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Filtering by: Creator Luck, Matthew A Remove constraint Creator: Luck, Matthew A Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Conceptual Representations within the Social and Material Contexts of an Engineering Workplace and Academic Environments

    Creator:
    Barner, Matthew S.
    Abstract:
    Situated cognition theory emphasizes the role that social and material contexts have on learning and knowledge application. Several studies of engineering workplace environments have noted differences between the social and material contexts of the workplace and those of undergraduate engineering education. No existing research has studied the social and material...
    Resource Type:
    Dissertation
    Full Text:
    . Matthew S. Barner, Author ACKNOWLEDGEMENTS To my advisor, Shane A. Brown, I am forever

  • Automated decomposition of complex parts for manufacturing with advanced joining processes

    Creator:
    Massoni, Brandon R.
    Abstract:
    Recent advancements in joining operations and additive manufacturing now allow complex metal parts to be built up from raw materials, as opposed to being machined down from solid blocks. This not only opens up the design space, but also allows for much more efficient manufacturing. By decomposing a complex part...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: ______________________________________________________ Matthew I. Campbell

  • A collaborative filtering algorithm and evaluation metric that accurately model the user experience

    Creator:
    McLaughlin, Matthew
    Abstract:
    "Collaborative filtering algorithms’ performances have been evaluated using a variety of metrics. These metrics, such as Mean Absolute Error and Precision, have often ignored recommendations for which they do not have data. Ignoring these recommendations has provided numbers which do not accurately represent the user experience. Qualitatively we have seen...
    Resource Type:
    Capstone Project
    Full Text:
    …………………...…………………………………... 5 Eq. 7 …………………...…………………………………... 12 1 A Collaborative Filtering Algorithm and Evaluation

  • A Study of Temporal Convolutional Neural Networks for Music Language Modeling as Applied to Automatic Music Transcription

    Creator:
    Meyn, Matthew
    Abstract:
    Automatic music transcription (AMT) is the task, given an acoustic representation of music, to recover a symbolic notation of the written notes expressed by the sound. Transcribing music with multiple notes sounding simultaneously is difficult for both humans and machines. Much existing work on AMT has focused on suitable acoustic...
    Resource Type:
    Capstone Project
    Full Text:
    Modeling as Applied to Automatic Music Transcription by Matthew Meyn A PROJECT REPORT submitted to

  • Scale effects on reinforced concrete beams strengthened for shear with discrete externally bonded carbon fiber-reinforced polymer U-wraps

    Creator:
    Dawson, Matthew Ryan
    Abstract:
    Current ACI design provisions for shear in reinforced concrete (RC) beams strengthened with externally bonded carbon fiber-reinforced polymer (CFRP) U-wraps may not adequately account for the effect of scale. This paper describes tests of 6 geometrically scaled beams to identify possible scale effects of such beams. Results indicated that effective...
    Resource Type:
    Masters Thesis
    Full Text:
    Carbon Fiber-Reinforced Polymer U-Wraps by Matthew Ryan Dawson A THESIS submitted to

  • Monte Carlo Tree Search with Fixed and Adaptive Abstractions

    Creator:
    Hostetler, Jesse A.
    Abstract:
    Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Jesse A. Hostetler for

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
    Resource Type:
    Dissertation
    Full Text:
    Machine Learning: Applications in Biology and Genomics Abstract approved: David A. Hendrix Machine

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
    Full Text:
    : Analytical Methods for Improving Causal Inference Abstract approved: Stephen A. Ramsey Causal inference is

  • Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

    Creator:
    Marcon dos Santos, Gilberto
    Abstract:
    This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
    Resource Type:
    Dissertation
    Full Text:
    : Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty Abstract approved: Julie A

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    Constraints for Sequential Decision Making Abstract approved: Alan P. Fern Writing a program that performs