Browsing RPI Theses Open Access by Author "Lai, Rongjie"
Now showing items 1-6 of 6
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Data quality monitoring and improvement of synchronized phasor measurements
Hao, Yingshuai (Rensselaer Polytechnic Institute, Troy, NY, 2018-12)Phasor measurement units (PMU) provide time-synchronized high-resolution measurements at rates from 30 up to 120 samples per second. Since the provided synchrophasor measurements facilitate the wide-area monitoring, ... -
Deep neural networks for mri applications
Lyu, Qing (Rensselaer Polytechnic Institute, Troy, NY, 2022-05)Magnetic resonance imaging (MRI) has been widely used for clinical disease diagnosis and neuroscience research since its invention. Compared with other commonly used medical imaging modalities like computed tomography and ... -
Low-order methods for nonconvex functional constrained optimization
Li, Zichong (Rensselaer Polytechnic Institute, Troy, NY, 2022-07)Recently, many real-world problems in engineering and data science not only have very large scales and complicated functional constraints, but also go beyond the scope of convex optimization and inevitably include nonconvex ... -
Nonconvex regularizers for sparse optimization and rank minimization
Sagan, April (Rensselaer Polytechnic Institute, Troy, NY, 2021-05)This dissertation addresses the problem of minimizing a nonconvex relaxation to the rank of a matrix. In the first of three works presented in this dissertation, we present the problem of rank minimization as a semidefinite ... -
System-wide advances in photon-counting CT : corrections, simulations, and image analysis
Getzin, Matthew (Rensselaer Polytechnic Institute, Troy, NY, 2019-12)Imaging plays a major role in biomedicine with X-ray computed tomography (CT) as a primary example. X-ray CT becomes increasingly popular since its invention in the 1970’s, now reaching over 100 million scans yearly ... -
The geometry of learning and the learning of geometry
Tatro, Norman Joseph (Rensselaer Polytechnic Institute, Troy, NY, 2021-05)Overall, this work concerns the intersection of machine learning and differential geometry. It investigates ways in which these fields can mutually inform each other. Our goal is to promote bringing a geometric paradigm ...