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第43届中国控制会议

作者： Yan Lv Xiao Gong Ruohan Yang Jiayue Fang Shuo Miao Zhichao Feng Yue Yu Key laboratory of information system and technology Beijing institute of control and electronic technology School of Electronics and Information Northwestern Polytechnical University The Rocket Force University of Engineering

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper focuses on cooperative formation tracking problem of heterogeneous leader-following multi-UAVs with faulty sensors where the faulty sensors of the followers can only measure the partial output of the ***,the kinematic models of heterogeneous multi-UAVs based on the individual speed response time constants are ***,by utilizing the output regulation method,a formation controller based on a distributed observer is proposed based on partial outputs obtained by the faulty sensors of the *** should be noted that the proposed controller can achieve formation tracking of UAVs with faulty sensors to the leader ***,a simulation example is performed to check out the validation of the proposed methods.

关键词： Heterogenous UAVs cooperative formation control sensor faults output regulation

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Containment control Protocol Design for PDE Agents Subject to External Disturbances

Containment Control Protocol Design for PDE Agents Subject t...

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第42届中国控制会议

作者： Yining Chen School of Artificial Intelligence Tiangong University Key Laboratory of System Control and Information Processing Ministry of Education

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples.

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Dynamic Event-Triggered Consensus on Matrix-Weighted Networks: Leadless Case 42

Dynamic Event-Triggered Consensus on Matrix-Weighted Network...

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42nd Chinese control Conference, CCC 2023

作者： Dou, Lintao Wei, Haoyu Huang, Jian Pan, Lulu Shao, Haibin Li, Dewei College of Intelligent Science National University of Defense Technology Changsha410003 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581543

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Research on Boot Problem and Automatic Repair of an Embedded Real-time Operating system 2

Research on Boot Problem and Automatic Repair of an Embedded...

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2nd International Conference on Computer Technologies, ICCTech 2023

作者： Zhou, Haoshang Gao, Zhaoling Wang, Zhongfu Wang, Xiaopeng State Key Laboratory for Traction and Control System of EMU and Locomotive CRRC Dalian Electric Traction R&D Center Co. Ltd China School of Intelligence and Electronic Engineering Dalian Neusoft University of Information China

ISBN: (纸本)9781665455824

At present, in the communication between Electric Multiple Units (EMU) and locomotive network control system, in order to ensure the stability of vehicle control and the accuracy and reliability of data recording, its key components - Central control Unit (CCU) and Event Record Machine (ERM) adopt an embedded real-time operation system. In order to facilitate debugging, the system needs to start Microsoft's DOS system before being called to start. Based on the feedback of the embedded real-time operating system startup failure from each locomotive and rolling stock depot, this paper will elaborate on its startup process and propose a solution that can automatically repair the startup problem, which has been tested in the laboratory to prove that this method can effectively avoid the system startup failure. © 2023 IEEE.

关键词： Program debugging

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Causal Attention Transformer for Video Text Retrieval

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IET Image processing 2025年第1期19卷

作者： Lan, Hua Lv, Chaohui School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper. © 2025 The Author(s). IET Image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Interactive computer graphics

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Toward Enhancing Cyber-Physical system Security with system Unidentifiability

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IFAC-PapersOnLine 2023年第2期56卷 1692-1697页

作者： Xiangyu Mao Jianping He Chongrong Fang Yunfeng Peng The Dept. of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

A substantial amount of research has shown that most attacks on cyber-physical systems rely on knowledge of system models, where the models can be obtained using identification methods. Hence, achieving unidentifiability is an attractive way for the security of systems. This paper focuses on the unidentifiability of systems and designs a controller against the attacks relying on identification. The key insight is that we propose a quantitative evaluation to measure the unidentifiability of the system. This evaluation can be taken as a criterion for evaluating the security of the system. Different from the existing research, the evaluation gives the exact number of unidentifiable parameters in the system. Moreover, we give the necessary and sufficient condition for each parameter to be unidentifiable. Finally, we design a controller based on this evaluation to maximize the number of unidentifiable parameters. Simulation demonstrates the effectiveness of our algorithm.

