The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from...The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from June 19 till June 22, 2016. The Conference was opened by NSL Director Xiangyang Huang, who placed the event within the goals of the Library, and lauded the spirit of intemational collaboration in the area of data science and knowledge discovery. The whole event was an encouraging success with over 370 registered participants and highly enlightening presentations. The Conference was organized by the Journal of Data andlnformation Science （JDIS） to bring the Joumal to the attention of an international and local audience.展开更多
In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.展开更多
This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time mo...This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time monitoring,behavior recognition,and data analysis to enhance teaching quality and management efficiency.A case study demonstrates significant improvements in student engagement,discipline,and personalized learning outcomes,with classroom interaction rates increasing by 25%and discipline issues decreasing by 40%.Despite challenges in accuracy,data storage,and ethical concerns,the integration of advanced technologies like virtual reality and blockchain offers promising potential for intelligent,data-driven educational models and quality improvement.展开更多
当参数失配时,永磁同步电机的显式模型预测(explicit model predictive,EMP)直接速度控制将出现明显的稳态静差。为此,现有方法通过配置扩张状态观测器(extended state observer,ESO)来实时观测和前馈补偿模型偏差,以实现无静差、高精...当参数失配时,永磁同步电机的显式模型预测(explicit model predictive,EMP)直接速度控制将出现明显的稳态静差。为此,现有方法通过配置扩张状态观测器(extended state observer,ESO)来实时观测和前馈补偿模型偏差,以实现无静差、高精度的转速跟随控制。但实验和理论分析表明,由于ESO的带宽有限,对于变化扰动的补偿能力较弱,参数失配时系统的动态性能恶化。为同时改善参数失配时系统的稳态控制精度和动态性能,并提高鲁棒性,该文将无模型控制与EMP控制进行融合,通过构造超局部预测模型和数据驱动观测器,提出新的EMP直接速度控制策略。实验结果表明:所提方法凭借数据驱动观测器的高观测带宽,可以同时在动态和稳态阶段实现参数失配的优良补偿,兼顾动态与稳态性能。展开更多
文摘The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from June 19 till June 22, 2016. The Conference was opened by NSL Director Xiangyang Huang, who placed the event within the goals of the Library, and lauded the spirit of intemational collaboration in the area of data science and knowledge discovery. The whole event was an encouraging success with over 370 registered participants and highly enlightening presentations. The Conference was organized by the Journal of Data andlnformation Science （JDIS） to bring the Joumal to the attention of an international and local audience.
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
基金2023 Education and Teaching Research Projects of China“Construction Education Association-Exploration and Practice of Digital Talent Cultivation for Intelligent Buildings Oriented towards China-ASEAN”(2023265)2024 Education and Teaching Reform Research Project of Guangxi Water Resources and Electric Power Vocational and Technical College“Exploration and Research on the Training Model of Innovative Digital Building Talents for China-ASEAN”(2024jgyb19)。
文摘This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time monitoring,behavior recognition,and data analysis to enhance teaching quality and management efficiency.A case study demonstrates significant improvements in student engagement,discipline,and personalized learning outcomes,with classroom interaction rates increasing by 25%and discipline issues decreasing by 40%.Despite challenges in accuracy,data storage,and ethical concerns,the integration of advanced technologies like virtual reality and blockchain offers promising potential for intelligent,data-driven educational models and quality improvement.
文摘当参数失配时,永磁同步电机的显式模型预测(explicit model predictive,EMP)直接速度控制将出现明显的稳态静差。为此,现有方法通过配置扩张状态观测器(extended state observer,ESO)来实时观测和前馈补偿模型偏差,以实现无静差、高精度的转速跟随控制。但实验和理论分析表明,由于ESO的带宽有限,对于变化扰动的补偿能力较弱,参数失配时系统的动态性能恶化。为同时改善参数失配时系统的稳态控制精度和动态性能,并提高鲁棒性,该文将无模型控制与EMP控制进行融合,通过构造超局部预测模型和数据驱动观测器,提出新的EMP直接速度控制策略。实验结果表明:所提方法凭借数据驱动观测器的高观测带宽,可以同时在动态和稳态阶段实现参数失配的优良补偿,兼顾动态与稳态性能。
