Matching Items (7)
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A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to

A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive or negative, ELM can classify knots that lead to large changes to beam deflection, while not over-emphasizing knots that may not be a problem. Using ELM with a regression-fit Young's Modulus on three-point bending of Douglass Fir, it is possible estimate the effects a knot will have on the shape of the resulting displacement curve.
Created2015-05
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Prior research has confirmed that supervised learning is an effective alternative to computationally costly numerical analysis. Motivated by NASA's use of abort scenario matrices to aid in mission operations and planning, this paper applies supervised learning to trajectory optimization in an effort to assess the accuracy of a less time-consuming

Prior research has confirmed that supervised learning is an effective alternative to computationally costly numerical analysis. Motivated by NASA's use of abort scenario matrices to aid in mission operations and planning, this paper applies supervised learning to trajectory optimization in an effort to assess the accuracy of a less time-consuming method of producing the magnitude of delta-v vectors required to abort from various points along a Near Rectilinear Halo Orbit. Although the utility of the study is limited, the accuracy of the delta-v predictions made by a Gaussian regression model is fairly accurate after a relatively swift computation time, paving the way for more concentrated studies of this nature in the future.
ContributorsSmallwood, Sarah Lynn (Author) / Peet, Matthew (Thesis director) / Liu, Huan (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of Earth and Space Exploration (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

ContributorsMerry, Tanner (Author) / Ren, Yi (Thesis director) / Zhang, Wenlong (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

ContributorsDaly, John H (Author) / Ren, Yi (Thesis director) / Zhuang, Houlong (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

In the last two decades, fantasy sports have grown massively in popularity. Fantasy football in particular is the most popular fantasy sport in the United States. People spend hours upon hours every year building, researching, and perfecting their teams to compete with others for money or bragging rights. One problem,

In the last two decades, fantasy sports have grown massively in popularity. Fantasy football in particular is the most popular fantasy sport in the United States. People spend hours upon hours every year building, researching, and perfecting their teams to compete with others for money or bragging rights. One problem, however, is that National Football League (NFL) players are human and will not perform the same as they did last week or last season. Because of this, there is a need to create a machine learning model to help predict when players will have a tough game or when they can perform above average. This report discusses the history and science of fantasy football, gathering large amounts of player data, manipulating the information to create more insightful data points, creating a machine learning model, and how to use this tool in a real-world situation. The initial model created significantly accurate predictions for quarterbacks and running backs but not receivers and tight ends. Improvements significantly increased the accuracy by reducing the mean average error to below one for all positions, resulting in a successful model for all four positions.

ContributorsCase, Spencer (Author) / Johnson, Jarod (Co-author) / Kostelich, Eric (Thesis director) / Zhuang, Houlong (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-05
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Popular competitive fighting games such as Super Smash Brothers and Street Fighter have some of the steepest learning curves in the gaming industry. These incredibly technical games require the full attention of the player and often take years to master completely. This barrier of entry prevents newer players from enjoying

Popular competitive fighting games such as Super Smash Brothers and Street Fighter have some of the steepest learning curves in the gaming industry. These incredibly technical games require the full attention of the player and often take years to master completely. This barrier of entry prevents newer players from enjoying the competitive social environment that such games offer, creating a rift between casual and competitive players. Learning the rules can sometimes be more difficult than playing the game itself. To truly master these concepts requires personal attention from someone who deeply understands the core mechanics that operate behind the scenes.
Meanwhile, machine learning is growing more advanced by the day. Online retailers like Amazon run complex algorithms to recommend future purchases and monitor price changes. Mobile phones use neural networks to interpret speech. GPS apps track anonymous motion data in smartphones to give real-time traffic estimates. Artificial intelligence is becoming increasingly ubiquitous because of its versatility in analyzing and solving human problems; it follows, then, that a machine could learn how to teach humans skills and techniques. HelperBot is a platform fighting game project that employs this cutting-edge learning technology to close the skill gap between novice and veteran gamers as quickly and seamlessly as possible.
ContributorsPalermo, Seth Daniel (Author) / Olson, Loren (Thesis director) / Marinelli, Donald (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One emerging solution is self-driving vehicles, which have been coined as a safer driving method by reducing fatalities due to driving

As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One emerging solution is self-driving vehicles, which have been coined as a safer driving method by reducing fatalities due to driving accidents. While completely automated vehicles are still in the testing and development phase, the United Nations predict their full debut by 2030 [1]. While many resources are focusing their time on creating the technology to execute decisions such as the controls, communications, and sensing, engineers often leave ethics as an afterthought. The truth is autonomous vehicles are imperfect systems that will still experience possible crash scenarios even if all systems are working perfectly. Because of this, ethical machine learning must be considered and implemented to avoid an ethical catastrophe which could delay or completely halt future autonomous vehicle development. This paper presents an experiment for determining a more complete view of human morality and how this translates into ideal driving behaviors.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.
Created2019-05