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
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
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
Abstract: Concerning smart grids real-time monitoring 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 the… Read More
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
Abstract: Machine learning approaches are affecting all aspects of modern society, from autocorrect applications on cell phones to self‐driving cars to facial recognition, personalized medicine, and precision agriculture. Although machine learning has a long history, drastic improvements in these application… Read More
Don’t miss the Drought Symposium at Colorado State University on June 21/22! Speakers include Detlef Weigel, Malia Gehan, Duke Pauli, Alina Zare, Brook Moyers, William Beavis, Chris Topp, and Mike Olsen. Alina Zare will discuss machine learning methods for phenotyping.