In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant fo...In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.展开更多
为实现126 k V高压真空断路器的智能化操作,满足断路器分合闸速度要求,提出一种新型的断路器分合闸电机操动机构及控制系统。结合126 k V高压真空断路器的负载特性,在分析表贴式、燕尾槽表贴埋入型、直线内嵌型和外V内嵌型4种电机转子后...为实现126 k V高压真空断路器的智能化操作,满足断路器分合闸速度要求,提出一种新型的断路器分合闸电机操动机构及控制系统。结合126 k V高压真空断路器的负载特性,在分析表贴式、燕尾槽表贴埋入型、直线内嵌型和外V内嵌型4种电机转子后,提出了一种多槽双层表贴埋入式定子及转子永磁电机设计方案,并设计了以数字信号处理器为核心的硬件控制装置。开展126 k V高压真空断路器的联机实验,结果表明,采用上述操动机构及控制系统能够满足126 k V高压真空断路器分合闸速度指标的要求,且分合闸时间具有良好的稳定性。展开更多
基金supported by Shandong Provincial Natural Science Foundation(No.ZR2023MF062)the National Natural Science Foundation of China(No.61771230).
文摘In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.
文摘为实现126 k V高压真空断路器的智能化操作,满足断路器分合闸速度要求,提出一种新型的断路器分合闸电机操动机构及控制系统。结合126 k V高压真空断路器的负载特性,在分析表贴式、燕尾槽表贴埋入型、直线内嵌型和外V内嵌型4种电机转子后,提出了一种多槽双层表贴埋入式定子及转子永磁电机设计方案,并设计了以数字信号处理器为核心的硬件控制装置。开展126 k V高压真空断路器的联机实验,结果表明,采用上述操动机构及控制系统能够满足126 k V高压真空断路器分合闸速度指标的要求,且分合闸时间具有良好的稳定性。
