1639 papers
This paper proposes a novel second-order sliding mode control strategy utilizing multi-channel decomposition and a super-twisting algorithm to effectively suppress chattering and enhance robustness against aerodynamic disturbances in quadrotor UAVs.
This paper presents a high-gain dual-switch non-isolated boost converter (DSNIBC) that utilizes a switched-inductor topology and interdependent duty cycles to achieve superior voltage gain with moderate stress, validated through comprehensive theoretical analysis, small-signal modeling, and experimental testing under photovoltaic MPPT conditions.
This study demonstrates the feasibility of a robotic additive repair workflow for damaged rammed-earth walls using a cement-free printable material, confirming successful wall-scale construction and preliminary mechanical performance where repaired specimens achieved 56.1% of the reference load-bearing capacity with effective interface bonding.
This paper presents and validates an IoT-driven electronics framework for tabletop CNC machines that integrates an ESP32-S3 microcontroller, Raspberry Pi 4B gateway, and multi-sensor array to achieve low-latency cloud connectivity, high-precision machining with sub-23 µm positioning error, and robust thermal management, establishing it as a cost-effective solution for smart manufacturing and Industry 4.0 applications.
This study demonstrates that adding 8% rice husk ash optimally improves the engineering properties of three Nigerian lateritic soils, with kaolinite-rich soils showing the most significant strength gains, thereby highlighting the critical influence of soil mineralogy on stabilization performance.
This paper proposes a physics-informed operator-decomposition framework that integrates an attention-enhanced U-Net, Fourier Neural Operator, and Karush-Kuhn-Tucker condition penalties to efficiently and accurately predict full-field pressure and displacement responses in three-dimensional layered rough surface contacts, overcoming the computational and geometric limitations of existing methods.
This study presents a hard-constrained physics-informed neural network (H-PINN) framework that significantly outperforms standard PINN and traditional numerical methods in accurately modeling biodegradable contaminant transport through GCL/SL composite liners and successfully identifies soil liner degradation parameters via inverse modeling.
This paper presents a comparative uncertainty-aware analysis of a conceptual autonomous bimodal morphing quadrotor swarm (MORPHFIRE) for early-stage wildfire hotspot suppression, evaluating the mass-energy performance and operational range of hybrid-electric and pure-electric architectures under specific pre-positioned intervention scenarios without modeling actual fire behavior or suppression effectiveness.
This study demonstrates that fabricated AlGaN/GaN HEMT devices exhibit a dose-dependent reduction in forward bias current following exposure to 60Co gamma radiation, confirming their sensitivity to low-dose radiation and potential utility in radiation-hard electronic systems.
This paper proposes an energy-constrained false data injection attack strategy for cyber-physical systems that maximizes terminal estimation error covariance while maintaining stealthiness, quantified by the Wasserstein distance, through an optimized attack signal and scheduling policy.