1710 papers
This paper challenges the treatment of AI data centers as inflexible loads by proposing an eleven-parameter framework that quantifies their spatial, temporal, and voltage-frequency flexibility, demonstrating through large-scale analysis that significant capacity accreditation is achievable and that remaining barriers are primarily institutional rather than physical.
This paper proposes a novel hybrid control architecture that combines a data-driven shallow neural network for real-time stick-slip delay prediction with an LMI-optimized Super-Twisting Sliding Mode Controller to significantly enhance trajectory tracking and stability in reconfigurable systems, achieving up to a 99% reduction in tracking error compared to conventional methods.
This study introduces a novel multi-scale framework that decouples macroscopic cycling efficiency from microscopic trajectory stability using crowdsourced GPS data and advanced machine learning, revealing critical non-linear environmental thresholds in Shenzhen to shift urban cycling governance from static allocation to precise, parametric interventions.
This paper presents 2D and 3D CFD models validated against microgravity tank filling experiments to characterize the relationship between fill rates, geyser flow regimes, and pressure evolution, ultimately demonstrating that stable geysers produce higher peak pressures and that 3D modeling is essential for capturing wobbly geyser behaviors.
This paper presents a full-scale proof of concept for a modular, demountable concrete system that integrates rigid origami principles with textile reinforcement to enable the fabrication and adhesive-free assembly of thin-walled, self-supporting structures through a novel fold-and-plug interlocking mechanism.
This paper proposes a hierarchical braking control strategy for aircraft engine ground test rigs that accounts for jet thrust coupling, utilizing a thrust-corrected dynamic model and coordinated force distribution to significantly improve braking stability and reduce tracking errors compared to conventional and fuzzy control methods.
This paper presents a computationally efficient, model-based motion planning approach that utilizes input-output normal forms and explicit numerical integration to enable two independent carts to cooperatively transport a cable-suspended underactuated load with exact trajectory tracking and verified tension feasibility, achieving significantly reduced tracking errors and oscillations compared to classical model-free methods in experimental industrial settings.
This study proposes and evaluates five machine learning algorithms trained on finite element data to predict the blast-induced damage of reinforced concrete columns, identifying CatBoost as the most effective model and highlighting structural depth, charge weight, and reinforcement yield strength as the primary influencing factors.
This study develops a Random Forest-based machine learning framework using LTPP data to accurately predict the occurrence and severity of moisture damage in asphalt pavements, enabling transportation agencies to implement proactive, climate-resilient maintenance strategies.
This paper introduces PACT, an interpretable decentralized coordination mechanism for electric-vehicle charging that uses a single broadcast price signal to guarantee hard capacity constraints are never exceeded while significantly reducing costs and meeting deadlines compared to independent learning approaches.