In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back proj...In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.展开更多
Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the ...Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the unique source （plasma jet）. However, there has been tittle study on the underlying mechanism of this key process. A thorough understanding of the ignition process of a plasma torch will be helpful for optimizing the design of the plasma torch structure and selection of the ignition parameters to prolong the service life of the ignition module. Thus, in this paper, the ignition process of a segmented plasma torch （SPT） is theoretically and experimentally modeled and analyzed. Corresponding electrical models of different stages of the ignition process axe set up and used to derive the electrical parameters, e.g. the variations of the arc voltage and arc current between the cathode and anode. In addition, the experiments with different ignition parameters on a home-made SPT have been conducted. At the same time, the variations of the arc voltage and arc current have been measured, and used to verify the ones derived in theory and to determine the optimal ignition parameters for a particular SPT.展开更多
To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-sca...To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.展开更多
密集场景下群株生菜的有效分割与参数获取是植物工厂生长监测中的关键环节。针对群株生菜中个体生菜鲜质量提取问题,该研究提出一种利用实例分割模型提取个体生菜点云,再以深度学习点云算法预测个体鲜质量的方法。该方法以群株生菜为研...密集场景下群株生菜的有效分割与参数获取是植物工厂生长监测中的关键环节。针对群株生菜中个体生菜鲜质量提取问题,该研究提出一种利用实例分割模型提取个体生菜点云,再以深度学习点云算法预测个体鲜质量的方法。该方法以群株生菜为研究对象,利用深度相机采集群株生菜俯视点云,将预处理后的点云数据输入实例分割模型Mask3D中训练,实现背景与生菜个体的实例分割,之后使用鲜质量预测网络预测个体生菜鲜质量。试验结果表明,该模型实现了个体生菜点云的分割提取,无多检和漏检的情况。当交并比(intersection over union,IoU)阈值为0.75时,群株生菜点云实例分割的精确度为0.924,高于其他实例分割模型;鲜质量预测网络实现了直接通过深度学习处理点云数据,预测个体生菜鲜质量的目的,预测结果的决定系数R2值为0.90,均方根误差值为12.42 g,优于从点云中提取特征量,再回归预测鲜质量的传统方法。研究结果表明该研究预测生菜鲜质量的精度较高,为利用俯视单面点云提取群株生菜中个体生菜表型参数提供了一种思路。展开更多
亚中尺度过程是海洋学研究的前沿热点领域,从高分辨率资料中实现亚中尺度信号的快速提取对开展亚中尺度动力学研究具有重要意义。为此,根据亚中尺度过程的物理特性,提出一种基于深度学习的自动识别方法,构建了基于U-Net网络的海洋亚中...亚中尺度过程是海洋学研究的前沿热点领域,从高分辨率资料中实现亚中尺度信号的快速提取对开展亚中尺度动力学研究具有重要意义。为此,根据亚中尺度过程的物理特性,提出一种基于深度学习的自动识别方法,构建了基于U-Net网络的海洋亚中尺度过程识别网络(submesoscale processes automatic identification network, SM-Net),该网络采用视觉几何组网络作为主干特征提取网络并引入改进的混合注意力模块以提升识别能力。基于高分辨率MITgcm (Massachusetts Institute of Technology general circulation model)模式数据,通过SM-Net准确识别出南海东北部全年的亚中尺度过程,并分类为冷涡、暖涡和锋面。南海东北部亚中尺度冷涡、暖涡和锋面均多发生于冬季,夏季的发生频率较低,但吕宋海峡的亚中尺度过程全年均较为活跃。除吕宋海峡外,亚中尺度冷涡夏季多发生于台湾岛西南海域、吕宋岛西南海域和吕宋岛沿岸,冬季多发生于南海北部陆坡陆架区;亚中尺度暖涡夏季多发生于吕宋岛沿岸,冬季在南海北部陆坡陆架区较为活跃;亚中尺度锋面的时空特征与冷涡相似,但黑潮流经区域的发生频率更高。亚中尺度过程罗斯贝数和动能的时空特征与发生频率具有较好的一致性,暖涡的动能、罗斯贝数和直径均弱于冷涡。上述识别方法在南海的成功运用,为应用SWOT (surface water and ocean topography)卫星数据研究亚中尺度过程提供了一定参考。展开更多
基金supported by the National Key R&D Program of China(No.2022YFF0800601)National Scientific Foundation of China(Nos.41930103 and 41774047).
文摘In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.
基金the support of National Natural Science Foundation of China (No. 51405315)the Talents Introduction Project of Sichuan University (No. yj2012043)
文摘Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the unique source （plasma jet）. However, there has been tittle study on the underlying mechanism of this key process. A thorough understanding of the ignition process of a plasma torch will be helpful for optimizing the design of the plasma torch structure and selection of the ignition parameters to prolong the service life of the ignition module. Thus, in this paper, the ignition process of a segmented plasma torch （SPT） is theoretically and experimentally modeled and analyzed. Corresponding electrical models of different stages of the ignition process axe set up and used to derive the electrical parameters, e.g. the variations of the arc voltage and arc current between the cathode and anode. In addition, the experiments with different ignition parameters on a home-made SPT have been conducted. At the same time, the variations of the arc voltage and arc current have been measured, and used to verify the ones derived in theory and to determine the optimal ignition parameters for a particular SPT.
文摘To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.
文摘密集场景下群株生菜的有效分割与参数获取是植物工厂生长监测中的关键环节。针对群株生菜中个体生菜鲜质量提取问题,该研究提出一种利用实例分割模型提取个体生菜点云,再以深度学习点云算法预测个体鲜质量的方法。该方法以群株生菜为研究对象,利用深度相机采集群株生菜俯视点云,将预处理后的点云数据输入实例分割模型Mask3D中训练,实现背景与生菜个体的实例分割,之后使用鲜质量预测网络预测个体生菜鲜质量。试验结果表明,该模型实现了个体生菜点云的分割提取,无多检和漏检的情况。当交并比(intersection over union,IoU)阈值为0.75时,群株生菜点云实例分割的精确度为0.924,高于其他实例分割模型;鲜质量预测网络实现了直接通过深度学习处理点云数据,预测个体生菜鲜质量的目的,预测结果的决定系数R2值为0.90,均方根误差值为12.42 g,优于从点云中提取特征量,再回归预测鲜质量的传统方法。研究结果表明该研究预测生菜鲜质量的精度较高,为利用俯视单面点云提取群株生菜中个体生菜表型参数提供了一种思路。
文摘亚中尺度过程是海洋学研究的前沿热点领域,从高分辨率资料中实现亚中尺度信号的快速提取对开展亚中尺度动力学研究具有重要意义。为此,根据亚中尺度过程的物理特性,提出一种基于深度学习的自动识别方法,构建了基于U-Net网络的海洋亚中尺度过程识别网络(submesoscale processes automatic identification network, SM-Net),该网络采用视觉几何组网络作为主干特征提取网络并引入改进的混合注意力模块以提升识别能力。基于高分辨率MITgcm (Massachusetts Institute of Technology general circulation model)模式数据,通过SM-Net准确识别出南海东北部全年的亚中尺度过程,并分类为冷涡、暖涡和锋面。南海东北部亚中尺度冷涡、暖涡和锋面均多发生于冬季,夏季的发生频率较低,但吕宋海峡的亚中尺度过程全年均较为活跃。除吕宋海峡外,亚中尺度冷涡夏季多发生于台湾岛西南海域、吕宋岛西南海域和吕宋岛沿岸,冬季多发生于南海北部陆坡陆架区;亚中尺度暖涡夏季多发生于吕宋岛沿岸,冬季在南海北部陆坡陆架区较为活跃;亚中尺度锋面的时空特征与冷涡相似,但黑潮流经区域的发生频率更高。亚中尺度过程罗斯贝数和动能的时空特征与发生频率具有较好的一致性,暖涡的动能、罗斯贝数和直径均弱于冷涡。上述识别方法在南海的成功运用,为应用SWOT (surface water and ocean topography)卫星数据研究亚中尺度过程提供了一定参考。
