The non-scaling effect on the penetration depth of rigid projectiles is an important issue that must be considered when extending the results of scaled experiments to prototype scenes.In this study,the evolution of th...The non-scaling effect on the penetration depth of rigid projectiles is an important issue that must be considered when extending the results of scaled experiments to prototype scenes.In this study,the evolution of the stress and strain of the target under penetration was analyzed.Expressions for the penetration resistance and penetration depth were obtained based on the conservation equation and continuity condition of the target.The penetration coefficients that characterize the nose shape,target resistance,and non-scaling effect were defined.Simplified calculation methods for the coefficients within the range of rigid projectile penetration were developed.Two methods for estimating the target parameters are proposed.The results show that the non-scaling effect is related to the failure process of the target and depends on the ratio of cavity radius to comminuted region radius.The nose shape coefficient can be approximated as a linear function of the length-to-diameter ratio of the nose.The noseshape coefficient of a flat-nosed projectile is 0.57.The caliber coefficient is related to the projectile diameter and reflects the non-scaling effect,which increases with the projectile diameter.A practical formula for calculating the penetration depth of rigid projectiles considering the non-scaling effect is also proposed.This formula is in good agreement with penetration experiments on rock and concrete.展开更多
快速、准确地获取耕地变化信息对粮食安全管理具有重要意义。针对遥感语义分割方法在大尺度范围高分辨率影像耕地非农化检测中因模型适用性不足导致错漏检较多的问题,提出一种以场景分类网络Xception为基础的耕地高分辨率影像多尺度场...快速、准确地获取耕地变化信息对粮食安全管理具有重要意义。针对遥感语义分割方法在大尺度范围高分辨率影像耕地非农化检测中因模型适用性不足导致错漏检较多的问题,提出一种以场景分类网络Xception为基础的耕地高分辨率影像多尺度场景分类方法--Multiscale Scene Classification-Xception(MSC-Xception)。该方法对耕地场景分类性能突出的轻量级场景分类网络Xception的输出层嵌入卷积注意力模块CBAM以增强模型在通道及空间特征上的提取能力,同时对单一尺度场景级分类在大尺度耕地提取中存在的混合场景分离度低和细节粗糙问题,先引入一种多尺度耕地场景特征融合的方法提高混合场景的分离度,再通过多尺度分割矢量的边界约束实现对场景级分类的边界精细化。相较于典型的Unet、PSPNet和DeeplabV3+语义分割方法,该方法能较好地减少大图斑漏检现象,在栖霞区2018年4月份GF-2影像的耕地提取实验中召回率和F1分数至少分别提高了15.1个百分点和8.8个百分点,在2018年至2022年栖霞区耕地非农化检测中,可疑图斑的查全率至少提高了7.16个百分点。展开更多
基金the National Natural Science Foundation of China(Grant Nos.52422808,52378401)to provide funds for this research。
文摘The non-scaling effect on the penetration depth of rigid projectiles is an important issue that must be considered when extending the results of scaled experiments to prototype scenes.In this study,the evolution of the stress and strain of the target under penetration was analyzed.Expressions for the penetration resistance and penetration depth were obtained based on the conservation equation and continuity condition of the target.The penetration coefficients that characterize the nose shape,target resistance,and non-scaling effect were defined.Simplified calculation methods for the coefficients within the range of rigid projectile penetration were developed.Two methods for estimating the target parameters are proposed.The results show that the non-scaling effect is related to the failure process of the target and depends on the ratio of cavity radius to comminuted region radius.The nose shape coefficient can be approximated as a linear function of the length-to-diameter ratio of the nose.The noseshape coefficient of a flat-nosed projectile is 0.57.The caliber coefficient is related to the projectile diameter and reflects the non-scaling effect,which increases with the projectile diameter.A practical formula for calculating the penetration depth of rigid projectiles considering the non-scaling effect is also proposed.This formula is in good agreement with penetration experiments on rock and concrete.
文摘快速、准确地获取耕地变化信息对粮食安全管理具有重要意义。针对遥感语义分割方法在大尺度范围高分辨率影像耕地非农化检测中因模型适用性不足导致错漏检较多的问题,提出一种以场景分类网络Xception为基础的耕地高分辨率影像多尺度场景分类方法--Multiscale Scene Classification-Xception(MSC-Xception)。该方法对耕地场景分类性能突出的轻量级场景分类网络Xception的输出层嵌入卷积注意力模块CBAM以增强模型在通道及空间特征上的提取能力,同时对单一尺度场景级分类在大尺度耕地提取中存在的混合场景分离度低和细节粗糙问题,先引入一种多尺度耕地场景特征融合的方法提高混合场景的分离度,再通过多尺度分割矢量的边界约束实现对场景级分类的边界精细化。相较于典型的Unet、PSPNet和DeeplabV3+语义分割方法,该方法能较好地减少大图斑漏检现象,在栖霞区2018年4月份GF-2影像的耕地提取实验中召回率和F1分数至少分别提高了15.1个百分点和8.8个百分点,在2018年至2022年栖霞区耕地非农化检测中,可疑图斑的查全率至少提高了7.16个百分点。
