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Solo Adaptation for Distributed Synchronization With Application to Event-Triggered Linear Multi-Agent systems

IEEE Transactions on Automatic control 2025年

作者： Chen, Ci Zheng, Zhiyang Lewis, Frank L. Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Automation Guangzhou510006 China Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China The University of Texas at Arlington UTA Research Institute Fort Worth76118 United States Guangdong University of Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology School of Automation Guangzhou China Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education School of Automation Guangzhou510006 China

We study a solo adaptation-based protocol design problem for distributed synchronization of event-triggered linear multi-agent systems (MAS) over general directed graphs. We show that a solo adaptive gain, a locally static control gain matrix, and a distributed state measurement are sufficient for obtaining adaptive distributed synchronization of linear MAS over directed graphs without any prior knowledge of the global communication network. We then extend the solo adaptation structure to formulate an event-triggered synchronization control protocol. The main feature of our result is that the time for triggering a subsequent event is now computed using the distributed event-triggered state measurement, rather than the continuous-time state measurement. © 1963-2012 IEEE.

关键词： Continuous time systems

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷

作者： Wang, Jie Xu, Tingfa Song, Liqiang Ding, Lihe Li, Hui Jiang, Peng Han, Yuqi Li, Jianan School of Optics and Photonics Beijing Institute of Technology Beijing China Beijing Institute of Technology Beijing China Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China National Astronomical Observatories Chinese Academy of Sciences Chaoyang-qu China Beijing Key Laboratory of Embedded Real-time Information Processing Technique Beijing China

Fine-grained geometry, obtained through the assimilation of localized point features, is crucial in the realms of object recognition and scene comprehension within point cloud contexts. Traditional point cloud backbones predominantly utilize max pooling for the amalgamation of local features, a process that tends to overlook spatial interrelations among points, consequently leading to the potential loss of fine-grained geometric details. To overcome this limitation, we introduce an innovative operation termed Position Adaptive Pooling (PAPooling), which is designed to amalgamate local features while sensitively considering the spatial positions of points. This is achieved by employing a graph-based representation to explicitly model the spatial relationships of points. PAPooling involves two principal components: first, the local graph construction, which establishes a local graph for a set of points by linking a central point with its adjacent points, thereby transforming pairwise relative positions into channel-specific attention weights;second, the attentive feature aggregation, which adeptly takes into account the contribution of each node and simulates the inter-node relationships within the local graph, effectively extracting representations of local features through a Graph Convolution Network (GCN). PAPooling's simplicity and efficacy make it a versatile addition to widely used point-based backbones such as PointNet++ and DGCNN, offering a plug-and-play solution. Comprehensive experimental analysis demonstrates PAPooling's enhanced capability in capturing local geometry, contributing significantly across a spectrum of applications including 3D shape classification, part segmentation, scene segmentation, and corruption defense, all with minimal computational increase. Code will be public at https://***/Roywangj/PAPooling/. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Object recognition

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

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Patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network’s Structural controllability

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1152-1165页

作者： Xichen Wang Yugeng Xi Wenzhen Huang Shuai Jia the Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240

Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.

关键词： Algebraic criteria complete selection rule set structural controllability driver nodes

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Model Predictive control for Vapor Compression Cycle of Refrigeration Process

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International Journal of Automation and computing 2018年第6期15卷 707-715页

作者： Xiao-Hong Yin Shao-Yuan Li Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

A model predictive controller based on a novel structure selection criterion for the vapor compression cycle （VCC） of refrigeration process is proposed in this paper. Firstly, those system variables are analyzed which exert significant influences on the system performance. Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design. The controller based on multivariable model predictive control （MPC） strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem. The effectiveness of the proposed MPC controller is verified on the experimental rig.

关键词： Vapor compression refrigeration cycle model structure structure selection criterion model reduction model predictive control.

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Distributed Cooperative Coverage of Mobile Robots with Consensus-Based Connectivity Estimation

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Journal of Shanghai Jiaotong university(Science) 2014年第3期19卷 279-286页

作者：李晓丽赵曙光刘浩 School of Information Science and Technology Donghua University Key Laboratory of System Control and Information Processing of Ministry of Education

This paper deals with the discrete-time connected coverage problem with the constraint that only local information can be utilized for each robot. In such distributed framework, global connectivity characterized by the second smallest eigenvalue of topology Laplacian is estimated through introducing distributed minimal-time consensus algorithm and power iteration algorithm. A self-deployment algorithm is developed to disperse the robots with the precondition that the estimated second smallest eigenvalue is positive at each time-step. