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中国科学：信息科学（英文版）
中国科学：信息科学（英文版）

周光召

月刊

1674-733X

informatics@scichina.org

010-64015683

100717

北京东黄城根北街16号

中国科学：信息科学（英文版）/Journal Science China Information SciencesCSCDCSTPCDEISCI
查看更多>>《中国科学》是中国科学院主办、中国科学杂志社出版的自然科学专业性学术刊物。《中国科学》任务是反映中国自然科学各学科中的最新科研成果，以促进国内外的学术交流。《中国科学》以论文形式报道中国基础研究和应用研究方面具有创造性的、高水平的和有重要意义的科研成果。在国际学术界，《中国科学》作为代表中国最高水平的学术刊物也受到高度重视。国际上最具有权威的检索刊物SCI，多年来一直收录《中国科学》的论文。1999年《中国科学》夺得国家期刊奖的第一名。
正式出版
收录年代

    A path planning algorithm for a crop monitoring fixed-wing unmanned aerial system

    Longhao QIANYi Lok LOHugh Hong-tao LIU
    1-19页
    查看更多>>摘要：With the growing demand for automation in agriculture,industries increasingly rely on drones to perform crop monitoring and surveillance.In this regard,fixed-wing unmanned aerial systems(UASs)are viable platforms for scanning a large crop field,given their payload capacity and range.To achieve maximum coverage without landing for battery replacement,an algorithm for producing a minimal required energy survey path is essential.Hence,an energy-aware coverage path planning algorithm is proposed herein.The constraints for a fixed-wing UAS to fly at low altitudes while achieving full coverage of the crop field are first analyzed.Then,the full path is decomposed into straight-line and U-turn primitives.Finally,an algorithm to calculate a combination of straight-line segments and U-turns is proposed to obtain the path with minimum required energy consumption.The genetic algorithm is used to efficiently determine the order of the straight-line paths to traverse.Case studies show that the proposed algorithm can produce planning results for a convex-polygon-shaped crop field.
      原文链接:
    • 万方数据
    • 维普

    Optimal rejection of bounded perturbations in linear leader-following consensus protocol:invariant ellipsoid method

    Siyuan WANGAndrey POLYAKOVMin LIGang ZHENG...
    20-30页
    查看更多>>摘要：The objective of the invariant ellipsoid method is to minimize the smallest invariant and attrac-tive set of a linear control system operating under the influence of bounded external disturbances.This study extends the application of this method to address the leader-following consensus problem.Initially,a linear control protocol is designed for the multi-agent system in the absence of disturbances.Subsequently,in the presence of bounded disturbances,by employing a similar linear control protocol,a necessary and sufficient condition is introduced to derive the optimal control parameters for the multi-agent system such that the state of followers converges to and remains in a minimal invariant ellipsoid around the state of the leader.
      原文链接:
    • 万方数据
    • 维普

    Autonomous multi-drone racing method based on deep reinforcement learning

    Yu KANGJian DIMing LIYunbo ZHAO...
    31-44页
    查看更多>>摘要：Racing drones have attracted increasing attention due to their remarkable high speed and excel-lent maneuverability.However,autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction.To address these issues,we propose a novel autonomous multi-drone racing method based on deep reinforcement learning.A new set of reward functions is proposed to make racing drones learn the racing skills of human experts.Unlike previous methods that required global information about tracks and track boundary constraints,the proposed method requires only limited localized track information within the range of its own onboard sensors.Further,the dynamic re-sponse characteristics of racing drones are incorporated into the training environment,so that the proposed method is more in line with the requirements of real drone racing scenarios.In addition,our method has a low computational cost and can meet the requirements of real-time racing.Finally,the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments.
      原文链接:
    • 万方数据
    • 维普

    UAV swarm air combat maneuver decision-making method based on multi-agent reinforcement learning and transferring

    Zhiqiang ZHENGChen WEIHaibin DUAN
    45-62页
    查看更多>>摘要：During short-range air combat involving unmanned aircraft vehicle(UAV)swarms,UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs.This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat.In this paper,a method based on multi-agent reinforcement learning(MARL)is proposed to address this issue.An actor network containing three subnetworks that can handle different types of situational information is designed.Hence,the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process.Separate state spaces for local and global information are designed for the actor and critic networks.A detailed reward function is proposed to encourage participation.To prevent lazy participants in air combat,a reward assignment operation is applied to distribute these dense rewards.Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario,and reflect the effectiveness of the proposed method.
      原文链接:
    • 万方数据
    • 维普

    Dynamic event-triggered fault-tolerant cooperative resilient tracking control with prescribed performance for UAVs

    Rong YUANZhengcai ANShuyi SHAOMou CHEN...
    63-80页
    查看更多>>摘要：In this paper,a resilient tracking control scheme with cooperative collision avoidance performance is studied for the fixed-wing unmanned aerial vehicle(UAV)leader-follower formation in the presence of actuator failures and external disturbances.Firstly,based on the control objectives of UAV formation tracking and collision avoidance,the transformation tracking errors are obtained using the prescribed performance control technique.Next,a fault detection mechanism is introduced to determine if there is the actuator fault.Subsequently,the event-triggered resilient fault observers are designed based on a dynamic event-triggered mechanism to estimate actuator faults.Furthermore,the prescribed performance functions and the H∞ performance index are employed to ensure the UAV formation collision-free and mitigate the impact of disturbances.Moreover,the resilient controller is designed to minimize the effect of the perturbations for the control gain and the fault observer gain on the system.The stability of the system is also proven through the Lyapunov stability analysis,and the controller gains are calculated by solving the linear matrix inequality.Finally,the validity of the proposed control strategy is demonstrated by the simulation analysis.
      原文链接:
    • 万方数据
    • 维普

