1593 papers
The paper introduces LyCo-SNN, a control framework that integrates a spiking neural network with a model-based Observer-LQR controller by using decoupled event allocation and Lyapunov-constrained execution to effectively augment structured feedback for both robotic manipulation and flight tasks under unmodeled dynamics.
This paper presents a unified, four-stage machine learning-driven pipeline that automates the selection of optimal logic locking techniques, integrates a lightweight and cryptographically robust STREAMLOCK mechanism with Dynamic Partial Reconfiguration for runtime updates, and achieves superior hardware efficiency and security across the entire IC protection lifecycle.
This study presents a parametrically optimized, hierarchical "box-in-box" vibration isolation system that significantly reduces mechanical excitations and stabilization time for microgal-precision gravimeters, thereby enhancing measurement reliability during transport in harsh environments like the Tibetan Plateau.
This paper proposes a novel deep reinforcement learning framework using a soft actor-critic algorithm to autonomously optimize a regenerative Rankine cycle with two feedwater heaters, achieving high thermal and exergy efficiencies while maintaining robust performance across diverse environmental and operational conditions.
This study utilizes AutoML and SHAP interpretability on US fatal crash data to identify distinct behavioral and contextual factors driving pedestrian failure-to-yield incidents, revealing specific risk patterns for different crossing maneuvers to inform targeted safety countermeasures.
This study utilizes multivariate data mining on 48,838 Texas police-reported crashes involving AEB-equipped vehicles to identify four distinct crash typologies, highlighting the operational heterogeneity of these incidents and providing an empirical basis for improving vehicle testing, sensing development, and roadway safety interventions rather than directly measuring AEB effectiveness.
This empirical study demonstrates that while an inertia-aware adaptive contraction control strategy for underactuated robots significantly improves mass identification compared to fixed-metric approaches, it often degrades tracking accuracy due to adaptation-induced instability, a trade-off that is partially mitigated by a proposed self-derived adaptation-rate governor.
This paper presents an auto-regressive multi-fidelity Gaussian process regression framework that fuses sparse, high-fidelity LDV measurements with abundant, biased CFD simulations to create a real-time, uncertainty-aware surrogate model for predicting disturbed velocity fields in virtual flow metering, achieving significant error reduction and reliable 95% prediction intervals.
This study demonstrates that placing mechanically shredded denim fabric in the face layers of three-layer particleboards preserves mechanical performance and meets industry standards, whereas core-layer incorporation significantly reduces strength, highlighting that maintaining the board's characteristic density gradient is more critical for performance than local material densification.
This paper presents a hardware-efficient, joint feed-aperture MIMO architecture that integrates a Butler-matrix-fed circular array with a 1-bit programmable metasurface to achieve simultaneous co-directional OAM multiplexing, low-divergence propagation, and programmable beam steering with high channel isolation.