Category: Publication

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 which an attacker can affect power system real-time operation. Namely, an attacker can focus on […]

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 the system model is the other part of the equation, since almost all applications in […]

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 includes height, crown area, and spatial location. Links: Citation: B. Weinstein, S. Marconi, A. Zare, […]

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 ability to detect outliers is of practical significance since it can help the system behave […]

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 areas recently have been driven by improvements to computational infrastructure; increased computing power; increased ability […]

Abstract: Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing numbers of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad data detection methods may not detect these attacks. Recently, physics-model and datadriven methods have been […]

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was published in Computers and Electronics in Agriculture.   The document and code can be found here.  Make sure […]

Congratulations to our labmates Guohao Yu, Alina Zare and Hudanyun Sheng, as well as collaborators, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently accepted to Machine Vision and Applications! Their paper explores the use of multiple instance learning to segment minirhizotron […]

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, titled “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was recently accepted to Computers and Electronics in Agriculture.  Check it out here!

Abstract: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is […]