Industrial Engineering Commons^™

Leveraging Synthetic Data For Efficient Training Of Ai Models For Real-World Object Detection, Reinaldo A. Moraga

Graduate Research Theses & Dissertations

Modern computer vision (CV) systems largely depend on real-world data for training, which is costly in terms of time, materials, and resources. As industries push toward automation and Artificial Intelligence (AI) -driven solutions, the need for enabling more efficient model training is growing. The primary aim of this work is to explore a framework tailored for industrial applications that uses synthetic images generated from 3D models to train a CV model capable of real-world object detection. This approach seeks to reduce the time, cost, and resources typically required for training AI models with real-world data. This work presents a method …

Digital Twin And Cybersecurity In Additive Manufacturing, Lidong Wang

Journal of Cybersecurity Education, Research and Practice

Additive manufacturing (AM) has been applied to automotive, aerospace, medical sectors, etc., but there are still challenges such as parts’ porosity, cracks, surface roughness, intrinsic anisotropy, and residual stress because of the high level of thermal gradient. It is significant to conduct the modeling and simulation of the AM process and achieve quality products. Digital Twin (DT) can help AM with forecasting defects/errors through simulation and real-time process monitoring. DT is a concept of Industry 4.0, and its digital structure reflects the real-time behaviors of a cyber-physical or physical system. This paper introduces the progress of DT applications in AM, …

Investigation Of Heavy Metal Composition And Associated Health Risks From Selected Groundwater Wells In Temeke, Dar-Es-Salaam, Justin M. Jeremiah

Tanzania Journal of Engineering and Technology (TJET)

Water quality continues to be the challenge to residents in Dar es Salaam due to various anthropogenic activities in the city. This study was conducted to assess groundwater quality by determining concentrations of heavy metals. Both probability and Nonprobability sampling techniques were used to collect samples from 40 groundwater wells in 24 wards of Temeke municipal and analysed using Microwave Plasma Atomic Emission Spectrometer (Agilent 4210 MP-AES) at the Geochemistry Laboratory of the University of Dar es Salaam. Results indicated varying heavy metal concentrations ranging from 0.71 ± 0.08 to 6.64 ± 0.21 mg/l for As, 0.02 ± 0.01 to …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore

USF Tampa Graduate Theses and Dissertations

This dissertation presents a comprehensive framework for enhancing organizational cybersecurity through data-driven intelligence. The research integrates multiple methodologies to tackle challenges in network intrusion detection and vulnerability management within cybersecurity operations centers (CSOCs). First, the research investigates vulnerability prioritization and mitigation techniques currently employed by CSOCs. To further streamline the vulnerability prioritization and mitigation process a machine learning (ML)-based Vulnerability Priority Scoring System (VPSS) is introduced, significantly improving the prioritization and mitigation of context-sensitive vulnerabilities. The VPSS outperforms traditional methods, reducing the cumulative vulnerability exposure score by up to 30% by considering both organizational context and vulnerability severity. Next, the …

A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless …

Sequential Optimization For Stressor-Informed Test Planning Through Integration Of Experimental And Simulated Data, Jacob Brecheisen

Data Science Undergraduate Honors Theses

This technical report details an innovative approach in reliability engineering aimed at maximizing system durability through a synergistic use of physical experimentation and computer-based modeling. Our methodology explores the efficient design and analysis of computer experiments and physical tests to facilitate accelerated reliability growth, while leveraging a sequential integration of data from these two distinct sources: costly physical experiments, characterized by random errors, and inexpensive computer simulations, marked by inherent systematic errors. The key innovation lies in the adoption of a closed-loop design and analysis method. This method begins by identifying a viable subset of important environmental stressors—such as temperature, …

Comparing North American Professional Sports League Season Formats Using Monte Carlo Simulation, Lathan Gregg

Industrial Engineering Undergraduate Honors Theses

Each NFL, NBA, and MLB season consists of a regular season, in which teams play a set number of scheduled games and a playoff, in which qualifying teams compete for a championship. At the conclusion of each season, teams are ranked based on their performance throughout the season. This study aims to investigate the ability of each league's season format to accurately rank teams using Monte Carlo simulation. Matches between two teams are simulated by using the team’s assigned strength ranks to calculate a winning probability for each team. The winning probabilities are simulated with different skill values, dictating how …

