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3月16日周一
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  1. arXiv先进空中交通研究历史资料

    Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods

    Real-time rooftop wind-speed distribution is important for the safe operation of drones and urban air mobility systems, wind control systems, and rooftop utilization. However, rooftop flows show strong nonlinearity, separation, and cross-direction variability, which make flow field reconstruction from sparse sensors difficult. This study develops a learning-from-observation framework using wind-tunnel experimental data obtained by Particle Image Velocimetry (PIV) and compares Kriging interpolation with three deep learning models: UNet, Vision Transformer Autoencoder (ViTAE), and Conditional Wasserstein GAN (CWGAN). We evaluate two training strategies, single wind-direction training (SDT) and mixed wind-direction training (MDT), across sensor densities from 5 to 30, test robustness under sensor position perturbations of plus or minus 1 grid, and optimize sensor placement via Proper Orthogonal Decomposition with QR decomposition. Results show that deep learning methods can reconstruct rooftop wind fields from sparse sensor data effectively. Compared with Kriging interpolation, the deep learning models improved SSIM by up to 32.7%, FAC2 by 24.2%, and NMSE by 27.8%. Mixed wind-direction training further improved performance, with gains of up to 173.7% in SSIM, 16.7% in FAC2, and 98.3% in MG compared with single-direction training. The results also show that sensor configuration, optimization, and training strategy should be considered jointly for reliable deployment. QR-based optimization improved robustness by up to 27.8% under sensor perturbations, although with metric-depend（预印本；同行评审状态请核对原文。）

3月12日周四
  1. 低空街企业动态历史资料

    四川川发低空经济运营有限公司登记成立并揭牌，注册资本1亿元，中无人机、中海信直、川发航投等联合组建

    3月10日，四川川发低空经济运营有限公司（以下简称“川发低空运营公司”）登记成立，法定代表人为王虎，注册资本1亿元，经营范围包括通用航空服务、航空运营支持...（披露日：2026-03-12）

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