【目的】针对风电法兰分类细、规格多、直径大、孔数多,导致多孔加工坐标计算量大、输入效率低,且极坐标、旋转坐标及宏程序、二次开发等加工方案难以满足法兰生产企业实际生产需求的问题,提出一种高效解决方案。【方法】基于Visual Stu...【目的】针对风电法兰分类细、规格多、直径大、孔数多,导致多孔加工坐标计算量大、输入效率低,且极坐标、旋转坐标及宏程序、二次开发等加工方案难以满足法兰生产企业实际生产需求的问题,提出一种高效解决方案。【方法】基于Visual Studio 2022开发平台,开发了一款高效实用、能灵活快速生成螺栓孔加工程序的专用CAM系统。该系统应用了模块化设计思路,把零件信息、加工参数等按相应模块独立处理,有利于系统根据法兰设计标准的变化而及时调整,自动生成不同规格的风电法兰螺栓孔加工程序。【结果】所开发的风电法兰螺栓孔加工CAM系统,实现了多孔加工程序的快速自动生成,显著降低了数控编程员的劳动强度,提高了法兰孔加工生产效率。【结论】未来可进一步对AutoCAD、NX平台进行二次开发,借助平台强大的二维三维图形设计基础,开发基于法兰零件的集设计制造为一体的中小型CAD/CAM系统,以满足企业不断发展的生产管理需求。展开更多
目的:系统梳理DRGs支付模式对医疗服务质量影响领域的研究热点与进展情况。方法:本研究基于Web of Science(WOS)核心合集数据库,利用CitNetExplorer文献计量软件,对1986—2025年10月间收录的206篇相关文献进行引文网络分析与可视化挖掘...目的:系统梳理DRGs支付模式对医疗服务质量影响领域的研究热点与进展情况。方法:本研究基于Web of Science(WOS)核心合集数据库,利用CitNetExplorer文献计量软件,对1986—2025年10月间收录的206篇相关文献进行引文网络分析与可视化挖掘。结果:针对DRGs支付模式对医疗服务质量影响的研究时间跨度较长,近几年进入快速增长期,当前研究热点可以划分为:(1)DRGs支付模式应用于医疗质量评价的研究;(2)DRGs支付模式对医疗服务质量的现实影响效果研究;(3)DRGs支付模式对医疗服务质量影响的异质性研究;(4)DRGs支付模式对医疗服务质量的影响机制与路径研究。未来需要尝试构建多维度分析模型,识别影响政策成效的关键情境因素,同时聚焦核心问题深入解析复杂因果作用机制,为优化DRGs支付模式和提高医疗服务质量提供更坚实的理论支撑。展开更多
Fig.1.The GenomeSyn tool for visualizing genome synteny and characterizing structural variations.A:The first synteny visualization map showed the detailed information of two or three genomes and can display structural...Fig.1.The GenomeSyn tool for visualizing genome synteny and characterizing structural variations.A:The first synteny visualization map showed the detailed information of two or three genomes and can display structural variations and other annotation information.B:The second type of visualization map was simple and only showed the synteny relationship between the chromosomes of two or three genomes.C:Multiplatform general GenomeSyn submission page,applicable to Windows,MAC and web platforms;other analysis files can be entered in the"other"option.The publisher would like to apologise for any inconvenience caused.展开更多
Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limit...Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.展开更多
美国国家仪器有限公司（NI）近日推出Measurement Studio软件的升级版本Measurement Studio 8。该软件是一个类库和控件的完整集合，适用于在基于Microsoft Visual Studio建立起来的应用程序中采集、分析和显示数据。现在借助ASP．NET的...美国国家仪器有限公司（NI）近日推出Measurement Studio软件的升级版本Measurement Studio 8。该软件是一个类库和控件的完整集合，适用于在基于Microsoft Visual Studio建立起来的应用程序中采集、分析和显示数据。现在借助ASP．NET的Web控件，这一升级版本为工程师们提供了用来创建可以在各种浏览器或操作系统下显示的Web页面的工具，以此来对他们的测试测量应用进行远程监控。Measurement Studio 8还提供与Microsoft Visual Studio 2005软件的完美集成、全新的用户界面控件、80多种新的分析方法和附加的数据采集代码生成功能。展开更多
文摘【目的】针对风电法兰分类细、规格多、直径大、孔数多,导致多孔加工坐标计算量大、输入效率低,且极坐标、旋转坐标及宏程序、二次开发等加工方案难以满足法兰生产企业实际生产需求的问题,提出一种高效解决方案。【方法】基于Visual Studio 2022开发平台,开发了一款高效实用、能灵活快速生成螺栓孔加工程序的专用CAM系统。该系统应用了模块化设计思路,把零件信息、加工参数等按相应模块独立处理,有利于系统根据法兰设计标准的变化而及时调整,自动生成不同规格的风电法兰螺栓孔加工程序。【结果】所开发的风电法兰螺栓孔加工CAM系统,实现了多孔加工程序的快速自动生成,显著降低了数控编程员的劳动强度,提高了法兰孔加工生产效率。【结论】未来可进一步对AutoCAD、NX平台进行二次开发,借助平台强大的二维三维图形设计基础,开发基于法兰零件的集设计制造为一体的中小型CAD/CAM系统,以满足企业不断发展的生产管理需求。
