1616 papers
This paper presents a novel in-hand manipulation planning framework that operates in a combined finger-object state space to overcome the limitations of traditional object-centric methods, enabling active-surface grippers to successfully plan and execute controlled sliding trajectories that achieve high success rates in both simulation and real-world experiments.
To address challenges such as poor illumination, complex backgrounds, and ballast interference in railway inspections, this paper proposes an improved D-fine based deep learning model incorporating ASFF, SCConv, and CoTAttention mechanisms, which significantly enhances the accuracy of ballasted track fastener detection in dark environments.
This paper presents and experimentally validates a compact, polarization-insensitive, quad-band metamaterial absorber based on a Concentric Octagonal Split-Ring Resonator (COSRR) that achieves high absorption and shielding effectiveness across the X, Ku, and K frequency bands.
This four-year field study demonstrates that electrochemical realkalization successfully restores the passivity of reinforcing steel in historic concrete by establishing a long-term CaCO₃-dominated solid-state buffer, while revealing that conventional phenolphthalein spray tests significantly underestimate this internal alkalinity due to slow dissolution kinetics compared to powder dissolution analyses.
This paper presents an ultra-high-speed thin film lithium niobate microring modulator on a silicon nitride-loaded platform that achieves a record-breaking 517 Gbps modulation rate, over 110 GHz electro-optic bandwidth, and 1.5 fJ/bit energy consumption, demonstrating transformative potential for next-generation photonic computing and wireless communication systems.
This study demonstrates that supervised deep learning, specifically using Restormer models, can effectively map fast (2 min 35 s) to high-quality (60 min) micro-CT acquisitions for Brazilian pre-salt carbonate rocks, significantly reducing noise and improving image quality to surpass intermediate scan times while acknowledging limitations in recovering fine details absent from the original fast inputs.
This study presents a scalable 3D printing strategy for cement-based Li-ion batteries that utilizes hydration-reinforced networks to achieve mechanically robust, hierarchically porous electrodes with exceptional long-term electrochemical stability, effectively transforming passive construction materials into active energy-storing structural components.
This study proposes an interpretable machine learning workflow that combines temporal decomposition, changepoint detection, and segmented modeling to effectively identify and analyze non-stationary temporal changes in dam monitoring data, thereby enhancing both predictive accuracy and structural health interpretation.
This paper proposes a context-aware state-transfer framework for structurally reconfigurable SDR receivers that preserves synchronization and minimizes bit-error rates by combining exact state preservation, regularized projection of configuration-dependent biases, projection-energy constraints, and reliability-oriented directional dwell times.
This paper presents a multibody-focused formulation of the moving horizon estimation problem that accommodates various model representations from ODEs to index-3 DAEs, demonstrating through comparative analysis that direct constraint handling in index-3 formulations offers superior computational efficiency while index-2 formulations provide an easily implementable alternative for enforcing kinematic constraints on both position and velocity levels.