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Filtering by: Creator DeCalesta, David S. Remove constraint Creator: DeCalesta, David S. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Pedestrian distraction : pedestrian behaviors at midblock crossings considering geometric and environmental conditions

    Creator:
    Kautz, Julia Anne
    Abstract:
    Pedestrian distraction at roadway crossings has been correlated with a higher risk of pedestrian-vehicle collisions due to the pedestrian's cognitive, visual, and motor attention being drawn to a wide variety of secondary tasks. This study is different from previous field studies of pedestrian midblock crossings in that the geometric layout...
    Resource Type:
    Masters Thesis
    Full Text:
    : ______________________________________________________ David S. Hurwitz Pedestrian distraction at roadway crossings has been correlated with a

  • Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks

    Creator:
    Deng, Dezhong
    Abstract:
    Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: David A. Hendrix Our goal is to build a system to model the RNA sequences that reveals

  • 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:
    AN ABSTRACT OF THE DISSERTATION OF Matthew S

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    Human and Biological Languages Abstract approved: David Hendrix and Liang Huang We explore the

  • 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

  • A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

    Creator:
    Stewart, Ryan H.
    Abstract:
    We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: Todd S. Palmer We present a novel multi-objective optimization methodology built upon

  • 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:
    Prof. Karl Haapala, Prof. David Kim, Rothanak Chan, Harsha Malshe, Steven Lindberg, Ryan Allen, and

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    learn- ing algorithm. In our approach, we learn the models P (s′|s, a) and R(s′|s, a), and then use

  • Long Wave Amplification in a Coral-Reef Lagoon

    Creator:
    Bheeroo, Vivek Anand
    Abstract:
    A series of laboratory experiments were conducted to study the wave field in the inner lagoon excited by ‘long’ incident waves. Three cases were considered: Cases A, B and C presenting incident waves of wavelength with factors of 1, 2 and 2.5 times the width of the reef respectively. The...
    Resource Type:
    Masters Thesis
    Full Text:
    committee members, Dr. David Hill, Dr. Greg Wilson and Dr. Prasad Tadepalli. Thank you for your time

  • A Distributed Scheme for Accelerating and Scaling PSPNet

    Creator:
    Krishna, Ajay
    Abstract:
    Advances in deep learning based image processing have led to their adoption for a wide range of applications, and in tow with these developments is a dramatic increase in the availability of high quality datasets. With this comes the need to accelerate and scale deep learning applications in order to...
    Resource Type:
    Masters Thesis
    Full Text:
    thesis committee, Dr. Prasad Tadepalli and Dr. David Roundy, for their insightful comments and timely