基于圆极化漏波阵的罗特曼透镜多波束天线设计

文章提出了一种具有无源波束扫描能力的平面圆极化多波束天线。该天线由基于基片集成波导(substrate integrated waveguide, SIW)的漏波天线阵列和罗特曼透镜馈源网络组成。其中漏波天线作为天线辐射体，通过在SIW传输线上加载90°顺序旋转的非谐振辐射缝隙，实现了圆极化的辐射特性。为了实现低成本的波束扫描能力，使用罗特曼透镜作为天线的馈电网络。该天线设计采用双层结构，通用将漏波天线阵列置于罗特曼透镜上层，实现了天线的小型化。经仿真验证，该天线可在9.5GHz~10.5GHz的工作频带范围内，实现良好的阻抗匹配及±44°的圆极化波束扫描能力，并且每个波束均可在3dB波束宽度内实现小于3dB的轴比。     

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.     

Study of aerosol anomaly associated with large earthquakes (M>6)

We present the variation of unusual atmospheric phenomena, aerosols, to understand the preseismic irregularities for two major earthquakes in Japan. We consider aerosol optical depth and Angstrom exponent data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite to establish possible connections between earthquakes and the generation of aerosols. Variation of the aerosol parameters shows significant changes before the April 15, 2016, Kumamoto earthquake ($M=7.0,h=10$ km) and the November 21, 2016, Fukushima earthquake ($M=6.9$ and $h=9$ km), where M indicates the Richter magnitude and h indicates the focal depth. To identify the source of the aerosol particles, we use the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT-4). This model uses both Lagrangian and Eulerian approaches to compute trajectories and establish a source-receptor relationship. We compute backward trajectories to check whether the aerosol generated near the epicenter is due to the preseismic processes or is transported from other areas. From our results, we conclude the fine-mode aerosols are generated in the vicinity of the epicenter, 3–7 days before the earthquakes.     

飞秒光学频率梳在计量领域的应用

飞秒光学频率梳是超快激光光学领域的重要研究方向，通过将锁模激光器重复频率（f_r）及载波包络相移频率（f_o）锁定至微波频率基准，在时域上呈现出飞秒量级的周期性超短脉冲序列，在频域上呈现出一系列离散等间隔排列的纵模分量，广泛应用于时间频率传递，空间尺度测量和精密光谱测量等诸多计量科学研究领域。根据飞秒光频梳的基本原理，着重介绍了其在计量领域的应用研究，分析了在计量领域的重要作用及研究的重要意义。