Signature Acquired From Railroad Bearing Onboard Condition Monitoring Sensor Modules, Kevin Quaye

Theses and Dissertations

The University Transportation Center for Railway Safety (UTCRS) has designed an onboard monitoring system that tracks vibration waveforms over time, to obtain a direct and more accurate indicator of bearing health. The data collected by these sensors is used for vibrational analyses of the bearings. The speed of the bearing is a fundamental parameter needed to carry out the analysis. GPS data can be used to determine bearing speed if available; however, due to size, cost and power constraints GPS is typically not available at the sensor location. This means that analysis must be delayed until the data is uploaded …

Using Efficient Vision Transformers To Improve Perception Systems In Autonomous Off-Road Vehicles, Adam S. Pickeral

All Theses

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also be quick to make decisions. In Autonomous Vehicle research, the environment perception system is one of the key com- ponents of development. The environment perception system allows the vehicle to understand its surroundings using cameras, light detection and ranging (LiDAR), and other sensor systems or modalities. Deep learning computer vision algorithms have shown to be the …

Physics-Informed Machine Learning Methods For Inverse Design Of Multi-Phase Materials With Targeted Mechanical Properties, Yunpeng Wu

All Dissertations

Advances in machine learning algorithms and applications have significantly enhanced engineering inverse design capabilities. This work focuses on the machine learning-based inverse design of material microstructures with targeted linear and nonlinear mechanical properties. It involves developing and applying predictive and generative physics-informed neural networks for both 2D and 3D multiphase materials.

The first investigation aims to develop a machine learning method for the inverse design of 2D multiphase materials, particularly porous materials. We first develop machine learning methods to understand the implicit relationship between a material's microstructure and its mechanical behavior. Specifically, we use ResNet-based models to predict the elastic …

Hybrid Physics-Infused Machine Learning Framework For Fault Diagnostics And Prognostics In Cyber-Physical System Of Diesel Engine, Shubhendu Kumar Singh

All Dissertations

Fault diagnosis is required to ensure the safe operation of various equipment and enables real-time monitoring of associated components. As a result, the demand for new cognitive fault diagnosis algorithms is the need of the hour. Existing deep learning algorithms can detect, classify, and isolate faults. Still, most depend solely on data availability and do not incorporate the system's underlying physics into their prediction. Therefore, the results generated by these fault-detecting algorithms sometimes need to make more sense and deliver when tested in actual operating conditions.

Similar to diagnosis, the fault prognosis of diesel engines is paramount in numerous industries. …

Rapid Prediction Of Buoyancy-Driven Exchange Flows At The Great Salt Lake: Ml Models And A 1d Shallow Water Approach, Eric M. Larsen

All Graduate Theses and Dissertations, Fall 2023 to Present

The Great Salt Lake in Utah, USA, is a hypersaline terminal lake divided in to northern and southern arms by the Union Pacific Railroad causeway since the 1950's. This separation has caused a difference in density and water surface elevation between lake arms. These differences result in a buoyancy-driven exchange flow occurring through an engineered breach in the causeway. Traditionally, modeling the flow through the breach has been done by numerically solving the 1D steady shallow water equations, and using computational fluid dynamics (CFD). The CFD models yield high accuracy results, but require substantial computing resources. This research proposes the …

Automated Measurement Of The Water Drop Penetration Time For The Analysis Of Soil Water Repellency, Danxu Wang

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this work, we develop an innovative system for the automated measurement of Water Drop Penetration Time (WDPT) - a parameter that is conventionally used for evaluating soil water repellency (SWR). Increased SWR can be a reason for plant stress and poor crop yields, create a risk of potential water runoff and floods and thus can pose risks to life and property loss. Timely evaluation of soil conditions can save resources and win time for responding to environmental disasters. Manual measurements of WDPT are labor-intensive, subjective, tend to produce variability of outcomes, and also not always available in remote or …

Real-Time Gun Detection In Video Streams Using Yolo V8, Harish Kumar Reddy Kunchala

Electronic Theses, Projects, and Dissertations

In this research, we advance the domain of public safety by developing a machine learning model that utilizes the YOLO v8 architecture for real-time detection of firearms in video streams. A diverse and extensive dataset, capturing a range of firearms in varying lighting and backgrounds, was meticulously assembled and preprocessed to enhance the model's adaptability to real-world scenarios. Leveraging the YOLO v8 framework, known for its real-time object detection accuracy, the model was fine-tuned to accurately identify firearms across different shapes and orientations.

The training phase capitalized on GPU computing and transfer learning to expedite the learning process while preserving …

Evaluation Of Machine Learning Algorithms And Statistical Analysis For Predicting Crash Severity And Determining Contributing Factors: A Comparative Study, Arian Golrokh Amin

Civil Engineering ETDs

Improving road traffic safety relies on the ability to accurately predict traffic crashes and understand the factors contributing to severe injuries. This study employs a dual approach, utilizing both statistical analysis and machine-learning methodologies. Initially, statistical analyses such as chi-squared tests and Cramer's V values are applied to a decade's worth of police report data from Albuquerque, New Mexico, to identify primary causal factors behind crashes. Subsequently, machine-learning models are developed and rigorously compared to predict the severity of traffic crash injuries. The selection of the most effective algorithm is based on comprehensive metrics including accuracy, precision, recall, and F1 …

Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas

Theses and Dissertations

This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …

Development Of Satellite-Assisted Forecasting System For Vibrio Vulnificus Prevalence In Coastal Waters, Saber Aradpour

LSU Doctoral Dissertations

Vibrio vulnificus is a halophilic gamma proteobacterium that is autochthonous to coastal waters and is responsible for 50% of seafood-related deaths in the United States. This study presents a series of nowcasting and forecasting models for predicting vibrio vulnificus abundance in oysters by identifying the long-range dependence of vibrio vulnificus abundance on antecedent environmental conditions and detecting the environmental conditions using satellite remote sensing technology. It was discovered that vibrio vulnificus abundance exhibits a long-range dependence on antecedent environmental conditions which can be characterized by seven independent environmental predictors (stressors) including sea surface temperature (SST), water level (WL), sea surface …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

Vysion Software, Isaias Hernandez-Dominguez Jr, Chander Luderman Miller

2024 Symposium

Vision loss presents significant challenges in daily life. Existing solutions for blind and visually impaired individuals are often limited in functionality, expensive, or complex to use. Vysion Software addresses this gap by developing a user-friendly, all-in-one AI companion app that provides features including text summarization, real-time audio descriptions, and AI-enhanced navigation. This project details the development plan, initial functionalities, and future vision for Vysion Software.

Machine Learning-Guided Design Of Nanolubricants For Minimizing Energy Loss In Mechanical Systems, Kollol Sarker Jogesh, Md. Aliahsan Bappy

Mechanical Engineering Faculty Publications

This study explores the significant potential of machine learningguided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the …