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Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from

Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.

ContributorsGreenhagen, Tanner Patrick (Author) / Yang, Yezhou (Thesis director) / Jammula, Varun C (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Covid-19 is unlike any coronavirus we have seen before, characterized mostly by the ease with which it spreads. This analysis utilizes an SEIR model built to accommodate various populations to understand how different testing and infection rates may affect hospitalization and death. This analysis finds that infection rates have a

Covid-19 is unlike any coronavirus we have seen before, characterized mostly by the ease with which it spreads. This analysis utilizes an SEIR model built to accommodate various populations to understand how different testing and infection rates may affect hospitalization and death. This analysis finds that infection rates have a significant impact on Covid-19 impact regardless of the population whereas the impact that testing rates have in this simulation is not as pronounced. Thus, policy-makers should focus on decreasing infection rates through targeted lockdowns and vaccine rollout to contain the virus, and decrease its spread.

Created2021-05
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Research indicates that over 7.7% of adults who seek medical care every year at a hospital report a delay in receiving care, having difficulty receiving care, or being unable to receive care due to long waiting times (Kennedy et al. 2004). This continue to stir the need for researchers to

Research indicates that over 7.7% of adults who seek medical care every year at a hospital report a delay in receiving care, having difficulty receiving care, or being unable to receive care due to long waiting times (Kennedy et al. 2004). This continue to stir the need for researchers to explore ways to extend healthcare services in minimal waiting times. This thesis research utilizes Arena, a discrete event simulation software, to analyze waiting times in a typical hospital setting. It goes on to explore the impact of cross training of hospital personnel in meeting the critical needs of patients while minimizing waiting times. Simulation output data were analyzed, and cross training was found to have significant impact on reducing waiting time when: intake of patients is higher than current (original) arrival rate, intake of appointment patients is highest, or intake of emergency patience is highest of the three patient categories.
ContributorsBusisi, Jeanbat (Author) / Theodore, Pavlic (Thesis director) / Feng, Ju (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has grown larger during the past three years, they have begun to struggle with the on-time delivery of their orders. Woodland

Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has grown larger during the past three years, they have begun to struggle with the on-time delivery of their orders. Woodland prides itself on their high-grade process that includes core processing, the molding process, cleaning process, and heat-treat process. To create each mold, it has to flow through each part of the system flawlessly. Throughout this process, significant bottlenecks occur that limit the number of molds leaving the system. To combat this issue, this project uses a simulation of the foundry to test how best to schedule their work to optimize the use of their resources. Simulation can be an effective tool when testing for improvements in systems where making changes to the physical system is too expensive. ARENA is a simulation tool that allows for manipulation of resources and process while also allowing both random and selected schedules to be run through the foundry’s production process. By using an ARENA simulation to test different scheduling techniques, the risk of missing production runs is minimized during the experimental period so that many different options can be tested to see how they will affect the production line. In this project, several feasible scheduling techniques are compared in simulation to determine which schedules allow for the highest number of molds to be completed.
ContributorsAdams, Danielle Renee (Author) / Pavlic, Theodore (Thesis director) / Montgomery, Douglas (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05