Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have ...Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.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
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
In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning ...In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.展开更多
Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment w...Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.展开更多
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
The scope and scale of rock engineering activities have witnessed continuous expansion,which makes the geological conditions of rock engineering increasingly complex,and there are more and more types of disasters occu...The scope and scale of rock engineering activities have witnessed continuous expansion,which makes the geological conditions of rock engineering increasingly complex,and there are more and more types of disasters occurring during the construction and operation processes.The uncertainty of engineering geological information and the unclear nature of rock mass failure and disaster mechanisms pose increasingly prominent challenges to the study of rock mechanics and engineering problems.The artificial intelligence technology develops driven by data and knowledge,especially the proposal of digital-twin technology and metaverse ideas.This has injected new innovative impetus for the development of rock mechanics and engineering intelligence,where data and knowledge have been greatly enriched and updated in recent years.This article proposes the construction idea of a rock mechanics and engineering artificial intelligence system based on the metaverse,including intelligent recognition of three-dimensional(3D)geological structures,intelligent recognition of 3D geostress,intelligent recognition of rock mechanical behavior,intelligent evaluation,monitoring and early warning of rock engineering disaster,intelligent design of rock engineering,and intelligent construction of rock engineering.Two typical engineering applications are used as case studies to illustrate the integrated method of applying this system to solve engineering problems with multiple tasks.展开更多
As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadri...As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadriven methods.In this paper,we briefly discuss general idea and advances of various transfer learning techniques in PHM domain,including domain adaptation,domain generalization,federated learning,and knowledge-driven transfer learning.Based on the observations from state of the art,we provide extensive discussions on possible challenges and opportunities of transfer learning in PHM domain to direct future development.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFB3304400)the National Natural Science Foundation of China(Nos.6230311,62303111,62076060,61932007,and 62176083)the Key Research and Development Program of Jiangsu Province of China(No.BE2022157).
文摘Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.
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。
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.
基金supported by the Key Program of the National Natural Science Foundation of China(Nos.52334008 and 51734004).
文摘In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.
基金supported by China Postdoctoral Science Foundation,Grant 2024M753544Science and Technology Project of CSG,Grant GDKJXM2022106.
文摘Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.
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funded by the National Natural Science Foundation of China(Grant Nos.51839003 and 41827806).
文摘The scope and scale of rock engineering activities have witnessed continuous expansion,which makes the geological conditions of rock engineering increasingly complex,and there are more and more types of disasters occurring during the construction and operation processes.The uncertainty of engineering geological information and the unclear nature of rock mass failure and disaster mechanisms pose increasingly prominent challenges to the study of rock mechanics and engineering problems.The artificial intelligence technology develops driven by data and knowledge,especially the proposal of digital-twin technology and metaverse ideas.This has injected new innovative impetus for the development of rock mechanics and engineering intelligence,where data and knowledge have been greatly enriched and updated in recent years.This article proposes the construction idea of a rock mechanics and engineering artificial intelligence system based on the metaverse,including intelligent recognition of three-dimensional(3D)geological structures,intelligent recognition of 3D geostress,intelligent recognition of rock mechanical behavior,intelligent evaluation,monitoring and early warning of rock engineering disaster,intelligent design of rock engineering,and intelligent construction of rock engineering.Two typical engineering applications are used as case studies to illustrate the integrated method of applying this system to solve engineering problems with multiple tasks.
文摘As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadriven methods.In this paper,we briefly discuss general idea and advances of various transfer learning techniques in PHM domain,including domain adaptation,domain generalization,federated learning,and knowledge-driven transfer learning.Based on the observations from state of the art,we provide extensive discussions on possible challenges and opportunities of transfer learning in PHM domain to direct future development.
