Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attr...Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners worldwide.Despite achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in MSIF.In light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently faced.Finally,an analysis and outlook on the future development directions of MSIF are presented.展开更多
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.展开更多
When the existing information does not contain all categories,the Generalized Evidence Theory(GET)can deal with information fusion.However,the question of how to determine the number of categories through GET is still...When the existing information does not contain all categories,the Generalized Evidence Theory(GET)can deal with information fusion.However,the question of how to determine the number of categories through GET is still intriguing.To address this question,a modified k-means clustering,named centers initialized clustering is proposed,filling the gap of identification and complement of the frame of discernment.Based on this clustering method,the number of cat-egories is determined.The initialized centers selected by center density keep the cluster results con-stant,enhancing the stability of clustering results.Besides,constructing Generalized basic Probability Assignment(GBPA)modules in a conservative way improves the reliability of the results.The mass of empty set in combined GBPAs is the indicator of the number of categories.Experiments on real and artificial data sets are conducted to show the effectiveness.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.62233003 and 62073072)the Key Projects of Key R&D Program of Jiangsu Province,China(Nos.BE2020006 and BE2020006-1)the Shenzhen Science and Technology Program,China(Nos.JCYJ20210324132202005 and JCYJ20220818101206014).
文摘Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners worldwide.Despite achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in MSIF.In light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently faced.Finally,an analysis and outlook on the future development directions of MSIF are presented.
基金supported by the National Science and Technology Major Project(2021ZD0112702)the National Natural Science Foundation(NNSF)of China(62373100,62233003)the Natural Science Foundation of Jiangsu Province of China(BK20202006)。
文摘This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.
基金supported by the National Natural Science Foundation of China(No.61973332)the JSPS Invitational Fellowships for Research in Japan(Short-term).
文摘When the existing information does not contain all categories,the Generalized Evidence Theory(GET)can deal with information fusion.However,the question of how to determine the number of categories through GET is still intriguing.To address this question,a modified k-means clustering,named centers initialized clustering is proposed,filling the gap of identification and complement of the frame of discernment.Based on this clustering method,the number of cat-egories is determined.The initialized centers selected by center density keep the cluster results con-stant,enhancing the stability of clustering results.Besides,constructing Generalized basic Probability Assignment(GBPA)modules in a conservative way improves the reliability of the results.The mass of empty set in combined GBPAs is the indicator of the number of categories.Experiments on real and artificial data sets are conducted to show the effectiveness.
