280 papers
This paper proposes a deep learning-based framework that integrates historical outage and weather data to predict event-level power system resilience, validated through both simulated and real-world datasets, to guide targeted investments in distributed energy resources for vulnerable regions.
This paper presents an analytical MAC-layer model for 5G NR Sidelink Mode 2 that explicitly incorporates standardized Semi-Persistent Scheduling features to derive closed-form expressions for packet reception ratio, which are validated against ns-3 simulations to provide design insights for enhancing 6G sidelink reliability.
This survey reviews the application of utility theory to cognitive modeling in robotics, tracing its evolution from behavior-based approaches to value systems that guide decision-making, learning, and cooperation in single and multi-agent environments, while identifying current limitations and proposing future research directions.
This paper demonstrates that large language model-powered generative agents, enhanced by in-context learning, can effectively relax the rigidity of traditional mathematical models to replicate both rational strategies and systematic behavioral deviations in power dispatch and auction scenarios.
This paper addresses the scalability challenges of state-space analysis in large-scale renewable power systems by leveraging system symmetry to define distinct inner-group and group-grid eigenvalue patterns and introducing a novel group participation factor for stability analysis and targeted optimization.
This paper reviews and analyzes various coordination schemes between Transmission and Distribution System Operators (TSOs and DSOs) to effectively integrate Distributed Energy Resources, aiming to maintain system balance and prevent network congestion while overcoming technical and market challenges associated with the ongoing energy transition.
This paper proposes an adaptive control framework for Euler-Lagrange systems that guarantees user-defined time-varying state and input constraints under uncertainties and disturbances by integrating a time-varying barrier Lyapunov function with a saturated control law, supported by an offline feasibility condition and validated through real-time helicopter experiments.
This paper proposes a novel flow-based conceptual design for coupling European capacity markets that, by harnessing cross-border capacity while respecting network constraints, reduces system costs and improves investment efficiency compared to isolated national mechanisms.
This paper introduces an augmented Model Predictive Control method for agile earth observation satellites that effectively balances high-performance nonlinear control capabilities with the computational simplicity required for hardware implementation, validated through both numerical simulations and physical experiments.
This paper proposes a systematic framework using Robust Positive Invariant sets to compute formally guaranteed trajectory tracking error bounds for autonomous helicopters, comparing three controller architectures to balance tracking performance with the conservatism of the derived error bounds.
This paper proposes and analyzes three communication-efficient distributed algorithms that achieve finite-time quantized average consensus in open multi-agent systems with dynamic directed links, arbitrary bounded processing delays, and continuous node turnover, while establishing novel topological conditions for convergence and demonstrating superior performance through simulations.
This paper proposes a model-free Deep Reinforcement Learning control framework for power inverters that utilizes an error energy-guided hybrid reward and adaptive importance weighting to distill a heavy policy into a lightweight neural network, achieving microsecond-level inference, superior transient response, and robust performance on a hardware experimental platform.
This paper proposes a robust control synthesis method for uncertain linear systems with input saturation by reformulating the problem within an integral quadratic constraints (IQC) framework, which yields improved -gain performance and numerically tractable LMI-based conditions compared to conventional static sector approaches.
This paper presents the implementation of a novel, rigorous time-domain phase-noise analysis module in the open-source QUCS simulator, featuring new closed-form expressions for amplitude and phase-amplitude correlations that surpass existing empirical models and commercial EDA capabilities in predicting the performance of noise-perturbed autonomous circuits.
This paper proposes a hierarchical quantum annealing-based model predictive control framework for large-scale household energy scheduling with hydrogen storage, demonstrating its superior scalability and effectiveness in solving complex optimization problems as the number of connected households increases compared to traditional methods.
This paper presents a curved monopole antenna design optimized for HF skywave radar that, through parametric analysis of curvature and straight-section length, achieves significantly enhanced gain and bandwidth compared to conventional monopoles, and demonstrates further performance improvements when scaled into a 12-element linear array.
This paper proposes a temperature-aware, distributed optimization approach using the alternating direction method of multipliers to co-optimize energy, carbon, water, and latency costs for LLM inference across geo-distributed edge data centers in Australia, leveraging ambient temperature diversity to enhance sustainability and efficiency.
This paper proposes a framework for synthesizing tunable input-to-state safety (TISSf) controllers that explicitly incorporate general compact input constraints by deriving verifiable certificates and design conditions to guarantee input compatibility and recursive feasibility, as demonstrated in a connected cruise control application.
This paper introduces VB-NET, a physics-constrained gray-box deep learning framework that mathematically equates air conditioning systems to virtual batteries, enabling highly accurate, interpretable, and data-efficient modeling for grid regulation even with minimal historical data.
This paper proposes an innovative inverse-dynamics observer that integrates a linear single-track vehicle model with a distributed tire representation described by hyperbolic partial differential equations to accurately estimate sideslip angles and tire forces using only yaw rate and lateral acceleration measurements, even under noise and model uncertainties.