Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.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
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
In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. Accor...In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.展开更多
为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学...为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。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
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
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。
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.
基金The National Natural Science Foundation of China (59990470).
文摘In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.
文摘为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。
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。
