Visual StudioNET 开发环境本身有很多与语言无关的特性。而正是这些很优秀的开发环境特性,令Visual Studio NET成为大家认可的杰出开发工具。在这篇文章里,作者给出了他们最喜爱的一些环境特性——他们认为这是每个开发者都应该了解并...Visual StudioNET 开发环境本身有很多与语言无关的特性。而正是这些很优秀的开发环境特性,令Visual Studio NET成为大家认可的杰出开发工具。在这篇文章里,作者给出了他们最喜爱的一些环境特性——他们认为这是每个开发者都应该了解并且称赞的特性。其中包括:调试存储过程支持、项目引用管理、类似图中的元数据(metadata)、借助于宏来自定义开发环境的程序设计等等。读者可以在CSDN网站《程序员》频道下载本文中的示例代码。展开更多
【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
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
文摘Visual StudioNET 开发环境本身有很多与语言无关的特性。而正是这些很优秀的开发环境特性,令Visual Studio NET成为大家认可的杰出开发工具。在这篇文章里,作者给出了他们最喜爱的一些环境特性——他们认为这是每个开发者都应该了解并且称赞的特性。其中包括:调试存储过程支持、项目引用管理、类似图中的元数据(metadata)、借助于宏来自定义开发环境的程序设计等等。读者可以在CSDN网站《程序员》频道下载本文中的示例代码。
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.
