To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling.

This article addresses the synchronization control problem for a industrial dual ball screw feed system (DBSFS) under varying cutting load. A general, robust synchronization control scheme based on a mixed H ₂ / H _∞ control frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the error propagation mechanism of a DBSFS is revealed; a general error propagation model is established; and a mixed H ₂ / H _∞ control frame is designed to handle the asynchronous motion due to cutting load and the flexibility of the transmission components. The main advantage is that the proposed method still maintains good dual-axis synchronization performance under varying cutting loads. Considering the importance of the transient and steady-state response, prescribed performance technology is adopted to guarantee the control effect and state constraints. Furthermore, the cutting load model is identified by orthogonal experiments and the general dynamic model is verified by modal tests. Finally, comparative simulations and machining experiments show the effectiveness of this approach via a dual-driven grinding wheel machine tool.

With the increasing complexity of operating conditions and the trend toward structural compactness in generators, the unbalanced electromagnetic force induced by air-gap eccentricity has become a critical factor affecting rotor dynamic behavior and operational reliability. To address the strong coupling and modeling challenges among the electromagnetic field, mechanical force field, and lubrication flow field under eccentric conditions, this study proposes a multi-physics coupled modeling approach that integrates electromagnetic, structural, and fluid dynamic interactions. Based on the spatial pose characteristics of the rotor under eccentric conditions, a three-dimensional mathematical model of the air-gap length is established, and an analytical expression for the lubricating oil film thickness distribution is derived. This framework enables the coupled solution of unbalanced electromagnetic force, hydrodynamic oil film supporting force, and rotor dynamic response. A 60 kW-rated diesel generator was selected as the research object for both numerical simulations and experimental investigations. The numerical results indicate that when the load power increases from 0 kW to 60 kW, the displacement amplitude of the rotor in the y-direction increases by approximately 155%, demonstrating a significant enhancement of transverse vibration intensity under increasing unbalanced electromagnetic excitation. Comparison between experimental and numerical results shows good agreement in both variation trends and amplitude levels, with a maximum relative error of 4.07%, thereby validating the accuracy and reliability of the proposed electromagnetic–structural–fluid coupled model for predicting rotor dynamic response in generators.

Simultaneously, reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic. Considering the law of mass, traditional lightweight acoustic metamaterials make it difficult to contr... | Find, read and cite all the research you need on Tech Science Press