Operations Research, Systems Engineering and Industrial Engineering Commons^™

Using Convolutional Neural Networks For Autonomous Drone Navigation, Joshua Jowers

Industrial Engineering Undergraduate Honors Theses

Unmanned Aerial Vehicles (UAVs), more commonly known as drones, serve various purposes, notably in military applications. Consequently, there arises a need for navigation methods impervious to intercepted signals [1]. Previous research has explored numerous solutions, including machine learning. This paper delves into a specific machine learning approach employing a Convolutional Neural Network (CNN) to discern image locations [2]. It elucidates the conversion of a CNN model between two machine learning libraries and presents results from multiple experiments examining parameters and factors influencing the approach's efficacy. These experiments encompass testing different data sources, image quantities, and processing pipelines to gauge their …

The Importance Of Data Preparation In A Data Science Problem, Sophia Beard

Data Science Undergraduate Honors Theses

This study is going to be based on an inventory outlier automation data science problem that is being solved to identify and prescribe inventory level outliers to help keep shelves stocked in terms of beverages. The objective of this paper will address why it is so important to understand the data that is involved in a particular data science problem and how planning ahead ensures a successful outcome in the data science world. In this data science project, Spatiotemporal Outlier Analysis for Inventory Intervention Automation, it was crucial for the team to understand, research, and visualize the data we were …

Great South Metals: Slitter Head Building Process, Nabhan Karim, Evan Swierski, Raven Morin, Patrick Kelsey, Michael Williams

Senior Design Project For Engineers

Great South Metals (GSM) operates two steel slitter machines, which take large coils of flat rolled steel and cut them into smaller strips for use in customers’ applications. Setting up the working components of each machine, called the slitter head, is a precise and arduous task. This system relies on several manually performed calculations and acute attention to detail; and thus, is prone to human error. The management at GSM commissioned our team to simplify the slitter head assembly process by creating an automated computer program and make additional recommendations to improve their operations.

The program we created is a …

The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley

Data Science Undergraduate Honors Theses

Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

Model-Based Comparison Of Biological Organism And Electro-Mechanical System Resiliency Strategies, Nicholas Ratycz

Mechanical Engineering Undergraduate Honors Theses

Bio-inspired design has been used by many engineers to solve difficult problems or to make manufacturing processes more efficient. Biomimetics is the study of implementing the structure or function of biological substances, materials, mechanisms, and processes onto artificial ones that mimic the original. The goal of the BIASD tool is to provide bio-inspiration for engineers by studying the fault-adaptive strategies that biological systems use, rather than just their structure or function. In this thesis, the fault adaptive strategies of both a biological example and that of a real cubesat are compared using three types of model-based system diagrams to show …

Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory, Megan Sickinger

Master's Theses

In this work, we study the use of modern portfolio theory in a cost-risk analysis of the Electric Reliability Council of Texas (ERCOT). Based upon the risk-return concepts of modern portfolio theory, we develop an n-asset minimization problem to create a risk-cost frontier of portfolios of technologies within the ERCOT electricity region. The levelized cost of electricity for each technology in the region is a step in evaluating the expected cost of the portfolio, and the historical data of cost factors estimate the variance of cost for each technology. In addition, there are several constraints in our minimization problem to …

Energy Optimization In Pneumatic Systems Using Ens-200 Energy Saving Trainer, Maha Guru Shankar Perumal

Open Access Theses & Dissertations

Various industries such as manufacturing and assembly, the automotive sector, printing and paper handling, packaging, aerospace, etc., widely use pneumatic systems for a variety of operations. Enterprises use pneumatic systems for cost-effectiveness, reliability, and simplicity. However, they also have certain inefficiencies, leading to high operating costs and reduced efficiency in various applications. The focus of this research is on energy optimization in pneumatic systems to improve efficiency in industrial applications, minimize energy waste, and reduce operational costs. The best practices for enhancing energy efficiency in pneumatic systems were proper system design, pressure regulation, leak detection and repair, energy-efficient components, and …

Investigating Potential Improvements To Mbse Software To Support Dodaf, Jennifer Marie Kimpson

Theses and Dissertations

The integration of models created through model-based systems engineering methods with DoDAF is not a new concept. By creating DoDAF-aligned models, DoD program managers are following DoD guidelines to hopefully achieve more effective communication with stakeholders and improve efficiency of their programs. With the use of MBSE tools to create these models, DoD program managers are able to take further advantage of the benefits of MBSE methods. However, even with the use of MBSE tools, there continue to be issues throughout DoD programs. The literature has shown that a continuous improvement approach regarding the use of MBSE tools with DoDAF …

Rsm For The Optimization Of Selective Laser Melting Process Parameters For Manufacturing Initial Layers Of Horizontal Overhang Structures, Prince Yaw Asamani

Theses and Dissertations

While prevalent in 3D printed objects, overhang features are difficult to print with selective laser melting due to the limitation of a purely vertical laser. To address this issue, overhang features are typically built on temporary supports, which are removed following printing. However, this results in wasted material and longer production time. Several authors have studied printing overhangs without support structures. However, there has not been a systematic approach to studying the effect of multiple process parameters on the quality of a printed overhang. This thesis investigates the effect of laser power, scanning speed, and hatch distance on the surface …

A Dedicated Lane Analysis For Supply Chain Resilience In The U.S.-Mexico Border: Cost-Comparison And Simulation Models, Carlo A. Zorola Gonzalez

Theses and Dissertations

Supply chains have been actively developing and implementing strategies to enhance resilience in response to various disruptive events like the COVID-19 pandemic, hurricanes, geopolitical tensions, and climate change. These strategies aim to address demand and supply imbalances, logistical challenges, and policy restrictions encountered in transborder commerce. One area significantly impacted by such disruptions is cross-border trade between the US and Mexico. Understanding these strategies is crucial for achieving supply chain resilience, defined as the ability of a supply chain to quickly adapt to sudden disruptions without affecting the flow of goods. In both the US-Mexico and US-Canada borders …

Application Of An Information Gain Model In A Motor Learning Task: An Application In A Laparoscopic Surgical Skills Task, Matthew Ball

All Theses

Psychomotor skills are comprised of physical movements and mental adaptations of movements to complete a task, generally using complex tools or components. Laparoscopic surgery training tasks are a prime example of tasks requiring psychomotor skills due to their demand for both fine hand movements and constant mental adaptation to the changing environment of the human body. A typical evaluation of these skills can be accomplished using learning curves, a graphical representation of skill growth in which a performance metric is plotted over a time experience metric. Previously, in the literature, several learning curves have been generated from the performance within …

A Transdisicplinary, Survivor-Centered Approach For Creating A System Dynamics Model Of Closed-Buyer Sex Trafficking Networks, Kemonte Yow

All Theses

One of the largest drivers of sex trafficking operations is the demand for commercial sex. To date, research from a quantitative side typically focuses on understanding how to best identify and remove victims from their trafficking situation and then support their healing and recovery. This may be because this is where data exists (in limited forms) to support quantitative modeling. This thesis broadens that scope to understand how demand for commercial sex impacts trafficking operations. In particular, this research implemented a transdisciplinary, survivor-centered approach to build a system dynamics model of a closed-buyer sex trafficking network, which is a network …

Depression And Anxiety: Investigating The Impacts Of Scheduling On The Mental Health Of Healthcare Workers During The Covid-19 Pandemic, Christofer Gonzaga

All Theses

Depression is a growing problem in the United States and has ballooned into a globally recognized mental health issue that affects shift workers in various fields (Lee et al., 2017). Rates of depression and anxiety have been shown to be substantially increasing and the COVID-19 pandemic amplified this prevalence of depression and anxiety. In the United States, the prevalence of depression symptoms was more than 3- fold higher during COVID-19 compared to before the pandemic (Ettman et al., 2020). The delivery of healthcare services is an ongoing and dynamic process, requiring clinicians to work in various shifts to ensure the …

A Design Science And Implementation Framework For Studying Interruptions And Its Application In A Dental Clinic., Carsten Ziegler

Electronic Theses and Dissertations

Interruptions during dental treatment are frequent, and often have detrimental effects on quality of care, patient safety and processing times, and have an impact on provider satisfaction and performance. Interruptions can significantly increase the cognitive load on dental providers, making it hard for them to concentrate on complex procedures. Frequent interruptions also disrupt the workflow in general, leading to delays in treatment, which can reduce the overall throughput of a clinic. Unfortunately, there is no comprehensive approach to collectively (i) analyze interruptions in dental healthcare, (ii) design an intervention, and (iii) implement and sustain it. This research proposes a comprehensive …

Reduction Of The Length Of Stay At The Emergency Department Of The Audie L. Murphy Hospital, Arwa Al Shikrian

Theses

We implemented the DMAIC framework (Define, Measure, Analyze, Improve, and Control) and Lean Six Sigma methods to reduce the length of stay (LOS) in the Emergency Department (ED) at Audie L. Murphy VA Hospital, a crucial issue affecting operational efficiency and patient care quality. This project was conducted in conjunction with an internship at the Hospital; our efforts were an integral part of a Green Belt project being conducted by Mr. Roarke Verkaik-Bushby, Hospital Administration Service, to

whom this author reported.

We employed quantitative research design, meticulously observing, measuring, and analyzing ED processes. We tracked patient flow, identified bottlenecks, and …

An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko

Research Collection School Of Computing and Information Systems

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Statistical And Machine Learning Analysis In Brain-Imaging Genetics: A Review Of Methods, Connor L. Cheek, Peggy Lindner, Elena L. Grigorenko

Engineering Management and Systems Engineering Faculty Research & Creative Works

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate …