Index Catalog // ScholarsArchive@OSU

Creator:: Harris, Jesse
Abstract:: Ocean Wave Energy Converters (WECs) are of interest around the globe as global economies begin to shift their interest to renewable forms of energy. However, the devices are costly to construct–a quality that can be relieved through proper modeling, control, and implementation. This paper presents the numerical and hybrid simulation...
Resource Type:: Masters Thesis
Full Text:: y W E C W av es pr es su re & ai r de ns ity A ir C ha m be r C on tr ol le r R ot

Creator:: Gaebele, Daniel T.
Abstract:: Converting energy from ocean waves is a challenging area for control theory application because of the nonlinear dynamics in various time scales. Generally, wave energy converter (WEC) control is applied in order to maximize power absorption, in the most common wave conditions, and subject to the devices’ physical constraints. Commonly,...
Resource Type:: Dissertation
Full Text:: a circular wave due to its motion after absorbing energy from the incident wave, (c): Next point

Creator:: Senaratne, Galpottage Dilan S.
Abstract:: Accurate information of power network parameters is essential for performing various power system monitoring and control tasks including state estimation, economic dispatch, and contingency analysis. In this paper, we present a novel approach of power network parameter correction wherein we exploit the sparse nature of parameter errors. Parameter error correction...
Resource Type:: Masters Thesis
Full Text:: comparative evaluation is demonstrated using the IEEE 14-bus and 57-bus test cases. c©Copyright by

Creator:: Kibria, Sharmin
Abstract:: We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. The identifiability-aware approach is proposed to leverage common characteristics of fundamentally identifiable gross errors to enhance error correction performance. First, we derive a necessary rank condition that the sparsity pattern of any...
Resource Type:: Dissertation

Creator:: Alsulaimawi, Zahir Ahmed Hussein
Abstract:: Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are circumventing them. Further, there have been concerns about the possibility of bias and discrimination in predictive applications. In order to address these issues, we propose an...
Resource Type:: Dissertation
Full Text:: input into two parts: the traditional noise vector z and a new “latent code” vector c. The code vector

ScholarsArchive@OSU