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Filtering by: Creator Palo, Daniel R. Remove constraint Creator: Palo, Daniel R. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science File Format pdf Remove constraint File Format: pdf

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  • Resource planning power flow & feasibility study

    Creator:
    von Jouanne, Annette and Wallace, Alan
    Abstract:
    The OSU Motor Systems Resource Facility (MSRF), co-directed by the Principal Investigators (PI's, bios included), is an Energy Systems Laboratory with operating capabilities up to 750kV A, testbeds up to 300hp, a 120kVA fully programmable source, and a bi-directional grid interface enabling regeneration back onto the grid. The MSRF was...
    Resource Type:
    Technical Report

  • Shape transformation using compactly supported radial basis functions

    Creator:
    Park, Byungsoo
    Abstract:
    Shape transformation is a technique for gradually changing one geometric shape to another. A recent approach presents the use of thin-plate radial basis functions as opposed to traditional "blobby sphere" implicit functions. Without the explicit evaluation of he energy function, this approach combined the two traditional steps into one by...
    Resource Type:
    Capstone Project
    Full Text:
    bas is functions or compactly-supported radial bas is functions r espect ive ly. In Chapter 6 I will

  • Adversarial Attacks in Natural Language Question-Answering

    Creator:
    Rahurkar, Prachi S.
    Abstract:
    Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
    Resource Type:
    Masters Thesis
    Full Text:
    , Jeffrey Heer, and Daniel Weld. Erru- dite: Scalable, reproducible, and testable error analysis. In

  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
    Resource Type:
    Dissertation