    Lead-free metal halide scintillator materials for imaging applications

    Junzhe LINDan GUOTianrui ZHAI
    81-97页
    查看更多>>摘要：High-energy radiation detection and imaging technology has significant applications in high-energy physics research,medical imaging,and industrial monitoring.Lead-free metal halides exhibit excep-tional potential for conducting indirect detection of high-energy radiation due to their characteristics of low toxicity,strong stability,high light yield,and large Stokes shift.This paper reviews the most recent advances in lead-free metal halide scintillator materials for X-ray imaging.Subsequently,it lists the most important parameters of scintillator performance and introduces the production procedures for single crystal,powder,and nanocrystal scintillators.Furthermore,it discusses the manufacturing of scintillator films with improved performance,focusing on large-area flexible scintillator films and the coupling with microstructures.Finally,it discusses current challenges and opportunities for enhancing X-ray imaging using lead-free metal halide scintillator materials.
      原文链接:
    • 万方数据
    • 维普

    Multi-agent policy transfer via task relationship modeling

    Rongjun QINFeng CHENTonghan WANGLei YUAN...
    98-110页
    查看更多>>摘要：Team adaptation to new cooperative tasks is a hallmark of human intelligence,which has yet to be fully realized in learning agents.Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks.We posit that the relationship among tasks provides key information for policy adaptation.We utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams,proposing to learn an effect-based task representation as a common latent space among tasks,and using it to build an alternatively fixed training scheme.Herein,we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks.Thus,the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source tasks.Furthermore,the learned transferred policies help solve tasks that are difficult to learn from scratch.
      原文链接:
    • 万方数据
    • 维普

    Learning-based counterfactual explanations for recommendation

    Jingxuan WENHuafeng LIULiping JINGJian YU...
    111-125页
    查看更多>>摘要：Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause.They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism.Among several counterfactual recommendation methods,item-based counterfactual explanation methods have attracted considerable at-tention because of their flexibility.The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items(i.e.,short length)such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions(i.e.,good quality).Usually,explanations are generated by ranking the precomputed importance of items,which fails to characterize the true importance of interacted items due to separation from the explanation generation.Ad-ditionally,the final explanations are generated according to a certain search strategy given the precomputed importance.This indicates that the quality and length of counterfactual explanations are deterministic;therefore,they cannot be balanced once the search strategy is fixed.To overcome these obstacles,this study proposes learning-based counterfactual explanations for recommendation(LCER)to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference.To achieve consistency between the computation of importance and generation of counterfac-tual explanations,the proposed LCER endows an optimizable importance for each interacted item,which is supervised by the goal of counterfactual explanations to guarantee its credibility.Because of the model's flexibility,the trade-off between quality and length can be customized by setting different proportions.The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several state-of-the-art baselines,both quantitatively and qualitatively.
      原文链接:
    • 万方数据
    • 维普

    EmotionIC:emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

    Yingjian LIUJiang LIXiaoping WANGZhigang ZENG...
    126-142页
    查看更多>>摘要：Emotion recognition in conversation(ERC)has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies.In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC)for ERC tasks.Our EmotionIC consists of three main components,i.e.,identity masked multi-head attention(IM-MHA),dialogue-based gated recurrent unit(DiaGRU),and skip-chain conditional random field(SkipCRF).Compared to previous ERC models,EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels.The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level.Specifically,IMMHA is applied to capture identity-based global contextual dependencies,while DiaGRU is utilized to extract speaker-and temporal-aware local contextual information.At the classification level,SkipCRF can explicitly mine com-plex emotional flows from higher-order neighboring utterances in the conversation.Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets.The ablation studies confirm that our modules can effectively model emotional inertia and contagion.
      原文链接:
    • 万方数据
    • 维普

    SeeMore:a spatiotemporal predictive model with bidirectional distillation and level-specific meta-adaptation

    Yuqing MAWei LIUYajun GAOYang YUAN...
    143-167页
    查看更多>>摘要：Predicting future frames using historical spatiotemporal data sequences is challenging and criti-cal,and it is receiving a lot of attention these days from academic and industrial scholars.Most spatiotempo-ral predictive algorithms ignore the valuable backward reasoning ability and the disparate learning complex-ities among different layers and hence,cannot build good long-term dependencies and spatial correlations,resulting in suboptimal solutions.To address the aforementioned issues,we propose a two-stage coarse-to-fine spatiotemporal predictive model with bidirectional distillation and level-specific meta-adaptation(SeeMore)in this paper,which includes a bidirectional distillation network(BDN)and a level-specific meta-adapter(LMA),to gain bidirectional multilevel reasoning.In the first stage,BDN concentrates on bidirectional dynamics modeling and coarsely constructs spatial correlations of different layers,while LMA is introduced in the second fine-tuning stage to refine the multilevel spatial correlations from a meta-learning perspective.In particular,BDN mimics the forward and backward reasoning abilities of humans in a distillation manner,which aids in the development of long-term dependencies.The LMA views learning of different layers as disparate but related tasks and guides the transfer of learning experiences among these tasks through learning complexities.Thus,each layer could be closer to its solutions and could extract more informative spatial cor-relations.By capturing the enhanced short-term spatial correlations and long-term temporal dependencies,the proposed model could extract adequate knowledge from sequential historical observations and accurately predict future frames whose backtracking preconditions are consistent with the historical sequence.Our work is general and robust enough to be integrated into most spatiotemporal predictive models without requiring additional computation or memory cost during inference.Extensive experiments on four widely used pre-dictive learning benchmarks validated the proposed model's effectiveness in comparison to state-of-the-art approaches(e.g.,10.6％improvement of Mean Squared Error on the Moving MNIST dataset).
      原文链接:
    • 万方数据
    • 维普