1616 papers
This paper proposes an Energy-Time-Aware Routing Strategy (ETARS) guided by a novel Enhanced Weather Conditional Moving Average (E-WCMA) predictor to optimize relay selection and mitigate energy shortages, queue congestion, and information staleness in long-range linear energy-harvesting wireless sensor networks.
This research proposes a control-based flywheel energy storage system to enhance regenerative braking and stopping performance in vehicles, demonstrating that while the system effectively recovers kinetic energy to improve efficiency and reduce battery stress, its performance in terms of energy recovery and stopping time varies significantly under different load conditions.
This paper proposes a novel hybrid framework integrating the Whale Optimization Algorithm with Gaussian Process Regression to achieve rapid, robust, and computationally efficient PID controller tuning for automotive cruise control systems, outperforming conventional methods in convergence speed and tracking accuracy.
This study demonstrates through numerical analysis that additively manufactured octet-truss lattice cores outperform conventional aluminum honeycomb cores in heat shielding by exhibiting lower effective thermal conductivity, reduced back-face temperatures, and decreased thermal deformation, thereby offering a superior, multifunctional alternative for next-generation aerospace thermal protection systems.
This study proposes an Enhanced RIME (E-RIME) algorithm incorporating chaotic initialization and adaptive mechanisms to outperform PSO and baseline RIME in minimizing the 20-year lifecycle cost of a standalone hybrid micro-grid at the Zafarana site, Egypt, through superior convergence and solution stability in a 42-dimensional optimization problem.
This paper proposes an exact analytical subdomain model with a saturation compensation technique for a double-rotor single-stator switch reluctance machine with a core-less stator, enabling accurate prediction of radial pressure for bearingless applications across arbitrary tooth configurations.
This study proposes a hybrid AI–ontology framework that integrates LSTM and ANN models for cyclone trajectory and impact prediction with semantic reasoning to transform raw forecasts into interpretable, location-specific risk assessments and actionable decision support, as validated by case studies of recent Bay of Bengal cyclones.
This study introduces a data-driven porous media model to optimize haemodialysis by identifying dialysate flow rates that significantly reduce water and energy consumption while maintaining high solute clearance, thereby establishing a framework for more sustainable and patient-specific dialysis strategies.
This study evaluates five machine learning algorithms for predicting local scour depth around cylindrical bridge piers in cohesive soils using 122 laboratory records, finding that the QNET model achieved the highest accuracy (R² = 0.9958) while highlighting the critical influence of the Froude number and the need for cautious interpretation due to the modest sample size.
This study empirically evaluates higher-order sliding mode differentiators on a servomotor to demonstrate that replacing the discontinuous sign function with a saturation function can mitigate stability and accuracy degradation caused by low sampling rates, thereby providing practical implementation guidelines for hardware-constrained applications.