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
Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming ...Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming upload traffic may lead to unacceptable uploading time.To tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading.By optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task.Although this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal solution.To tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural networks.Specifically,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM algorithm.Furthermore,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss function.Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.展开更多
With the development of power system,the operation mode of powersystem is becoming more and more complex.In order to improve the security level of power system operation,a knowledge-driven method for calculating the t...With the development of power system,the operation mode of powersystem is becoming more and more complex.In order to improve the security level of power system operation,a knowledge-driven method for calculating the transmission section limit of large-scale power grid is proposed.Firstly,a calculation model of transmission section limit is established by comprehensively considering static security and stability constraints and transient stability constraints of a large power grid;Then,a knowledge-driven calculation method of transmission section limit is proposed,which makes the calculation process of transmission section limit knowledgeable through knowledge mapping,and the implementation scheme of generator autonomous search and generator optimal switching is designed to realize the fast calculation of transmission section limit of large power grid.Finally,the effectiveness and correctness of the proposed method are verified in a regional power grid with 2128 nodes and a cross-regional power grid with 12643nodes in China.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
Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower...Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).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
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
This study delves into the dynamics of green product innovation,artificial intelligence(Al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in Oman.It emphasizes the crucial...This study delves into the dynamics of green product innovation,artificial intelligence(Al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in Oman.It emphasizes the crucial role of government intervention and R&D investments in this process.Based on the responses of 214 top managers in Oman,the research employs structural equation modeling to analyze the intricate relationships between these factors.The findings underscore a significant positive correlation between green innovation,Al implementation,and intellectual capital,with government involvement and R&D investments as vital moderators.This study provides a novel perspective on the synergy of technology,innovation,and intellectual capital in developing economies.It offers essential insights for business leaders,policymakers,and scholars,highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive advantage.The research adds to the existing body of knowledge on innovation and competitiveness.It offers practical implications for enhancing firm performance in Oman and similar emerging markets.展开更多
There is a growing interest in developing human-computer dialogue systems which is an important branch in the field of artificial intelligence(AI).However,the evaluation of large-scale Chinese human-computer dialogues...There is a growing interest in developing human-computer dialogue systems which is an important branch in the field of artificial intelligence(AI).However,the evaluation of large-scale Chinese human-computer dialogues is still a challenging task.To attract more attention to dialogue evaluation work,we held the fourth Evaluation of Chinese Human-Computer Dialogue Technology(ECDT).It consists of few-shot learning in spoken language understanding(SLU)(Task 1)and knowledge-driven multi-turn dialogue competition(Task 2),the data sets of which are provided by Harbin Institute of Technology and Tsinghua University.In this paper,we will introduce the evaluation tasks and data sets in detail.Meanwhile,we will also analyze the evaluation results and the existing problems in the evaluation.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.
基金supported in part by National Key Research and Development Program of China(2020YFB1807700)in part by National Natural Science Foundation of China(62201414)+2 种基金in part by Qinchuangyuan Project(OCYRCXM-2022-362)in part by Science and Technology Project of Guangzhou(2023A04J1741)in part by Chongqing key laboratory of Mobile Communications Technologg(cqupt-mct-202202).
文摘Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming upload traffic may lead to unacceptable uploading time.To tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading.By optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task.Although this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal solution.To tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural networks.Specifically,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM algorithm.Furthermore,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss function.Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.
基金supported by the Science andTechnology Project of State Grid:Research on artificial intelligence analysis technology of available transmission capacity(ATC)of key section under multiple power grid operation modes(5100-202255020A-1–1-ZN).
文摘With the development of power system,the operation mode of powersystem is becoming more and more complex.In order to improve the security level of power system operation,a knowledge-driven method for calculating the transmission section limit of large-scale power grid is proposed.Firstly,a calculation model of transmission section limit is established by comprehensively considering static security and stability constraints and transient stability constraints of a large power grid;Then,a knowledge-driven calculation method of transmission section limit is proposed,which makes the calculation process of transmission section limit knowledgeable through knowledge mapping,and the implementation scheme of generator autonomous search and generator optimal switching is designed to realize the fast calculation of transmission section limit of large power grid.Finally,the effectiveness and correctness of the proposed method are verified in a regional power grid with 2128 nodes and a cross-regional power grid with 12643nodes in China.
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Project (Nos. 60302012 60202002) supported by the NationaNatural Science Foundation of China and the Research GrantCouncil of the Hong Kong Special Administrative Region (NoPolyU 5119.01E) China
文摘Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).
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.
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This study delves into the dynamics of green product innovation,artificial intelligence(Al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in Oman.It emphasizes the crucial role of government intervention and R&D investments in this process.Based on the responses of 214 top managers in Oman,the research employs structural equation modeling to analyze the intricate relationships between these factors.The findings underscore a significant positive correlation between green innovation,Al implementation,and intellectual capital,with government involvement and R&D investments as vital moderators.This study provides a novel perspective on the synergy of technology,innovation,and intellectual capital in developing economies.It offers essential insights for business leaders,policymakers,and scholars,highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive advantage.The research adds to the existing body of knowledge on innovation and competitiveness.It offers practical implications for enhancing firm performance in Oman and similar emerging markets.
基金supported by the National Natural Science Foundation of China(No.62076081,No.61772153,No.61936010).
文摘There is a growing interest in developing human-computer dialogue systems which is an important branch in the field of artificial intelligence(AI).However,the evaluation of large-scale Chinese human-computer dialogues is still a challenging task.To attract more attention to dialogue evaluation work,we held the fourth Evaluation of Chinese Human-Computer Dialogue Technology(ECDT).It consists of few-shot learning in spoken language understanding(SLU)(Task 1)and knowledge-driven multi-turn dialogue competition(Task 2),the data sets of which are provided by Harbin Institute of Technology and Tsinghua University.In this paper,we will introduce the evaluation tasks and data sets in detail.Meanwhile,we will also analyze the evaluation results and the existing problems in the evaluation.
