In the construction of Metaverses,sensors that are referred to as the“bridge of information transmission”,play a key role.The functionality and efficiency of today’s sensors,which operate in a manner similar to phy...In the construction of Metaverses,sensors that are referred to as the“bridge of information transmission”,play a key role.The functionality and efficiency of today’s sensors,which operate in a manner similar to physical sensing,are frequently constrained by their hardware and software.In this research,we proposed the Parallel Sensing framework,which includes background,concept,basic methods and typical application of parallel sensing.In our formulation,sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence,in order to boost the performance of the sensors.Each sensor will have a parallel counterpart in the virtual world within the framework of parallel sensing.Digital sensors serve as the brain of sensors and maintain the same properties as physical sensors.Parallel sensing allows physical sensors to operate in discrete time periods to conserve energy,while cloud-based descriptive,predictive,and prescriptive sensors operate continuously to offer compensation data and serve as guardians.To better illustrate parallel sensing concept,we show some example applications of parallel sensing such as parallel vision,parallel point cloud and parallel light fields,both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception models.Experimental results demonstrate the effective of parallel sensing framework.The interaction between the real and virtual worlds enables sensors to operate actively,allowing them to intelligently adapt to various scenarios and ultimately attain the goal of“Cognitive,Parallel,Crypto,Federated,Social and Ecologic”6S sensing.展开更多
SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive...SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive upgrades across industries through embedded intelligence,ushering in an era of intelligent interconnection of all things.However,managing real-time interactions among devices,infrastructure,and services in 6G networks is much more complex than in previous generations.Massive data streams from terrestrial nodes(e.g.,edge devices,sensors,distributed computing)and non-terrestrial nodes(LEO/MEO/GEO satellites)demand more intelligent and efficient processing.展开更多
In the case of composite girders, an effective cooperation of both parts of the section is influenced by deformability of connectors. Limited flexural stiffness of welded studs, used commonly in bridge structures, doe...In the case of composite girders, an effective cooperation of both parts of the section is influenced by deformability of connectors. Limited flexural stiffness of welded studs, used commonly in bridge structures, does not provide full interaction of a steel beam and a concrete slab. This changes strain distribution in cross-sections of a composite girder and results in redistribution of internal forces in steel and concrete element. In the paper partial interaction index defined on the basis of a neutral axis position, which can be used for verification of steel-concrete interaction in real bridge structures rather than in specimens is proposed. The range of the index value changes, obtained during load testing of a typical steel-concrete composite beam bridge, is presented. The investigation was carried out on a motorway viaduct, consisting of two parallel structures. During the testing values of strains in girders under static and quasi-static loads were measured. The readings from the gauges were used to determine the index, characterizing composite action of the girders. Results of bridge testing under movable load, changing position along the bridge span is presented and obtained in-situ influence functions of strains and index values are commented in the paper.展开更多
基金supported by the National Key R&D Program of China(2018AAA0101502)the Science and Technology Project of SGCC(State Grid Corporation of China):Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control。
文摘In the construction of Metaverses,sensors that are referred to as the“bridge of information transmission”,play a key role.The functionality and efficiency of today’s sensors,which operate in a manner similar to physical sensing,are frequently constrained by their hardware and software.In this research,we proposed the Parallel Sensing framework,which includes background,concept,basic methods and typical application of parallel sensing.In our formulation,sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence,in order to boost the performance of the sensors.Each sensor will have a parallel counterpart in the virtual world within the framework of parallel sensing.Digital sensors serve as the brain of sensors and maintain the same properties as physical sensors.Parallel sensing allows physical sensors to operate in discrete time periods to conserve energy,while cloud-based descriptive,predictive,and prescriptive sensors operate continuously to offer compensation data and serve as guardians.To better illustrate parallel sensing concept,we show some example applications of parallel sensing such as parallel vision,parallel point cloud and parallel light fields,both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception models.Experimental results demonstrate the effective of parallel sensing framework.The interaction between the real and virtual worlds enables sensors to operate actively,allowing them to intelligently adapt to various scenarios and ultimately attain the goal of“Cognitive,Parallel,Crypto,Federated,Social and Ecologic”6S sensing.
文摘SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive upgrades across industries through embedded intelligence,ushering in an era of intelligent interconnection of all things.However,managing real-time interactions among devices,infrastructure,and services in 6G networks is much more complex than in previous generations.Massive data streams from terrestrial nodes(e.g.,edge devices,sensors,distributed computing)and non-terrestrial nodes(LEO/MEO/GEO satellites)demand more intelligent and efficient processing.
文摘In the case of composite girders, an effective cooperation of both parts of the section is influenced by deformability of connectors. Limited flexural stiffness of welded studs, used commonly in bridge structures, does not provide full interaction of a steel beam and a concrete slab. This changes strain distribution in cross-sections of a composite girder and results in redistribution of internal forces in steel and concrete element. In the paper partial interaction index defined on the basis of a neutral axis position, which can be used for verification of steel-concrete interaction in real bridge structures rather than in specimens is proposed. The range of the index value changes, obtained during load testing of a typical steel-concrete composite beam bridge, is presented. The investigation was carried out on a motorway viaduct, consisting of two parallel structures. During the testing values of strains in girders under static and quasi-static loads were measured. The readings from the gauges were used to determine the index, characterizing composite action of the girders. Results of bridge testing under movable load, changing position along the bridge span is presented and obtained in-situ influence functions of strains and index values are commented in the paper.
