4 citations · 8 across the 5 of their papers we have counts for
6 papers
Improving Slow Transfer Predictions: Generative Methods Compared
Jacob Taegon Kim, Alex Sim, Kesheng Wu +1
Monitoring data transfer performance is a crucial task in scientific computing networks. By predicting performance early in the communication phase, potentially sluggish transfers…
Multi-Resolution Model Fusion for Accelerating the Convolutional Neural Network Training
Kewei Wang, Claire Songhyun Lee, Sunwoo Lee +8
Neural networks are rapidly gaining popularity in scientific research, but training the models is often very time-consuming. Particularly when the training data samples are large h…
Feature Engineering and Classification Models for Partial Discharge in Power Transformers
Jonathan Wang, Kesheng Wu, Alex Sim +1
To ensure reliability, power transformers are monitored for partial discharge (PD) events, which are symptoms of transformer failure. Since failures can have catastrophic cascading…
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow
Jeeyung Kim, Mengtian Jin, Youkow Homma +3
In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dyna…
Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches
Julian Bellavita, Alex Sim, Kesheng Wu +4
The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cac…
Access Trends of In-network Cache for Scientific Data
Ruize Han, Alex Sim, Kesheng Wu +6
Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user comm…