361 papers
This paper proposes a unified co-optimization framework for utility-scale off-grid renewable power-to-hydrogen systems that coordinates heterogeneous electrolyzers and other resources to maximize hydrogen production while ensuring frequency security through a novel system-level response model and stage-wise transient constraints.
This paper proposes an SEIRV epidemic model with relapse and intervention to analyze malware propagation dynamics, establish stability conditions, identify key influencing parameters, and optimize cost-effective control strategies using a hybrid gradient-based framework calibrated with real-world infection data.
This paper proposes an alternating learning framework for cooperative multi-agent reinforcement learning under communication constraints, where a global agent observes only a subset of local agents, and proves that this approach converges to an approximate Nash equilibrium with improved sample complexity compared to methods operating on the full joint state space.
This paper proposes a joint hardware-workload co-optimization framework using an evolutionary algorithm to design generalized in-memory computing accelerators that significantly reduce the energy-delay-area product across multiple neural network workloads, overcoming the limitations of single-workload specialized designs.
This paper evaluates various offline reinforcement learning algorithms within a stochastic telecom environment and finds that Conservative Q-Learning offers the most robust policies for network control, while sequence-based methods serve as competitive alternatives when high-quality trajectory data is abundant.
This paper proposes a method to identify nonlinear acyclic networks in continuous time with nonzero initial conditions and full excitations, demonstrating that measuring all sinks is necessary and sufficient for identifying trees and general directed acyclic graphs, while utilizing higher-order derivatives and dictionary functions to recover edge dynamics and parallel paths.
This paper establishes a converse Lyapunov theorem for the boundedness of reachability sets in infinite-dimensional systems with Lipschitz continuous flows, demonstrating its applicability to semi-linear evolution equations and deriving a result for forward completeness in ordinary differential equations without restricting input magnitudes.
This paper proposes a security-constrained substation reconfiguration framework using a novel heuristic approach with multiple master problems (HMMP) and linear AC power flow to optimize substation topology while ensuring N-1 security against line, coupler, and busbar contingencies, thereby balancing system security and operational costs with improved computational scalability.
This paper proposes a kernel-based quantum clustering method that encodes renewable energy profile similarity into an Ising model solvable by a Coherent Ising Machine, effectively alleviating the Curse of Dimensionality in NP-hard clustering problems as verified by real optical quantum computer tests.