This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is pr...This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.展开更多
Relation extraction is a key task for knowledge graph construction and natural language processing,which aims to extract meaningful relational information between entities from plain texts.With the development of deep...Relation extraction is a key task for knowledge graph construction and natural language processing,which aims to extract meaningful relational information between entities from plain texts.With the development of deep learning,many neural relation extraction models were proposed recently.This paper introduces a survey on the task of neural relation extraction,including task description,widely used evaluation datasets,metrics,typical methods,challenges and recent research progresses.We mainly focus on four recent research problems:(1)how to learn the semantic representations from the given sentences for the target relation,(2)how to train a neural relation extraction model based on insufficient labeled instances,(3)how to extract relations across sentences or in a document and(4)how to jointly extract relations and corresponding entities?Finally,we give out our conclusion and future research issues.展开更多
基金This work was supported in part by the Australian Research Council Discovery Early Career Researcher Award under Grant DE200101128.
文摘This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.
基金the National Natural Science Foundation of China(Grant Nos.61922085 and 61533018)the Natural Key R&D Program of China(Grant No.2018YFC0830101)+3 种基金the Key Research Program of the Chinese Academy of Sciences(Grant No.ZDBS-SSW-JSC006)Beijing Academy of Artificial Intelligence(BAAI2019QN0301)the Open Project of Beijing Key Laboratory of Mental Disorders(2019JSJB06)the independent research project of National Laboratory of Pattern Recognition。
文摘Relation extraction is a key task for knowledge graph construction and natural language processing,which aims to extract meaningful relational information between entities from plain texts.With the development of deep learning,many neural relation extraction models were proposed recently.This paper introduces a survey on the task of neural relation extraction,including task description,widely used evaluation datasets,metrics,typical methods,challenges and recent research progresses.We mainly focus on four recent research problems:(1)how to learn the semantic representations from the given sentences for the target relation,(2)how to train a neural relation extraction model based on insufficient labeled instances,(3)how to extract relations across sentences or in a document and(4)how to jointly extract relations and corresponding entities?Finally,we give out our conclusion and future research issues.
