Congratulations to our labmates and collaborators Keerthiraj Nagaraj, Nader Aljohani, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair! Their paper, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors… Read More
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STATE ESTIMATOR AND MACHINE LEARNING ANALYSIS OF RESIDUAL DIFFERENCES TO DETECT AND IDENTIFY FDI AND PARAMETER ERRORS IN SMART GRIDS
Abstract: In the modern Smart Grid (SG), cyber-security is an increasingly important topic of research. An attacker can mislead the State Estimation (SE) process through a False Data Injection (FDI) on real-time measurement values or they can attack the parameters… Read More
CROSS-LAYERED DISTRIBUTED DATA-DRIVEN FRAMEWORK FOR ENHANCED SMART GRID CYBER-PHYSICAL SECURITY
Abstract: This paper presents a cross-layer strategy for detecting a variety of potential cyber-attacks on the Smart Grid. While most literature focus on False Data Injection attacks on the measurements taken throughout the Smart Grid, there are many ways in… Read More
A NETWORK PARAMETER DATABASE FDI CORRECTION PHYSICS-BASED MODEL: A MACHINE LEARNING SYNTHETIC MEASUREMENT BASED APPROACH
Abstract: Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of… Read More
SUPER RESOLUTION FOR ROOT IMAGING PUBLISHED IN APPS!
Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak! Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested… Read More
NVIDIA PARTNERS WITH UF IN AI INITIATIVE!
Today the University of Florida announced a $70 million artificial intelligence partnership with NVIDIA! The initiative will create an AI-centric data center at UF, housing the fastest AI supercomputer in higher education. Along with this technology, UF will collaborate with… Read More
NULL SPACE ANALYSIS OF NEURAL NETWORKS PRESENTED AT ICML
Congratulations to our labmates, Matt Cook, Alina Zare and Paul Gader for presenting at the 37th International Conference on Machine Learning (ICML) Workshop on Uncertainty and Robustness in Machine Learning! Their paper, titled “Outlier Detection through Null Space Analysis of Neural… Read More
TREE CROWNS DATASET NOW AVAILABLE!
We are happy to announce the publication of a new dataset! The NEON Tree Crowns Dataset is a collection of individual tree crown estimates for 100 million trees from 37 geographic sites across the United States. This dataset provides predicted… Read More
NEON TREE CROWNS DATASET
Abstract: The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction… Read More
OUTLIER DETECTION THROUGH NULL SPACE ANALYSIS OF NEURAL NETWORKS
Abstract: Many machine learning classification systems lack competency awareness. Specifically, many systems lack the ability to identify when outliers (e.g., samples that are distinct from and not represented in the training data distribution) are being presented to the system. The… Read More