检索结果-内蒙古大学图书馆

IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-17页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China James Watt School of Engineering University of Glasgow Glasgow U.K State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. IEEE

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Large Language Models as Few-Shot Defect Detectors for Additive Manufacturing

Large Language Models as Few-Shot Defect Detectors for Addit...

引用

2024 China Automation Congress, CAC 2024

作者： Fang, Qihang Xiong, Gang Wang, Fang Shen, Zhen Dong, Xisong Wang, Fei-Yue State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368604

Additive manufacturing (AM) is a promising technology to tailor complex structures in a layer-by-layer fashion. Its wide adoption, however, is hampered by occurrence of defects that deteriorates product quality. Defect detection methods have therefore been developed to address quality issues, primarily utilizing deep learning models. Despite achieving appreciable results, these models require a substantial amount of labeled data and extensive training, which are costly and time-consuming. Thus, it is worth developing a new approach that can perform defect detection (1) with minimal labeled data, and (2) without the necessity for model updating. Recently, the large language model (LLM) has demonstrated remarkable generalization capability across various fields. It features an in-context learning (ICL) capability that allows it to learn from a context without any retraining. This study explores the potential of applying an LLM for AM defect detection based on ICL. To this end, a dataset pertaining to the resin deposition status in vat photopolymerization, as a field where prevailing LLMs have limited exposure, is collected. Two contributions are made to achieve more accurate defect detection by prompting. First, a multimodal context that includes both image-label pairs and expert knowledge is tailored, which contains comprehensive information about deposition status. Second, a repeated query and voting (RQV) technique is proposed to mitigate occasional misjudgements, thus enhancing the reliability of the defect detection. Comprehensive ablation studies verify the effectiveness of both the multimodal context and RQV technique. As a result, 96.2% classification accuracy is achieved in such a challenging few-shot setting, obviating the need of model updating. © 2024 IEEE.

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

引用

Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly *** aims to pursue a list of preferred items for a target *** deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and ***,these methods may suffer from data sparsity *** for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and *** interactions,which take place in the range of a group,form a local view of a certain *** addition to local information,groups with common interests may also show similar tastes on ***,group representation can be conducted according to the similarity among groups,which forms a global view of a certain *** this paper,we propose a novel global and local information fusion neural network(GLIF)model for group *** GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local ***,our model also leverages ANN to obtain a group′s global representation based on the similarity among different ***,it fuses global and local representations based on attention mechanism to form a group′s comprehensive ***,group recommendation is conducted under neural collaborative filtering(NCF)*** experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

来源：评论

学校读者我要写书评

暂无评论

Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting 9th

The Prospects of Multi-modal Pre-trained Models in Epidemi...

引用

9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Fei, Jiaqiang Zhao, Pengfei Luo, Tianyi Wang, Jiaojiao Cao, Zhidong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819758029

Accurate epidemic forecasting is imperative in light of the increasing global threat posed by epidemic diseases. Presently, researchers predominantly approach this challenge as a time-series prediction task and have proposed a variety of effective methodologies. However, two critical issues deserve thorough consideration. Firstly, the transmission of infectious diseases is a profoundly intricate process influenced by a multitude of factors, encompassing complex dynamics and propagation mechanisms. In contrast to the multifaceted nature of real-world scenarios, prevailing modeling approaches often exhibit undue simplification, resulting in imprecision and a deficit of interpretability. Secondly, existing methodologies excessively rely on data and require substantial adaptation for new infectious disease scenarios. The endeavor to conduct rapid and precise simulations and predictions during the early stages of transmission is challenging due to limited data availability. Thus, we aim to explore the feasibility and effective strategies of leveraging multi-modal pre-trained technology for epidemic forecasting. This approach offers the potential to comprehensively grasp the transmission process from multi-view perspectives and acquire richer representations by integrating multi-modal data. Moreover, through pre-trained, this methodology captures overarching patterns and principles of infectious disease transmission, facilitating swift and accurate predictions via fine-tuning, particularly in scenarios of sparse early-stage transmission data. We discuss the prospects of multi-modal pre-trained models in epidemic forecasting and provide feasible strategies for constructing the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Computational Experiments for complex Social systems:Experiment Design and Generative Explanation

引用

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

来源：评论

学校读者我要写书评

暂无评论

Neural Network Observer based LPV Fault Tolerant control of a Flying-wing Aircraft

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2023年 1-15页

作者： Liu, Shiqian Lyv, Weizhi Yang, Congjie Wang, Feiyue Whidborne, James F. school of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China school of Aerospace Transport and Manufacturing Cranfield University Cranfield UK

For the problem of fault tolerant trajectory tracking control for a large Flying-Wing (FW) aircraft with Linear Parameter-Varying (LPV) model, a gain scheduled H∞ controller is designed by dynamic output feedback. Robust synthesis of this gain scheduled H∞ control is carried out by an affine Parameter Dependent Lyapunov Function (PDLF). The problem of trajectory tracking control for the LPV plant is transformed into solving an infinite number of linear matrix inequalities by the PDLF design, and the linear matrix inequalities are solved by convex optimization techniques. To overcome model uncertainties due to linearization and external disturbances, a radial basis function neural network disturbance observer is proposed, and to estimate actuator faults, an LPV fault estimator is designed. Furthermore, a composite controller is proposed to realize fault tolerant trajectory tracking control, which combines the LPV control with the fault estimator and disturbance observer, as well as an active-set based control allocation to avoiding actuator saturation. The approach is tested by simulation of two scenarios that show responses of the altitude, speed and heading angle to (i) unknown disturbances and (ii) actuator faults. The results show that the proposed neural network observer based LPV control has better performances for both disturbance rejecting and fault-tolerant trajectory tracking. IEEE

关键词： Actuators

来源：评论

学校读者我要写书评

暂无评论

A DAOs-Based Publishing System for Advancing Open Access 4

A DAOs-Based Publishing System for Advancing Open Access

引用

4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Ma, Siji Li, Juanjuan Guan, Sangtian Ni, Qinghua Lin, Fei Zhang, Tengchao Huang, Jun Wang, Fei-Yue Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

The development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. This framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent 27

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

引用

27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Lin, Fei Tian, Yonglin Wang, Yunzhe Zhang, Tengchao Zhang, Xinyuan Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Capital University of Economics and Business Beijing100070 China University of Chinese Academy of Sciences Department of Artificial Intelligence Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

ISBN: (纸本)9798331505929

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Pager Explosion:Cybersecurity Insights and Afterthoughts

引用

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：