The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling ...The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.展开更多
LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previou...LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.展开更多
The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of...The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of empirical parameters only considers the forward simulation process,and insufficient consideration is given to the model inversion problem.This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function.The effectiveness of the method is then verified using measured data.Simultaneously,this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM,resulting in the following conclusions.First,blindly utilizing vegetation parameters recommended by previous model studies is not advisable.To ensure the accuracy of the simulation,it is necessary to adjust the vegetation parameters appropriately.Second,to ensure the ability of the WCM solving both forward and inverse problems,it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function.Third,soil backscatter simulations have an impact on the solution of vegetation parameters,and more accurate soil scattering models provide a better representation of the modeled vegetation.This study presents a dependable method for resolving the vegetation parameters of the WCM,thereby offering a valuable reference for the application of the model in surface parameter inversion research.展开更多
基金The Construction S&T Project of the Department of Transportation of Sichuan Province(Grant No.2023A02)the National Natural Science Foundation of China(No.52109135).
文摘The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.
基金National Natural Science Foundation of China,Grant/Award Number:51779269。
文摘The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of empirical parameters only considers the forward simulation process,and insufficient consideration is given to the model inversion problem.This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function.The effectiveness of the method is then verified using measured data.Simultaneously,this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM,resulting in the following conclusions.First,blindly utilizing vegetation parameters recommended by previous model studies is not advisable.To ensure the accuracy of the simulation,it is necessary to adjust the vegetation parameters appropriately.Second,to ensure the ability of the WCM solving both forward and inverse problems,it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function.Third,soil backscatter simulations have an impact on the solution of vegetation parameters,and more accurate soil scattering models provide a better representation of the modeled vegetation.This study presents a dependable method for resolving the vegetation parameters of the WCM,thereby offering a valuable reference for the application of the model in surface parameter inversion research.