文摘目的:系统梳理DRGs支付模式对医疗服务质量影响领域的研究热点与进展情况。方法:本研究基于Web of Science(WOS)核心合集数据库,利用CitNetExplorer文献计量软件,对1986—2025年10月间收录的206篇相关文献进行引文网络分析与可视化挖掘。结果:针对DRGs支付模式对医疗服务质量影响的研究时间跨度较长,近几年进入快速增长期,当前研究热点可以划分为:(1)DRGs支付模式应用于医疗质量评价的研究;(2)DRGs支付模式对医疗服务质量的现实影响效果研究;(3)DRGs支付模式对医疗服务质量影响的异质性研究;(4)DRGs支付模式对医疗服务质量的影响机制与路径研究。未来需要尝试构建多维度分析模型,识别影响政策成效的关键情境因素,同时聚焦核心问题深入解析复杂因果作用机制,为优化DRGs支付模式和提高医疗服务质量提供更坚实的理论支撑。
文摘Fig.1.The GenomeSyn tool for visualizing genome synteny and characterizing structural variations.A:The first synteny visualization map showed the detailed information of two or three genomes and can display structural variations and other annotation information.B:The second type of visualization map was simple and only showed the synteny relationship between the chromosomes of two or three genomes.C:Multiplatform general GenomeSyn submission page,applicable to Windows,MAC and web platforms;other analysis files can be entered in the"other"option.The publisher would like to apologise for any inconvenience caused.
基金supported by the National Natural Science Foundation of China(Grant No.62033007)the Major Fundamental Research Program of Shandong Province(Grant No.ZR2023ZD37).
文摘Siamese tracking algorithms usually take convolutional neural networks(CNNs)as feature extractors owing to their capability of extracting deep discriminative features.However,the convolution kernels in CNNs have limited receptive fields,making it difficult to capture global feature dependencies which is important for object detection,especially when the target undergoes large-scale variations or movement.In view of this,we develop a novel network called effective convolution mixed Transformer Siamese network(SiamCMT)for visual tracking,which integrates CNN-based and Transformer-based architectures to capture both local information and long-range dependencies.Specifically,we design a Transformer-based module named lightweight multi-head attention(LWMHA)which can be flexibly embedded into stage-wise CNNs and improve the network’s representation ability.Additionally,we introduce a stage-wise feature aggregation mechanism which integrates features learned from multiple stages.By leveraging both location and semantic information,this mechanism helps the SiamCMT to better locate and find the target.Moreover,to distinguish the contribution of different channels,a channel-wise attention mechanism is introduced to enhance the important channels and suppress the others.Extensive experiments on seven challenging benchmarks,i.e.,OTB2015,UAV123,GOT10K,LaSOT,DTB70,UAVTrack112_L,and VOT2018,demonstrate the effectiveness of the proposed algorithm.Specially,the proposed method outperforms the baseline by 3.5%and 3.1%in terms of precision and success rates with a real-time speed of 59.77 FPS on UAV123.
文摘美国国家仪器有限公司（NI）近日推出Measurement Studio软件的升级版本Measurement Studio 8。该软件是一个类库和控件的完整集合，适用于在基于Microsoft Visual Studio建立起来的应用程序中采集、分析和显示数据。现在借助ASP．NET的Web控件，这一升级版本为工程师们提供了用来创建可以在各种浏览器或操作系统下显示的Web页面的工具，以此来对他们的测试测量应用进行远程监控。Measurement Studio 8还提供与Microsoft Visual Studio 2005软件的完美集成、全新的用户界面控件、80多种新的分析方法和附加的数据采集代码生成功能。
