321 papers
This foundational concept paper proposes the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), an architecture integrating quantized neural networks and adaptive multi-sensor fusion to enable real-time, high-fidelity crater detection on resource-constrained, radiation-hardened space exploration hardware.
This paper proposes Learning-IPM, a novel framework that utilizes an LSTM network to project the interior-point method's central path from early stable iterations while enforcing operational constraints, thereby significantly accelerating optimal power flow solutions without compromising feasibility or accuracy.
This paper proposes a continuous, time-invariant feedback control strategy that achieves almost global asymptotic stabilization to a desired location on the n-sphere while avoiding star-shaped and conic constraints by dynamically guiding the state either along geodesics or toward the antipode of unsafe regions.
This paper proposes a dispatch-aware deep neural network (DA-DNN) that accelerates optimal transmission switching by embedding a differentiable DC-OPF layer to enforce physical constraints and generate feasible solutions without relying on costly pre-solved labels, thereby achieving scalability, robustness, and generalization to untrained system configurations.
This paper presents a radiation-hardened 130 nm CMOS delta-sigma current digitizer capable of achieving over 200 dB dynamic range and sub-picoampere resolution for beam loss monitoring, while maintaining a 10 µs response time for machine protection and demonstrating robust performance up to 100 Mrad total ionizing dose.
This paper introduces the GenAI Workbench, a conceptual Model-Based Systems Engineering framework that integrates multimodal data and generative AI to bridge the gap between detailed component design and holistic system architecture by automatically extracting requirements and generating system structures from source documents.
This paper presents a multidisciplinary design optimization framework using OpenMDAO and Dymos that simultaneously optimizes low-thrust trajectories and spacecraft power systems for asteroid orbit insertion, explicitly coupling variable-specific impulse Hall thruster performance with time-varying solar power availability to overcome the limitations of traditional simplifying assumptions.
This paper proposes a real-time, loosely coupled GNSS and IMU integration framework using Factor Graph Optimization that enhances service availability in challenging urban environments by trading off some positioning accuracy for improved computational efficiency compared to traditional batch methods.
This paper presents a real-time, tightly coupled GNSS-IMU integration method based on factor graph optimization that utilizes incremental optimization with fixed-lag marginalization to achieve robust, high-accuracy positioning in dense urban environments, as validated by experiments on the UrbanNav dataset.
This paper proposes a two-time-scale design for local safety filters in networked systems that eliminates the need for global coordination by using derivative estimation, while providing explicit bounds on safety degradation relative to the time-scale parameter and estimation errors.
This study analyzes the long-term effectiveness of eco-routing under population growth in six U.S. cities, revealing that while emissions scale superlinearly with population and eco-routing creates carbon bottlenecks, targeted capacity expansion on these critical links significantly reduces both emissions and travel time without compromising routing efficiency.
This paper bridges learning-to-communicate in multi-agent systems with decentralized stochastic control by formalizing the problem under a common-information framework, identifying quasi-classical information structures as a tractable subclass, and developing provable algorithms with quasi-polynomial complexity for such scenarios.
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.