关键词： system Identification Security and Unidentifiability Fisher information

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Spatial Temporal Block Transformer Network for Skeleton-Based Action Recognition

Spatial Temporal Block Transformer Network for Skeleton-Base...

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Fan Li, Dewei Wang, Gang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9781665465335

Recently, skeleton-based action recognition has be-come very popular in areas such as autonomous driving and environmental awareness due to its small data size and high ac-curacy. Graph convolutional networks (GCN) can model skeleton data well by adapting to the graph structure of the skeleton. However, GCN lacks the ability to extract global spatial and temporal dependencies. Moreover, an action is not only formed by one single skeleton point or frame but several skeleton points and frames. In this paper, a novel spatial-temporal block transformer network based on the self-attention mechanism is proposed, which efficiently models global spatial-temporal dependencies. The model uses spatial-temporal blocks as input, and spatial-temporal feature aggregation modules are applied to enhance the information exchange between blocks. Extensive experiments on the two skeleton-based action recognition datasets show the remarkable performance of our model, which outperforms most GCN-based methods and all transformer-based methods. © 2022 IEEE.

关键词： Musculoskeletal system

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Research on the logic monitoring method for the cloud computing based safety computer

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Transportation Safety and Environment 2023年第2期5卷 79-87页

作者： Yaran Yang Lianchuan Ma Tao Tang Huan Ding Zhen Liu School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China CRSC Research&Design Institute Group Co.Ltd. Beijing 100070China

With the development of railway construction in China,the computing demand of the train control system is increasing day by *** application of cloud computing technology on the rail transit signal system has become a research hotspot in recent *** to improve the safety and availability of the safety computer platform in the cloud computing environment is the key problem when applying cloud computing to the train operation control *** the cloud platform is in an open network environment,fac-ing many security vulnerabilities and malicious network attacks,it is necessary to monitor the operation of computer programmes through edge safety ***,this paper encrypts the logical monitoring method,and then proposes a secure computer de fence model based on the dynamic heterogeneous redundancy *** the continuous time Markov chain(CTMC)is used to quantitatively solve the stable probability of the system,and the influence of different logical monitoring methods on the anti-attack performance of the system is ***,the experiment proves that the dynamic heterogeneous redundancy structure composed of encryption logic monitoring can guarantee the safe and stable operation of the safety computer more effectively.

关键词： safety computer logical monitoring method continuous time Markov modelling(CTMC) cloud computing anti-aggression analysis

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Online-Learning-Enabled Distributionally Robust Motion control via Uncertainty Propagation and Ambiguity Set Compression

Online-Learning-Enabled Distributionally Robust Motion Contr...

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IEEE Conference on Decision and control

作者： Han Wang Chao Ning Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We develop a safe, efficient, and flexible motion control framework for collision-free navigation of robots in uncertain and dynamic environments. With the gathered real-time data at each control time, the motion distribution of obstacles is estimated from data streams by leveraging the Dirichlet process mixture model. We propose a novel uncertainty propagation method, which theoretically admits the derivation of obstacle position distributions over the entire prediction horizon only based on the current position and motion distribution of obstacles. To enhance the robustness against estimated distribution errors, an ambiguity set utilizing local-moment information of position distributions is constructed. Additionally, we introduce a compression scheme to automatically adjust the complexity (granularity) of the ambiguity set by aggregating distributions that are close in Wasserstein space, thereby flexibly striking a trade-off between control performance and computation time. The distributionally robust collision avoidance constraint based on the compressed ambiguity set is reformulated into separating hyperplanes, which are applied in the model predictive controller to obtain the optimal collision-free trajectory. To resolve the infinite-dimensionality issue inherent in the computation of the separating hyperplanes, we equivalently derive a tractable semi-definite programming formulation exploiting distributionally robust optimization techniques. The efficacy of the proposed framework is demonstrated through simulation.

关键词： Uncertainty Simulation Programming Robustness Real-time systems Trajectory Safety Collision avoidance Robots Streams

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