Assessment Of Risk Factors Related To Body Pain Complaints In Tanzania Construction Industry, Fatma K. Mohamed

Tanzania Journal of Engineering and Technology (TJET)

The construction industry is associated with risks that can result in musculoskeletal diseases. Although young male workers who are presumed to be healthy dominate the industry in Tanzania, body pain complaints have been widely reported. The aim of this study is to assess the prevalence and causes of body pains in workers. A cross-sectional study involving 396 workers was conducted. A chi square test was used for testing association of independent categorical variables and binary logistic regression analysis was used to determine predictors for body pain complaints. The results show that all study participants complained of at least one form …

Mathematical Modeling For Dental Decay Prevention In Children And Adolescents, Mahdiyeh Soltaninejad

Dissertations

The high prevalence of dental caries among children and adolescents, especially those from lower socio-economic backgrounds, is a significant nationwide health concern. Early prevention, such as dental sealants and fluoride varnish (FV), is essential, but access to this care remains limited and disparate. In this research, a national dataset is utilized to assess sealants' reach and effectiveness in preventing tooth decay, particularly focusing on 2nd molars that emerge during early adolescence, a current gap in the knowledge base. FV is recommended to be delivered during medical well-child visits to children who are not seeing a dentist. Challenges and facilitators in …

Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering

Theses and Dissertations

Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …

A Two-Stage Based Life Cycle And Principal Component Analysis For Decision Support Of Potential Municipal Solid Wastes Management Scenarios, Case Study Dar Es Salaam, Tanzania, Edwin Richard Ndibalema Dr

Tanzania Journal of Engineering and Technology (TJET)

In most of the urban areas of Tanzania, poor and inadequate solid waste management is a growing environmental and public health concern. Despite an array of solid waste management options, choosing an appropriate one remains a challenge. This study applied a novel two-stage-based approach to assess the environmental impacts and sustainability performance of the 27 scenarios for municipal solid wastes (MSW) management of Dar es Salaam City, Tanzania. In the first stage, the study utilized a life cycle analysis (LCA) to quantify the environmental impacts of MSW scenarios. In the second stage, the study used the principal component analysis (PCA) …

Containerization Of Seafarers In The International Shipping Industry: Contemporary Seamanship, Maritime Social Infrastructures, And Mobility Politics Of Global Logistics, Liang Wu

Dissertations, Theses, and Capstone Projects

This dissertation discusses the mobility politics of container shipping and argues that technological development, political-economic order, and social infrastructure co-produce one another. Containerization, the use of standardized containers to carry cargo across modes of transportation that is said to have revolutionized and globalized international trade since the late 1950s, has served to expand and extend the power of international coalitions of states and corporations to control the movements of commodities (shipments) and labor (seafarers). The advent and development of containerization was driven by a sociotechnical imaginary and international social contract of seamless shipping and cargo flows. In practice, this liberal, …

Product Costing And Pricing In Small And Medium Enterprises In Tanzania, Cosmas F Kindole

Tanzania Journal of Engineering and Technology (TJET)

metalwork small and medium enterprises located in Dar es Salaam with the aim of establishing methods used by SMEs in Tanzania in setting selling prices of their products. The study was necessitated by the apparent discrepancy of prices for similar products from SMEs. The approach used to gather information was mainly through direct interview and the use of questionnaires. Direct observation and literature review were also used to establish the whole costing and pricing practices in metal manufacturing SMEs. The findings of the study established that product selling practices of the surveyed SMEs were based on instinct and appearance of …

Rattus Norvegicus As A Biological Detector Of Clandestine Remains And The Use Of Ultrasonic Vocalizations As A Locating Mechanism, Gabrielle M. Johnston

Master's Theses

In investigations, locating missing persons and clandestine remains are imperative. One way that first responder and police agencies can search for the remains is by using cadaver dogs as biological detectors. Cadaver dogs are typically used due to their olfactory sensitivity and ability to detect low concentrations of volatile organic compounds produced by biological remains. Cadaver dogs are typically chosen for their stamina, agility, and olfactory sensitivity. However, what is not taken into account often is the size of the animal and the expense of maintaining and training the animal. Cadaver dogs are typically large breeds that cannot fit in …

