Granting Agency: U.S. Department of EnergyProject Title: Light-Matter Interactions in Artificial Spin LatticesAmount: $2.75M for first two years, renewable for an additional four years and $10 million.
Description: This Department of Energy EPSCoR Implementation program will enhance energy-related research capability in the EPSCoR jurisdictions of Kentucky and Delaware and establish partnerships with three additional university partners and four national laboratory partners. The Implementation program seeks to "jumpstart" research capability with increased human and technical resources by accelerating the careers of nine early- and mid- career faculty members through research support, enhanced instrumentation, and the mentorship of established researchers. The Implementation program will also create a pipeline of early-career (postdoctoral, graduate, and undergraduate) researchers in X-ray science, condensed matter physics, and unconventional computing. A detailed plan for Promoting Inclusive and Equitable Research will advance scientific excellence throughout the research effort. The scientific goal of the program is to understand how photons, spanning microwave to X-ray energies, interact with arrays of nanoscale magnets to enable novel approaches to low-power, high performance computing. These nanomagnet arrays, referred to as artificial spin lattices, are emerging as new platforms for unconventional computing including reservoir and neuromorphic systems. Understanding the interactions of artificial spin lattices with photons, especially those at X-ray energies and carrying orbital angular momentum (OAM), will allow us to both probe and program the states and dynamics of these systems.
SCHOLARS@UK
Granting Agency: National Science FoundationProject Title: Collaborative Research: Digital Twin Predictive Reliability Modeling of Solid-State TransformersAmount: $480,000 Description: Solid state transformer (SST) is deemed as a revolutionary technology for future power systems. It is much more compact than the conventional electromagnetic transformer, with significant controllability advantage both in power flow control and power quality regulation. However, one major technical barrier that constrains the practicality of SST is the low reliability compared to the conventional transformers, due to the large device count including semiconductor transistors, auxiliary circuits, passive components and internal connections. Currently, the reliability of SST has received little attention. To address the problem, our team at the University of Kentucky and Dr. Mohammad Agamy’s team at State University of New York at Albany will collaborate and develop a comprehensive systematic framework of online health monitoring for SSTs to significantly improve the reliability to electric faults. The proposed health monitoring framework will include online prognosis and diagnosis of potential electrical faults that could occur to SST, targeting common semiconductor switching faults and health degradation in the high- frequency transformers. Specifically, a portfolio of critical SST parameters will be monitored through a smart gate driver that will be integrated with the power electronic building blocks (PEBBs), so degradation in the semiconductor modules can be predicted and diagnosed during the fault incipient stage. A novel data driven digital twin approach is proposed to predict the behavior of the SST converter modules and it will compute specific health performance indices to make it more computationally effective compared to full physical model computations. Fast online diagnostic algorithm will be developed and embedded in the SST microcontroller, so a fault can be identified and characterized, to minimize the downtime cost and avoid cascaded failures.
Granting Agency: Office of Naval Research Project Title: Evolution of Metric-Based Assessment of Shipboard Power System PerformanceAmount: $398,746
Description: This project aims to transform the evaluation of shipboard power system performance through the advancement of metric-based assessment methods. By developing innovative approaches, this project seeks to provide a comprehensive and dynamic framework for assessing the performance of shipboard power systems in various mission contexts. The outcomes of this research will have significant implications. The enhanced assessment methods and optimized control strategies will enable naval vessels to operate more efficiently, sustainably, and effectively in a range of mission environments.Ultimately, the project aims to enhance the operability of shipboard power systems while reducing the cost of system operation, leading to improved mission effectiveness, reduced fuel consumption, and extended range and endurance of naval vessels. This project represents a significant step towards achieving advanced performance assessment and control strategies in shipboard power systems. It has the potential to drive innovation in naval operations and contribute to the overall advancement of maritime capabilities.
This project is a collaboration led by Florida State University and comprises Florida Agricultural and Mechanical University, University of New York at Buffalo, and Georgia Tech. The industrial partners include Raytheon, Boeing, and Advanced Magnet Lab. The UK team will also work closely on this project with the high-tech company QM Power, Inc. The multi-organizational academic and industrial team will consider how hybrid hydrogen-electric power generation could be combined with fuel cell technology to lower emissions.
Granting Agency: National Science Foundation Project Title: Core: Small: Hypergraph Signal Processing and Networks via t-Product DecompositionsAmount: $254,145
Description: This proposal presents a collaborative research effort aimed at developing a new hypergraph signal processing (HGSP) framework based on tensor representations, capable of exploiting multi-way interactions of data from complex systems. HGSP generalizes and subsumes the concepts and tools developed under the umbrella of graph signal processing (GSP) which only consider pairwise couplings between data and, thus, cannot capture high-dimensional interactions among multiple nodes in complex biological, social, and engineering networks. The proposed research radically departs from prior work that relies on symmetric canonical polyadic (CP) tensor decompositions. Instead, the theoretical underpinnings are based on the more recently introduced t-product multiplication operation in tensor algebra which allows tensor factorizations that are analogous to matrix factorizations such as the SVD and eigen decompositions. The advantages of adopting t-eigen decompositions are compelling — they preserve the intrinsic structure of tensors and the high-dimensional nature of signal representations; most importantly, the orthogonal eigen basis derived from this formulation allows for a loss-free Fourier decomposition and computationally efficient calculations.
