Baseline Data from Servo Motors in a Robotic Arm for Autonomous Machine Fault Diagnosis

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: low lubrication, no lubrication, two gears chipped, and four gears chipped. Each fault was also examined at 180, 135, 90, and 45-degree swings of the servo arm. Component level data was obtained using an Arduino microcontroller and a feedback wire in each servomotor to obtain the actual position of the servo arm, which allowed for the calculation of the difference in actual and theoretical position and the speed of the servo arm at the various faults. System level data was obtained using OptiTrack’s motion tracking software, Motive, to track the position of two reflective markers on the hand of the robotic arm. At the component level, the low lubrication and no lubrication faults did not exhibit a large difference from the normal servomotor, whereas the servomotors with the gears chipped exhibited significant differences when compared to the normal servomotor. When evaluating the difference in position and speed of the servo arm at larger degree sweeps it was more evident that failure occurred, as opposed to the data at smaller degree sweeps. At the system level, the error was not as visible in the data as there wasn’t much distinction between the speeds of the robotic arm’s hand when the servomotors with faults were placed in it. The results of this work indicate that servomotors can be used to create fault behavior datasets at the component and system level that are usable for machine learning

Land Grant Application- Brown, Jacob (Hiram)

Land grant application submitted to the Maine Land Office on behalf of Jacob Brown for service in the Revolutionary War, by their widow Rhoda.https://digitalmaine.com/revolutionary_war_me_land_office/1135/thumbnail.jp

The Impact of Age on Quantitative Infrared Pupillometry in Healthy Children 1-18 Years

OBJECTIVE: The goal of this study was to establish normative values under ambient light conditions for measurements of quantitative pupillometry in children. METHODS: This was a cross-sectional analysis of pupillometry values obtained in children. Quantitative pupillomentry measurements were obtained from children between 1 and 18 years of age being seen for either a well child check or other outpatient appointment. Participants were excluded if they were in pain, on any medication known to affect pupil size (i.e. opioids, stimulants), or had any chronic neurologic conditions. RESULTS: A total of 242 children were enrolled in this study, with pupillometry readings obtained from a total of 171 children after exclusions. Maximum and minimum pupil size increased slightly with age; however, the correlation was weak (r = 0.14 and r = 0.16). Similarly weak correlations with age also were observed for maximum constriction velocity (r = -0.12) and dilation velocity (r = 0.05). No differences were observed between males and females for any of the pupil parameters. Maximum (5.35 mm vs. 4.91 mm) and minimum (3.71 mm vs. 3.36 mm) pupil sizes were significantly larger in Whites than African-American participants. CONCLUSIONS: Pupil size and reactivity show little correlation with age, appear to be relatively independent of ontogeny, and therefore would not be expected to significantly impact further exploration in utilizing pupillometry as a biomarker across the pediatric age range. Differences in race should be taken into consideration when pupillometry is used in mixed populations

Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization

These results will show that the use of Linear General Line Coordinates (GLC-L) can visualize multidimensional data better than typical methods, such as Parallel Coordinates (PC). The results of using GLC-L will display visuals with less clutter than PC and be easier to see changes from one graph to the next. Visualizing the Pareto Frontier with GLC-L allows n-D data to be viewed at once, compared to typical methods that are limited to 2 or 3 objectives at a time. This method details the process of selecting a ”best” case, from a group of equals in the Pareto Subset and comparing it against an optimal solution. Selecting a ”best” case from a Pareto Subset is difficult, because every individual is better in some ways to its peers. The ”best” case is the solution to the specific task for each dataset

Digital Demonstrations: Examinations of Protests and Politics in Cory Doctorow’s Young Adult Fiction

Within this paper, I intend to examine the manner by which author and blogger Cory Doctorow utilizes complex themes of digital labor exploitation and intellectual property law within his young adult fiction in order to bring about positive social change, with particular attention paid to the 2008 novel Little Brother and its 2013 sequel Homeland, the 2010 novel For the Win, the 2012 novel Pirate Cinema, and 2014’s In Real Life, a graphic novel written by Doctorow and illustrated by Jen Wang. Throughout Doctorow’s realistic depictions of slightly-fictionalized versions of contemporary life and embellishments of near-future settings, readers are drawn into examinations of the realities of major issues, including gold farming, digital labor unionization, governmental monitoring of citizen activities, and creative leveraging of copyright law by multinational corporations. Although Doctorow himself has been a vocal proponent of these sorts of issues for some time, his utilization of these themes within works aimed at a young adult audience often suggests more concrete personal solutions to these issues than his own advocacy. By incorporating practicable information into his writings aimed at this audience, Doctorow has created a sort of manual for digital activism and piracy between the lines of a number of engrossing narratives. Throughout these works, representation of real-life organizations like Britain’s anti-kettling group Sukey occurs, as do descriptions of strategies and materials important for a protestor to avoid arrest or abuse at the hands of law enforcement. At the same time, moral quandaries about digital information and labor rights are presented to readers to contemplate, leading to a better-informed populace of young digital consumers that will be prepared to both articulate their opinions and defend their rights in uncertain times

Essays in Experimental Economics and Applied Econometrics

This dissertation presents one essay that utilizes an experiment and two essays that use observational data to causally examine important economic questions. In the first essay, I describe a laboratory experiment with human participants designed to test key theoretical predictions related to the effects of using public and secret reserve prices in independent private value, first-price sealed bid auctions. The results support theoretical predictions related to bidding in public reserve auctions and predicted revenue differences between public and secret reserve auctions. Predictions regarding buyer bidding in secret reserve auctions, and that public and secret reserves generate higher revenue than no reserve auctions, is not supported by the data. The second essay investigates the relationship between air quality and human endurance and strength. I utilize a database of physical fitness scores from members of the United States Air Force and merge this with particulate matter measures from the Environmental Protection Agency. I exploit daily variation in particulate matter and the random assignment of members to work locations to identify a causal effect of particulate matter on member run times, push-ups, and crunches. I find that particulate matter exposure results in significant decreases in push-up and crunch repetitions and increases run times. Furthermore, I find that the physical fitness test failure rate increases with exposure to high particulate matter concentrations. The third essay, a joint project with Celeste K. Carruthers (University of Tennessee, Knoxville), Michael Koefed and Aaron Phipps (United States Military Academy), and Jenna Webb Kramer (RAND Corporation), estimates the causal effects of providing free community college on military enlistments and aptitude. To identify these relationships we employ a two-way fixed effects model and exploit the staggered rollout of the Tennessee Promise scholarship program, which is a last-dollar aid program that became available to graduating high school seniors in a subset of Tennessee counties in 2009 and by 2015 was implemented statewide. Our quantitative analysis complements the findings of Dr. Kramer’s qualitative analysis and shows that Tennessee promise led to a significant decrease in military enlistments. We also find that Tennessee Promise motivated more technically adapt persons to enlist