【目的】解决钢箱系杆拱桥的钢拱肋在施工过程中精度控制难度大和耗时长的问题。【方法】以某钢箱系杆拱桥为工程背景,采用建筑信息模型(building information modeling,BIM)及3D激光扫描技术,对拱肋钢构件在加工制作与拼接过程中的质...【目的】解决钢箱系杆拱桥的钢拱肋在施工过程中精度控制难度大和耗时长的问题。【方法】以某钢箱系杆拱桥为工程背景,采用建筑信息模型(building information modeling,BIM)及3D激光扫描技术,对拱肋钢构件在加工制作与拼接过程中的质量检测进行信息化管控。【结果】BIM技术结合3D激光扫描技术可快速地检测钢拱肋构件的质量并监测拱肋施工线形;钢箱拱肋构件的最大制作误差在1.2 mm以内,构件在拼接过程中的最大误差在1.1 mm以内,以上误差均满足设计规范的要求;与传统检测方法相比,点云数据在各坐标轴方向的偏差为1.0~3.0 mm,平均偏差为1.2~1.5 mm,具有较高的可靠性。【结论】基于BIM+3D激光扫描技术,可实现钢箱拱肋构件施工过程中拱肋线形质量的动态管控。展开更多
Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an eff...Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.展开更多
【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方...【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方法和构建高精确巷道三维模型技术。【方法】通过基于邻域半径R、最小邻域点数Imin、空间阈值σc、特征保持因子σs等参数自适应的分类巷道点云去噪算法,设计基于符号距离函数(signed distance functions,SDF)的深度学习隐式曲面重建方法。集成均值法、改进的基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法和改进的双边滤波算法,构建分类处理技术框架,集成算法自动分析巷道点云数据中的噪声类型,并通过自适应机制高效去除不同尺度噪声,实现主体点云数据的深度净化。采用PointNet++提取巷道点云局部区域特征,导入神经隐式网络学习局部上下文信息,生成全局模型SDF,并结合移动立方体算法构建精细化的巷道三维模型。【结果和结论】以安徽省张集煤矿1∶1模拟巷道为实验场景,开展多维空间的巷道点云去噪与三维重建研究。研究结果表明:(1)集成算法可根据巷道场景与噪声类别动态调整去噪策略,具备自适应优化性能,产生的Ⅰ类和Ⅱ类误差分别为1.54%和5.37%,可在保留主体点云特征的同时有效去除大、小尺度及重复点三类噪声。(2)重建算法能在保持巷道模型精度与光滑度的同时有效减少孔洞,且精准刻画复杂位置的结构细节,重建巷道的平均偏差、标准偏差、均方根误差分别为0.037、0.040、0.041 m,满足智能化矿山建设高精度要求,为矿山数字化转型升级与智能精准开采提供高质量的三维数据支撑。展开更多
针对TSV数量限制下的3D No C测试,如何在功耗约束条件下充分利用有限的TSV资源快速地完成3D No C测试,这属于NP难问题,采用基于云模型的进化算法对有限的TSV资源进行位置寻优,以及对通信资源进行分配研究,在满足功耗约束以及路径不冲突...针对TSV数量限制下的3D No C测试,如何在功耗约束条件下充分利用有限的TSV资源快速地完成3D No C测试,这属于NP难问题,采用基于云模型的进化算法对有限的TSV资源进行位置寻优,以及对通信资源进行分配研究,在满足功耗约束以及路径不冲突条件下调度测试数据,以实现芯核的最大化并行测试,减少测试时间。以ITC’02测试标准电路作为实验对象,实验结果表明,本文方法可以有效地进行TSV的位置寻优以及资源的合理分配,从而提高TSV利用率,减少测试时间。展开更多
基金the National Key R&D Program of China(2017YFB1002702).
文摘Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.
文摘【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方法和构建高精确巷道三维模型技术。【方法】通过基于邻域半径R、最小邻域点数Imin、空间阈值σc、特征保持因子σs等参数自适应的分类巷道点云去噪算法,设计基于符号距离函数(signed distance functions,SDF)的深度学习隐式曲面重建方法。集成均值法、改进的基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法和改进的双边滤波算法,构建分类处理技术框架,集成算法自动分析巷道点云数据中的噪声类型,并通过自适应机制高效去除不同尺度噪声,实现主体点云数据的深度净化。采用PointNet++提取巷道点云局部区域特征,导入神经隐式网络学习局部上下文信息,生成全局模型SDF,并结合移动立方体算法构建精细化的巷道三维模型。【结果和结论】以安徽省张集煤矿1∶1模拟巷道为实验场景,开展多维空间的巷道点云去噪与三维重建研究。研究结果表明:(1)集成算法可根据巷道场景与噪声类别动态调整去噪策略,具备自适应优化性能,产生的Ⅰ类和Ⅱ类误差分别为1.54%和5.37%,可在保留主体点云特征的同时有效去除大、小尺度及重复点三类噪声。(2)重建算法能在保持巷道模型精度与光滑度的同时有效减少孔洞,且精准刻画复杂位置的结构细节,重建巷道的平均偏差、标准偏差、均方根误差分别为0.037、0.040、0.041 m,满足智能化矿山建设高精度要求,为矿山数字化转型升级与智能精准开采提供高质量的三维数据支撑。
文摘针对TSV数量限制下的3D No C测试,如何在功耗约束条件下充分利用有限的TSV资源快速地完成3D No C测试,这属于NP难问题,采用基于云模型的进化算法对有限的TSV资源进行位置寻优,以及对通信资源进行分配研究,在满足功耗约束以及路径不冲突条件下调度测试数据,以实现芯核的最大化并行测试,减少测试时间。以ITC’02测试标准电路作为实验对象,实验结果表明,本文方法可以有效地进行TSV的位置寻优以及资源的合理分配,从而提高TSV利用率,减少测试时间。
