activity
20162021
most citedHybrid data-driven physics model-based framework for enhance cyber-physical smart grid security

37 citations · 44 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security

Allen Starke, Keerthiraj Nagaraj, Cody Ruben +5

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environm…

eess.SY2019★ 37 cited

Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security

Cody Ruben, Surya Dhulipala, Keerthiraj Nagaraj +5

This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, i…

cs.CV2019★ 1 cited

Peanut Maturity Classification using Hyperspectral Imagery

Sheng Zou, Yu-Chien Tseng, Alina Zare +3

Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight (yield), and critically influences seed vigor and other…

cs.CV2017★ 2 cited

Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation

Sheng Zou, Hao Sun, Alina Zare

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability…

cs.CV2016★ 1 cited

Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation

Sheng Zou, Alina Zare

The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a h…

cs.CV2016★ 3 cited

Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances

Sheng Zou, Alina Zare

The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed…