2018 article

Work-In-Progress: Making Machine Learning Real-Time Predictable

2018 39TH IEEE REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2018), pp. 157–160.

By: H. Xu n & F. Mueller n

author keywords: Edge Computing; Real-time Predictability; Keras; Caffe
TL;DR: This work identifies the subset of ML problems appropriate for edge devices by investigating if they result in real-time predictable services for a set of widely used ML libraries, and enhances the Caffe library to make it more suitable for real- time predictability. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: March 18, 2019

2016 conference paper

A resilient software infrastructure for wide-area measurement systems

2016 ieee power and energy society general meeting (pesgm).

By: T. Qian*, H. Xu*, J. Zhang n, A. Chakrabortty n, F. Mueller* & Y. Xin*

TL;DR: This work designs and implements a software infrastructure to estimate power grid oscillation modes based on real-time data collected from Phasor Measurement Units (PMUs), and deploys a distributed algorithm on the basis of the Prony algorithm and the Alternating Directions Method of Multipliers (ADMM). (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.