1625 papers
This paper presents the development, sensitivity analysis, and Antarctic validation of Cryotwin, a digital twin framework featuring an advanced semi-analytical model that accurately predicts and optimizes the thermal drilling performance of the IceCraft melting probe for future icy moon exploration.
This controlled benchmark study on a simulated manipulator demonstrates that learned residual controllers consistently underperform classical composite adaptive control across various conditions and that the metric of residual non-parameterizability fails to predict when learning-based approaches would be beneficial.
This paper proposes a novel evaluation framework for manufacturing plant maintenance sustainability that integrates the MELP model with complex network theory to quantify interactions among environmental, economic, and social indicators, thereby enhancing traditional models with network centrality characteristics and demonstrating robustness through sensitivity analysis.
This paper proposes a cost-sensitive stacked ensemble classifier that outperforms individual machine learning models, achieving 98.34% accuracy in classifying smart grid stability using a UCI benchmark dataset to enable effective real-time monitoring and early warning of unstable conditions.
This paper compares various near-field electromagnetic field strength calculation methods for medium-wave antennas, establishes a current distribution model to derive near-field strengths from far-field monitoring points, and analyzes deviations between theoretical calculations and field measurements to support environmental assessment and precise pollution control.
This paper proposes and evaluates three deep learning data fusion strategies that combine low-resolution noon reports with external data sources to accurately predict vessel shaft power across varying data availability scenarios, demonstrating improved accuracy, cross-fleet generalization, and practical deployment guidance for heterogeneous maritime fleets.
This paper argues that replacing exogenous cost assumptions with endogenous learning-by-doing in energy system models reveals critical non-linear policy dynamics, including sensitive intervention points and technology coupling, which are otherwise invisible and lead to systematic misestimations of policy effectiveness.
This study demonstrates that fly ash and Ground Granulated Blast-furnace Slag (GGBS) based geopolymers serve as effective, lower-carbon alternatives to ordinary Portland cement for stabilizing Madurai clay, with 35% fly ash and 45% GGBS mixtures significantly enhancing unconfined compressive strength, reducing compressibility, and minimizing volumetric shrinkage.
This paper introduces ST-GNNFormer SurgeCast, a globally adaptive graph transformer framework that successfully unifies tropical cyclone-induced storm surge forecasting across all ocean basins into a single model, demonstrating robust generalization in data-sparse regions and achieving high predictive accuracy on both reanalysis and independent in-situ observational data.
This paper proposes an efficient full-discretization framework based on the Implicit Adams Method to enhance the accuracy and computational efficiency of chatter prediction and stability lobe generation for robotic milling, particularly under low radial immersion and varying postures.