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This paper summarizes our work on experimental characterization and analysis of reduced-voltage operation in modern DRAM chips, which was published in SIGMETRICS 2017, and examines the work's significance and future potential. We take a…
Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accuracy and training efficiency. This paper delves into the…
The mechanism for phase distortion in linear beam vacuum devices is identified in the simplest system that pertains to such devices, the force-free drifting electron beam. We show that the dominant cause of phase distortion in a force-free…
Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system…
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time…
Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading additional computation for improved performance without…
The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased…
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often…
Part II of this paper elaborates on the unique capability of the proposed power flow analysis framework to obtain the true solution corresponding to the stable operating point of a network. It explains the significance of obtaining the true…
Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped to a dedicated hardware unit, making them well-suited for…
Modern safety-critical energy infrastructures are increasingly operated in a hierarchical and modular control framework which allows for limited data exchange between the modules. In this context, it is important for each module to…
Battery energy storage boosts up the response speed of power system frequency regulation, but must be recharged carefully to minimize the distortion to the frequency regulation response. This paper proposes a nonlinear feedback controller…
With the fast-increasing penetration of inverter-based resources (IBRs), the voltage support capability of the grid following (GFL) IBRs under low voltage ride through (LVRT) control significantly influences the transient voltage stability…
This paper proposes an advanced control strategy to eliminate both current sharing error and DC circulating current caused by line impedance mismatched and measurement errors in islanded AC microgrid system. The proposed adaptive virtual…
AI workloads are turning large data centers into highly dynamic power-electronic loads; fault-time behavior and workload pulsing can stress weak-grid points of interconnection. This paper proposes a centralized medium-voltage (MV)…
Inverter-based resources (IBRs) are a key component in the ongoing modernization of power systems, with grid-forming (GFM) inverters playing a central role. Effective fault current limiting is a major challenge to modernizing power systems…
Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of…
This paper proposes a distributed diagnosis scheme to detect and estimate actuator and power line faults in DC microgrids (e.g., electric-vehicle charging microgrids) subject to unknown power loads and stochastic noise. To address actuator…
This paper investigates the linear output regulation problem with both the exosystem and the plant fully unknown. A data-driven regulator is proposed to achieve asymptotic regulation and closed-loop stability without performing model…
Turbulent flow over permeable interface is omnipresent featuring complex flow topology. In this work, a data driven, end to end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have…