Trace Dna Detection Using Diamond Dye: A Recovery Technique To Yield More Dna, Leah Davis

Master's Theses

This study aspires to find a new screening approach to trace DNA recovery techniques to yield a higher quantity of trace DNA from larger items of evidence. It takes the path of visualizing trace DNA on items of evidence with potential DNA so analysts can swab a more localized area rather than attempting to recover trace DNA through the general swabbing technique currently used for trace DNA recovery. The first and second parts consisted of observing trace DNA interaction with Diamond Dye on porous and non-porous surfaces.

The third part involved applying the Diamond Dye solution by spraying it onto …

Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez

Senior Design Project For Engineers

Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …

Lignin Derived Hydrophobic Deep Eutectic Solvents As Sustainable Extractants, Yuxuan Zhang, Qi Qiao, Usman Lame Abbas, Jun Liu, Yi Zheng, Christopher Jones, Qing Shao, Jian Shi

Markey Cancer Center Faculty Publications

Solvent innovation has become a central task for improving the sustainability of chemical processes1. Deep eutectic solvents (DESs) emerge as environmentally friendly alternatives to toxic and volatile organic solvents. One appealing aspect for DESs is that they can be synthesized using naturally occurring compounds from biomass. Herein, we prepared novel hydrophobic DESs based on lignin derivatives and characterized their physicochemical properties including density, viscosity, and thermal behavior. The results showed that five lignin-derived hydrophobic DESs made from menthol, thymol, and 2,6-dimethoxyphenol were promising as green solvents due to their low viscosities and environmentally friendly constituents. To evaluate the potential application …

Improving The Health And Safety Of Manufacturing Workers By Detecting And Addressing Personal Protective Equipment (Ppe) Violations In Real-Time With The Use Of Automated Ppe Detection Technology, Joseph Olufemi Fasinu

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Centers for Disease Control and Prevention (CDC) emphasized that Personal Protective Equipment (PPE) can significantly reduce the risk of occupational injuries and illnesses. However, improper use, failure to use, and other PPE-related violations can still result in injuries and fatalities. Eye and face protection violation has been one of the top 10 most frequently violated OSHA standards in fiscal years 2018, 2019, 2020, 2021 and 2022 consecutively. A common practice among safety professionals to ensure PPE compliance has been to physically inspect or monitor PPE usage among workers, which has been found to be unsustainable on a continuous real-time …

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …

The Rise Of Terrorism In Africa And What It Means For U.S. Policymakers, Bridget Mary Hughes

Master's Theses

This thesis utilizes three case studies to measure the rise of terrorism in Africa. This qualitative data leads the reader to one ultimate question: what does the rise in terrorism in Africa mean for the United States (U.S.) and U.S. policymakers, National Security academia, students in National Security, if anything at all? The answer is: if civilian and military compounds are being targeted by these violent extremists, as these three case studies demonstrate, it is my recommendation that U.S. policymakers cease sending troops to the African theater and, instead, allocate funding to the citizens and nonprofits of the countries whom …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy

Engineering Management & Systems Engineering Theses & Dissertations

The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …

Advancing Statistical Learning And Decision Modeling Using Irregularly-Sampled Multivariate Data For Managing Respiratory Diseases, Maryam Alimohammadi

Graduate Theses and Dissertations

Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. …

Computation Of Risk Measures In Finance And Parallel Real-Time Scheduling, Yajuan Li

Dissertations

Many application areas employ various risk measures, such as a quantile, to assess risks. For example, in finance, risk managers employ a quantile to help determine appropriate levels of capital needed to be able to absorb (with high probability) large unexpected losses in credit portfolios comprising loans, bonds, and other financial instruments subject to default. This dissertation discusses the computation of risk measures in finance and parallel real-time scheduling.

Firstly, two estimation approaches are compared for one risk measure, a quantile, via randomized quasi-Monte Carlo (RQMC) in an asymptotic setting where the number of randomizations for RQMC grows large, but …

Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley

Faculty Publications

Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad

Graduate Theses and Dissertations

In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …

Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan

Computer Science Theses & Dissertations

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …