Operations Research, Systems Engineering and Industrial Engineering Commons^™

Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties, Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Genda Chen, Cihan H. Dagli

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP …

Resources Based Planning Framework For Infrastructure Maintenance And Rehabilitation Projects, Heba Gad

Theses and Dissertations

Infrastructure maintenance and rehabilitation projects involve activities scattered over a large geographical area (e.g., scattered road segments maintenance, telecom towers maintenance program, etc.). Planning such projects require a resource-based approach that accounts for the implications of resource mobility between activities’ locations in terms of time & cost. Existing scheduling techniques fall short of addressing the unique challenges of the scattered nature of these projects in combination with organization's limited resources availability. To address this need, this research presents a resources-based planning framework for infrastructure maintenance and rehabilitation scattered projects with the objective of enhancing resources utilization achieving time and cost …

Data-Efficient, Federated Learning For Raw Network Traffic Detection, Mikal Willeke, David A. Bierbrauer, Nathaniel D. Bastian

ACI Journal Articles

Traditional machine learning (ML) models used for enterprise network intrusion detection systems (NIDS) typically rely on vast amounts of centralized data with expertly engineered features. Previous work, however, has shown the feasibility of using deep learning (DL) to detect malicious activity on raw network traffic payloads rather than engineered features at the edge, which is necessary for tactical military environments. In the future Internet of Battlefield Things (IoBT), the military will find itself in multiple environments with disconnected networks spread across the battlefield. These resource-constrained, data-limited networks require distributed and collaborative ML/DL models for inference that are continually trained both …

Improving Inventory For A Large Food Service Supplier, John Krolick, Chrsitian Ingersoll, Joseph Fuentes, Harmoni Blackstock, John Miller, Alexander Contarino

Mathematica Militaris

Golden State Foods (GSF) is an international food service supplier. Their Georgia manufacturing plant currently produces thousands of condiments for restaurants across the United States. This project analyzed the inventory policies of seasonal ingredients, in order to decrease GSF’s working capital and inventory holding costs. By inputting product recipes, ingredient usage, and weekly inventory data into a dynamic lot-sizing model, we predict optimal order quantities and reorder points for GSF’s seasonal and expensive ingredients. This will potentially decrease the facility’s $1.2 million holding cost and increase its long-term profits compared to the status quo.

Prioritizing Ports And Waterways Safety Assessments (Pawsas), Matalynn Clark, Peyton Phillips, Claire Portigue, Justin Canovas, Eric Johnson, Andrew Zuckerman

Mathematica Militaris

A Ports and Waterways Safety Assessment (PAWSA) is a discussion forum facilitated by the United States Coast Guard Navigation Center (NAVCEN) to analyze the state of a port or waterway as it relates to navigation, vessel traffic, and physical attributes of the waterway. A successful workshop requires the collaboration between various stakeholders such as waterway users, environmental interest groups and local law enforcement. Without involving the relevant parties and encouraging their insight, the USCG risks creating an incomplete understanding of the port’s dynamic.

This project examines the characteristics of eight ports across the U.S. to determine which is in most …

Tank Level Controller Plc Lab, Siddhi Upadhyaya, Teghvir Grewal

Electrical Engineering

The California Polytechnic State University San Luis Obispo’s Electrical Engineering Department is currently developing lab experiments for the new EE435 (Industrial Power Control and Automation) class. In order to support these efforts, fourth year Cal Poly students are expected to develop laboratory experiments that will be conducted during this new class for the semester system. The lab experiment focused on in this project is called the Tank Level Controller. This experiment will introduce EE 435 students to Schneider Electric Programmable Logic Controller (PLC) hardware and software, which is prominent in the automation industry.
This experiment will require students to develop …

Constrained Optimization Based Adversarial Example Generation For Transfer Attacks In Network Intrusion Detection Systems, Marc Chale, Bruce Cox, Jeffery Weir, Nathaniel D. Bastian

ACI Journal Articles

Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious network packets without requiring feature engineering. Adversarial machine learning methods have been used to evade classifiers in the computer vision domain; however, existing methods do not translate well into the constrained cyber domain as they tend to produce non-functional network packets. This research views the payload of network packets as code with many functional units. A meta-heuristic based generative model is developed to maximize classification loss of packet payloads with respect to a surrogate model by repeatedly substituting units of code with functionally equivalent counterparts. The …

Passive Vs. Active Wearable Technology Monitoring Trunk Flexion In Elementary Teachers, Bailey Jose

Theses and Dissertations

The objective of this study was to assess the biomechanical and subjective measures of elementary school teachers while wearing active and/or passive wearable devices during the average workday. Five elementary school teachers wore a harness that held an Upright GO 2 posture tracking device and a Vicon Blue Trident sensor on the participant's upper back for two school days. Haptic feedback was on for one day and off for the other. Data from the Vicon wearable was analyzed to determine participants’ trunk flexion severity, frequency, and duration. Surveys were used to determine perceived exertion and perception of wearable technology. This …

Adaptive Large Neighborhood Search Algorithm – Performance Evaluation Under Parallel Schemes & Applications, Sandip Kumar

Theses and Dissertations

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a …

A Generalizable Method And Case Application For Development And Use Of The Aviation Systems – Trust Survey (As-Ts)., Jamison Hicks

Theses and Dissertations

Automated systems are integral in the development of modern aircraft, especially for complex military aircraft. Pilot Trust in Automation (TIA) in these systems is vital for optimizing the pilot-vehicle interface and ensuring pilots use the systems appropriately to complete required tasks.

The objective of this research was to develop and validate a TIA scale and survey methodology to identify and mitigate trust deficiencies with automated systems for use in Army Aviation testing. There is currently no standard TIA assessment methodology for U.S. Army aviation pilots that identifies trust deficiencies and potential mitigations.

A comprehensive literature review was conducted to identify …

High-Level Requirements For Conceptual Design Of Bridge Deflection Measurement System Using Model-Based Systems Engineering, Mariana Villalabeitia Arenas

Theses and Dissertations

Bridges are essential in the infrastructure transportation system. Repairs and maintenance are the key activities to keep them safe for use. Over the years, the evolution of technology has been applied to improve the way bridges are designed, built, monitored, inspected, and repaired. Nevertheless, there is still a gap between the research efforts and the application of this knowledge in the practical field of bridge inspections. Model-based Systems Engineering is a formalized methodology in the system design that is centered around the model throughout all the life cycle stages of a system, supporting the requirements, design, analysis, verification, and validation …

Automated Visualization Pipeline For Near Real-Time Risk Management System, Paris Joslin

Industrial Engineering Undergraduate Honors Theses

In modern society, technological capabilities and the amount of data readily available to users continue to grow exponentially. Many have adopted these new capabilities but lack the infrastructure needed to efficiently utilize high-powered software and programs. Without a method to collect, store, and process large datasets in real-time, individuals and businesses can quickly become overwhelmed, inhibiting effective decision-making processes. There is potential to improve decision-making abilities by enhancing the computing infrastructure. To accomplish this task, we will explore the ideas surrounding High Performance Computing (HPC) and data visualization software. High Performance Computing is the ability to process data and perform …

Mapping Information Flows That Support Adaptive Capacity In The Sterile Processing Department (Spd), Jacob Hionis

All Theses

The Sterile Processing Department (SPD) is the part of the hospital that is responsible for decontaminating, assembling, and sterilizing surgical trays for use in the Operating Rooms (ORs). Errors in the SPD can lead to delayed surgeries or patient harm. The SPD is a complex system, with multiple interconnected subdepartments and roles. Complex systems require coordination between stakeholders. This study aims to discover how an organization can adapt to scale, and how information flows support these adaptations. In this study, observations, semi-structured interviews, and on-the-job interviews were conducted with members across the SPD and OR to elicit pressures, adaptations, and …

Design Of Self-Actualized Ad Recommendation Systems, Karishma Patil

All Theses

Traditional recommender systems trap users in filter bubbles, in an attempt to personalize the suggestions to users’ preferences, these systems can limit users’ perspectives and promote complacency. This paper takes a user-centered design approach towards creating a self-actualized ad recommendation system, that supports self-discovery, goal achievement, and seeking of relevant recommendations that encourage users’ self-actualization needs and go beyond algorithmic recommendations.

Network Effects Of Emergency Department Clinician Strain And Patient Congestion, Aisha Nelson

All Theses

We develop a discrete-event simulation model to study how staffing strain affects patient outcomes across a network of Emergency Departments (EDs). The aim is to observe how clinician staffing and transfers throughout the system affect the system’s behavior. We will study the network of the seven EDs in the Prisma Health-Upstate system. Patient acuity and resource need are stratified using the five-level Emergency Severity Index (ESI). Patient flow data within and between EDs are collected from EPIC, staffing data from ShiftAdmin, and environmental COVID-19 prevalence data from the Department of Health and Environmental Control. Time periods include the Omicron wave …

Studying Pedestrian’S Unmarked Midblock Crossing Behavior On A Multilane Road When Interacting With Autonomous Vehicles Using Virtual Reality, Fengjiao Zou

All Dissertations

This dissertation focuses on the challenge of pedestrian interaction with autonomous vehicles (AVs) at unmarked midblock locations where the right-of-way is unspecified. A virtual reality (VR) simulation was developed to replicate an urban unmarked midblock environment where pedestrians cross a four-lane arterial roadway and interact with AVs. One research goal is to investigate the impact of roadway centerline features (undivided, two-way left-turn lane, and median) and AV operational schemes portrayed through on-vehicle signals (no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications) on pedestrian crossing behavior. Results demonstrate that both roadway centerline design features and AV operations and signaling show …

Evaluation Of Maternal Patient Experience During Covid-19 Using Natural Language Processing, Debapriya Banik

Open Access Theses & Dissertations

Healthcare policymakers are constantly investigating how to improve this situation and provide a more patient-centered care. Delivering excellent medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, like HCAHPS, to measure their patients' experiences. The United States has the highest maternal mortality or morbidity rate of the developed countries, so we used maternal patients as the patient cohort to evaluate various touchpoints. The power of social media can be harnessed to provide researchers with valuable insights into understanding patient's experience and care. We used the "COVID-19Tweets" Dataset, which has over twenty-eight million …

Challenges, Limitations, And Strengths For An Optimal Predictive Maintenance Application, Erick Armando Rosales

Open Access Theses & Dissertations

Industry 4.0, the fourth industrial revolution, has emerged as the most recent digital transformation worldwide, expanding and reshaping the manufacturing industry by introducing novel technologies. In Industry 4.0, Smart Manufacturing (SM) and the Internet of Things (IoT) have collaborated to bring the best of both worlds and make the new manufacturing era more cost-effective, automated, and digitized. As a result, many businesses are putting sensors, intricate networks of integrated systems, big data analytics, cloud computing, and storage in place to use predictive maintenance (PdM) best. PdM uses IoT to convert physical activities into digital activities, also known as digitization. Predictive …

Causal Modeling Framework For Nuclear Power Plant Licensing Process, Lauren Kimberly Kiser

Theses and Dissertations

Interests in clean energy revived the nuclear power industry. For the first time in decades, innovative technologies and plant designs are being considered by regulatory agencies. This dissertation explores a Bayesian Network and AHP approach to causal modeling of the Combined License review process for new nuclear power plants (NPP). Historically lengthy and expensive, NPP licensing is critical to ensuring safe operation of the plants. With this comes a high standard for applicants to reach that can result in multiple revision cycles and long review times. New plant designs and fluctuating public support lead to a complex and dynamic series …

Modeling The Impact Of Scheduling Risks On Multi-Team Agile Projects, Bria Marie Booth

Theses and Dissertations

Agile project management allows for a quick response to a changing project environment. This opens possible avenues for new opportunities, but also may expose ongoing projects to previously unknown or unexpected threats. Risks must be continuously monitored as a product is worked on to avoid lost potential. This paper will propose a discrete event simulation model that multi-team projects may use to predict the impact to the project’s schedule. Using discrete event simulation early in the project’s planning cycle offers a greater understanding of the possible or probable impact of risks on the schedule. This would help to prepare project …