Seismic impedance inversion is a geophysical technique that transforms seismic data into quantitative subsurface properties, primarily acoustic impedance. This process enables the identification of rock boundaries, hydrocarbon reservoirs, and litholo...
The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hinde...
Modeling and learning representations for road networks and vehicle trajectories are crucial in enabling intelligent transportation systems, with applications ranging from traffic forecasting to many other downstream inference tasks. However, learnin...
Accurate and interpretable solar power forecasting is critical for effectively integrating Photo-Voltaic (PV) systems into modern energy infrastructure. This paper introduces a novel two-stage hybrid framework that couples deep learning-based time se...
Accurate biomass estimation is essential for forest monitoring, energy planning and carbon accounting in Sub-Saharan Africa (SSA), where destructive sampling is often impractical. Biomass estimation models (BEMs) offer scalable alternatives, but thei...
BACKGROUND: Underwater environments face challenges with image degradation due to light absorption and scattering, resulting in blurring, reduced contrast, and color distortion. This significantly impacts underwater exploration and environmental moni...
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, whi...
As a crucial component in rotating machinery, bearings are prone to varying degrees of damage in practical application scenarios. Therefore, studying the fault diagnosis of bearings is of great significance. This article proposes the Kepler algorithm...
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidiscip...
In this study, an adaptive force-position-speed collaborative process planning framework for robot polishing was proposed to improve the stability of the robot polishing process. The material removal model based on Preston's theory was studied, and t...
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