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】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
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
针对硅通孔(TSV)价格昂贵、占用芯片面积大等问题,该文采用基于云模型的进化算法对TSV数量受约束的3维片上网络(3D No C)进行测试规划研究,以优化测试时间,并探讨TSV的分配对3D No C测试的影响,进一步优化3D No C在测试模式下的TSV数量...针对硅通孔(TSV)价格昂贵、占用芯片面积大等问题,该文采用基于云模型的进化算法对TSV数量受约束的3维片上网络(3D No C)进行测试规划研究,以优化测试时间,并探讨TSV的分配对3D No C测试的影响,进一步优化3D No C在测试模式下的TSV数量。该方法将基于云模型的进化算法、小生境技术以及遗传算法的杂交技术结合起来,有效运用遗传、优胜劣汰以及保持群落的多样性等理念,以提高算法的寻优速度和寻优精度。研究结果表明,该算法既能有效避免陷入局部最优解,又能提高全局寻优能力和收敛速度,缩短了测试时间,并且优化了3D No C的测试TSV数量,提高了TSV的利用率。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。
文摘针对硅通孔(TSV)价格昂贵、占用芯片面积大等问题,该文采用基于云模型的进化算法对TSV数量受约束的3维片上网络(3D No C)进行测试规划研究,以优化测试时间,并探讨TSV的分配对3D No C测试的影响,进一步优化3D No C在测试模式下的TSV数量。该方法将基于云模型的进化算法、小生境技术以及遗传算法的杂交技术结合起来,有效运用遗传、优胜劣汰以及保持群落的多样性等理念,以提高算法的寻优速度和寻优精度。研究结果表明,该算法既能有效避免陷入局部最优解,又能提高全局寻优能力和收敛速度,缩短了测试时间,并且优化了3D No C的测试TSV数量,提高了TSV的利用率。
