1690 papers
This paper presents a site-specific machine learning framework that forecasts indoor laboratory relative humidity up to seven days in advance by leveraging local weather data and calendar-based occupancy patterns, thereby enabling researchers to better plan humidity-sensitive electrospinning experiments and improve reproducibility.
This paper introduces ADMOS, an adaptive dual-mode UAV swarm architecture that combines distributed state estimation, a hysteresis-based hybrid supervisor, and higher-order control barrier functions to maintain resilient asset defense and zero safety violations even under severe communication jamming where traditional consensus-based systems fail.
This study demonstrates that M30-grade eco-friendly precast concrete can be successfully produced by replacing up to 50% of natural aggregates with recycled materials and utilizing low-carbon binders like LC³ and geopolymer, thereby achieving comparable strength and durability while significantly reducing embodied carbon and supporting a circular economy.
This paper establishes a nonlinear controllability framework for coal mill pulverizers using operator theory and functional analysis, demonstrating that full controllability is achieved only when raw coal feed rate and classifier speed are utilized as control inputs while maintaining constant primary air flow.
This study utilized motion capture to analyze the biomechanics of six common Taekwondo kicks performed by experienced athletes, revealing that while kick speeds vary by technique type, the resulting kick energy remains consistently around 172 J to inform protective equipment design.
This paper presents the development and characterization of a portable creatinine biosensor based on a Ta2O5/SiO2/p-Si/Al EISCAP field-effect capacitor modified with creatinine deiminase, utilizing a combined experimental and kinetic modeling approach to evaluate its performance across varying buffer concentrations and determine key enzymatic parameters.
To address the scarcity of annotated data for global aviation contrail monitoring, this study introduces ContrailNet, a cost-efficient UNet-based computer vision model that leverages transfer learning and specialized attention mechanisms to achieve high-accuracy detection with minimal computational resources, thereby enabling rapid global coverage reconstruction and supporting regulatory compliance for climate impact assessment.
This paper proposes a neurodynamics-based motion planning framework for human–robot collaborative assembly that estimates operator fatigue from facial cues to dynamically adjust robot trajectories and safety margins, thereby balancing task efficiency with enhanced human safety without requiring prior learning.
This paper proposes a regularized code-domain matrix estimation and compensation framework that effectively mitigates RF-induced crosstalk in shared-chain digital beamforming arrays, significantly improving beamforming, nulling, and angle-estimation performance compared to unstructured least-squares approaches.
This paper demonstrates that introducing brief pauses into the vertical descent trajectory of aerial vehicles in confined spaces allows adverse wall-adjacent vortices to dissipate, thereby resetting aerodynamic instability and enabling safer, more stable flight without requiring hardware or control system modifications.