Granting Agency: Tennessee Valley Authority Project Title: EVsmartPV2H - EV Integration Smart Power SystemsAmount: $18,000 Description: Tennessee Valley Authority (TVA) has awarded a new research project to UK PEIK Institute and SPARK Laboratory researchers to study virtual power plants for the distributed energy resources aggregation and demand response optimal controls. The project will evaluate the potential beneficial impact of coordinated operation of appliances, including HVAC systems and water heaters, solar PV, battery systems, and electric vehicles on the future electric power distribution systems for communities.
Granting Agency: Tennessee Valley Authority Project Title: Study Of Electric Vehicle Integration On A Distribution System And Potential Mitigation SolutionsAmount: $100,000 Description: Tennessee Valley Authority (TVA) has awarded a new research project to UK PEIK Institute and SPARK Laboratory researchers to study virtual power plants for the distributed energy resources aggregation and demand response optimal controls. The project will evaluate the potential beneficial impact of coordinated operation of appliances, including HVAC systems and water heaters, solar PV, battery systems, and electric vehicles on the future electric power distribution systems for communities.
Granting Agency: National Science Foundation Project Title: CAREER: Transforming Machine Learning Models Developed in Lab to Manufacturing Plants for In- Process Quality PredictionAmount: $567,930 Description: This CAREER project will boost the deployment of Industry 4.0 and ML techniques in manufacturing plants and accelerate the path to smart factories. Beyond the robotic welding plants to be covered in the project and the two major industrial collaborators, the developed generalizable ML architecture is easily expandable to a broad scope of manufacturing processes and will benefit the entire manufacturing sector. Especially, through working with the Kentucky Association of Manufacturers (KAM), the project outcomes will be disseminated to small-medium manufacturers in the Commonwealth of Kentucky, to help them realize digital transformation and grow their business competitiveness. Furthermore, novel theories developed in this work will impact the fundamental science community and have the potential to become widespread in the natural sciences, engineering, and healthcare applications, such as big data-supported health tracking systems. On the educational front, the research outcomes will be incorporated into curriculum development and high-school 360-hour research projects. Also, demo website and technical webinars will be held for manufacturers, to maximize the social awareness of the research outcomes.
Granting Agency: National Science Foundation Robust Intelligence Project Title: Optimal Transport Generative Adversarial Networks: Theory, Algorithms, and ApplicationsAmount: $590,000
Description: Generative Adversarial Network (GAN) models have proven to be exceptionally efficient and effective, particularly in generating high quality samples. However, there are some significant challenges in using GANs, with training difficulties being a notable one. The objective of this project is to advance theory and training algorithms for GANs and to demonstrate their effectiveness through two applications: one arising in a human-robot collaborative welding system and the other in imbalanced data sampled from skewed class distributions. By tackling these challenges and studying real-world applications, this project aims to contribute to the broader utilization of generative models across diverse domains.
Granting Agency: Institute of Neurological Disorders & StrokeProject Title: Federated Digital Pathology Platform for AD/ADRD Research and DiagnosticsAmount: $1,778,991 Description: In order to optimize and standardize procedures for digital neuropathology (DNP) studies in Alzheimer’s disease and related dementia (AD/ADRD), this project connects multiple research centers for integrated whole slide image (WSI) advanced analytics. Using thousands of WSIs from diverse subjects, and methods cross-validated with benchmark standards, the project will develop an open-source federated platform with integrated artificial intelligence/machine learning (AI/ML) capabilities for image segmentation, classification, and synthetic data generation that can be meaningfully expanded for research, diagnostic, and didactic applications.
Granting Agency: National Institute of Food and AgricultureProject Title: Improving the Spatial and Spectral Calibration of Remote Sensing Imagery from Unmanned Aircraft SystemsAmount: $612,765
Description: Remote sensing using drones is a common strategy for collecting site specific measurements in precision agriculture. This work seeks to improve the accuracy of drone-based imagery through automated spatial and spectral calibration processes. The expected outcomes are improved workflow when processing drone imagery into maps and 3D models, and better scalability when transitioning from research to production.
Granting Agency: National Science FoundationProject Title: NSF Engines Development Award: Advancing Carbon Centric Circular Economy Technologies for Advanced Manufacturing Solutions (KY, TN) - Generating Advanced Manufacturing Excellence for Change (GAME Change) for the Southeastern Commerce CorridorAmount: $1,000,000
Description: The coalition of research, education, economic development, industrial and manufacturing leaders of the Southeastern Commerce Corridor (SCC) of Kentucky and Tennessee aims to create a diverse innovation and talent development hub that secures U.S. competitiveness in Next-Generation Manufacturing (NGM) and supply chain logistics, supports closed-cycle manufacturing to reduce waste and increases efficiencies across sectors including automotive, aerospace, energy, food and beverage, and materials.
