Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-...Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.展开更多
In this paper, a new image reconstruction algorithm employing dynamic grids technique is proposed for tomographic gamma scanning. The key feature of the algorithm is the use of adaptive grid refinement in areas that i...In this paper, a new image reconstruction algorithm employing dynamic grids technique is proposed for tomographic gamma scanning. The key feature of the algorithm is the use of adaptive grid refinement in areas that indicate large values. Simulation results show that the application of dynamic grids has a good performance in emission reconstruction with a distinct advantage in the accurate positioning of the 'hot spots' and reducing the number of grids, but doesn't achieve a tangible improvement in transmission reconstruction.展开更多
移动单线激光雷达(Laser detection and ranging,LiDAR)扫描(Mobile single-layer LiDAR scanning,MSLS)树冠叶面积估计方法使用单一视角的单线激光雷达采集树冠点云数据,获取的冠层信息不够全面,限制了树冠叶面积估计精度。本文提出一...移动单线激光雷达(Laser detection and ranging,LiDAR)扫描(Mobile single-layer LiDAR scanning,MSLS)树冠叶面积估计方法使用单一视角的单线激光雷达采集树冠点云数据,获取的冠层信息不够全面,限制了树冠叶面积估计精度。本文提出一种基于移动多线LiDAR扫描(Mobile multi-layer LiDAR scanning,MMLS)的树冠叶面积估计方法,使用多线LiDAR从多个视角采集树冠点云数据,提升树冠叶面积估计精度。首先,将多线LiDAR采集的点云数据变换到世界坐标系下,通过感兴趣区域(Region of interest,ROI)提取出树冠点云。然后,提出一种MMLS树冠点云融合方法,逐个融合单个激光器采集的树冠点云,设置距离阈值删除重复点,添加新点。最后,构建MMLS空间分辨率网格,建立基于树冠网格面积的树冠叶面积估计模型。实验使用VLP-16型多线LiDAR传感器搭建MMLS系统,设置1、1.5 m 2个测量距离和间隔45°的8个测量角度对6个具有不同冠层密度的树冠进行数据采集,共得到96个树冠样本。采用本文方法,树冠叶面积线性估计模型的均方根误差(Root mean squared error,RMSE)为0.1041 m^(2),比MSLS模型降低0.0578 m^(2),决定系数R^(2)为0.9526,比MSLS模型提高0.0675。实验结果表明,本文方法通过多线LiDAR多视角树冠点云数据采集、MMLS树冠点云融合和空间分辨率网格构建,有效提升了树冠叶面积估计精度。展开更多
基金supported by the Natural Science Foundation of Henan Province(No.222300420596)China Railway Science and Technology Innovation Program Funded Project(CZ02-Special-03)Science and Technology Innovation Project funded by China Railway Tunnel Group(Tunnel Research 2021-03)。
文摘Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.
基金Supported by National Natural Science Foundation of China(No.51106095,11175118)China Postdoctoral Science Foundation(No.20110490717)
文摘In this paper, a new image reconstruction algorithm employing dynamic grids technique is proposed for tomographic gamma scanning. The key feature of the algorithm is the use of adaptive grid refinement in areas that indicate large values. Simulation results show that the application of dynamic grids has a good performance in emission reconstruction with a distinct advantage in the accurate positioning of the 'hot spots' and reducing the number of grids, but doesn't achieve a tangible improvement in transmission reconstruction.
文摘移动单线激光雷达(Laser detection and ranging,LiDAR)扫描(Mobile single-layer LiDAR scanning,MSLS)树冠叶面积估计方法使用单一视角的单线激光雷达采集树冠点云数据,获取的冠层信息不够全面,限制了树冠叶面积估计精度。本文提出一种基于移动多线LiDAR扫描(Mobile multi-layer LiDAR scanning,MMLS)的树冠叶面积估计方法,使用多线LiDAR从多个视角采集树冠点云数据,提升树冠叶面积估计精度。首先,将多线LiDAR采集的点云数据变换到世界坐标系下,通过感兴趣区域(Region of interest,ROI)提取出树冠点云。然后,提出一种MMLS树冠点云融合方法,逐个融合单个激光器采集的树冠点云,设置距离阈值删除重复点,添加新点。最后,构建MMLS空间分辨率网格,建立基于树冠网格面积的树冠叶面积估计模型。实验使用VLP-16型多线LiDAR传感器搭建MMLS系统,设置1、1.5 m 2个测量距离和间隔45°的8个测量角度对6个具有不同冠层密度的树冠进行数据采集,共得到96个树冠样本。采用本文方法,树冠叶面积线性估计模型的均方根误差(Root mean squared error,RMSE)为0.1041 m^(2),比MSLS模型降低0.0578 m^(2),决定系数R^(2)为0.9526,比MSLS模型提高0.0675。实验结果表明,本文方法通过多线LiDAR多视角树冠点云数据采集、MMLS树冠点云融合和空间分辨率网格构建,有效提升了树冠叶面积估计精度。
