Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral...Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.展开更多
利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代...利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代理模型优化方法。首先,依据物理结构之间的几何相似性构建易于精确解析化的相似电机;随后,建立相似电机设计变量-优化目标的解析映射模型并开展灵敏度分析;进而,对设计变量分层,将变量空间划分为高-低灵敏度子空间,以提高相似电机迁移结果与原型优化结果的一致性。少变量的高灵敏度参数空间通过原电机有限元分析(finite element analysis,FEA)数据建立常规代理模型进行优化,而多变量的低灵敏度参数空间则基于充足的相似电机解析数据并结合少量原型电机FEA数据,利用迁移学习训练多重保真代理模型完成最终优化。所提方法突破了精确解析模型拓扑限制,降低了结构复杂电机解析建模难度,并通过分层优化策略结合多重保真迁移显著提升高维优化效率,在保证精度前提下大幅减少计算量。该方法已用于内置式交替极永磁游标电机多目标优化,样机试验验证了有效性。展开更多
文摘Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.
文摘利用精确解析模型生成的数据可辅助构建数值仿真样本集,为代理模型提供高质量训练数据,从而在降低计算成本的同时提升多目标优化效率。但现有解析建模常受电机拓扑约束,适用范围有限。为此,该文提出一种基于几何相似性迁移学习的电机代理模型优化方法。首先,依据物理结构之间的几何相似性构建易于精确解析化的相似电机;随后,建立相似电机设计变量-优化目标的解析映射模型并开展灵敏度分析;进而,对设计变量分层,将变量空间划分为高-低灵敏度子空间,以提高相似电机迁移结果与原型优化结果的一致性。少变量的高灵敏度参数空间通过原电机有限元分析(finite element analysis,FEA)数据建立常规代理模型进行优化,而多变量的低灵敏度参数空间则基于充足的相似电机解析数据并结合少量原型电机FEA数据,利用迁移学习训练多重保真代理模型完成最终优化。所提方法突破了精确解析模型拓扑限制,降低了结构复杂电机解析建模难度,并通过分层优化策略结合多重保真迁移显著提升高维优化效率,在保证精度前提下大幅减少计算量。该方法已用于内置式交替极永磁游标电机多目标优化,样机试验验证了有效性。
