Congratulations to our labmates and collaborators: Joshua Peeples, Sarah Walker, Connor McCurley, Alina Zare, James Keller and Weihuang Xu! Their paper, “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification”, was recently accepted to IEEE Geoscience and Remote Sensing… Read More
Tag: machine learning
WALKER PRESENTS AT UF 2021 UNDERGRADUATE RESEARCH SYMPOSIUM!
Congratulations to our labmate, Sarah Walker! Sarah presented her work, titled “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification” at UF’s 2021 Undergraduate Research Virtual Symposium. The virtual symposium featured outstanding undergraduate researchers across all colleges at UF.… Read More
DIVERGENCE REGULATED ENCODER NETWORK FOR JOINT DIMENSIONALITY REDUCTION AND CLASSIFICATION
Abstract: In this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network. Motivated by a popular dimensionality reduction approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), our proposed method incorporates a classification loss computed on samples… Read More
DIVISIVE CLUSTERING ACCEPTED TO MLDM!
Congratulations to our labmates and collaborators: Diandra Prioleau, Kiana Alikhademi, Armisha Roberts, Joshua Peeples, Alina Zare and Juan Gilbert! Their paper, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data” was recently accepted to the the 2021 International Conference… Read More
APPLICATION OF DIVISIVE CLUSTERING FOR REDUCING BIAS IN IMBALANCED DATA
Abstract: A lack of diversity and representativeness within training data causes bias in the machine learning pipeline by influencing the performance of many machine learning models to favor the majority of samples that are most similar. It is necessary to… Read More
STATE ESTIMATOR ACCEPTED TO NAPS 2020!
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
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
PLANTS MEET MACHINES PUBLISHED IN APPLICATIONS IN PLANT SCIENCES!
Congratulations to our labmates and collaborators Pamela S. Soltis, Gil Nelson, Alina Zare and Emily K. Meineke! They wrote the introduction for a special issue of Applicaitons in Plant Sciences. Their introduction is titled “Plants meet machines: Prospects in… Read More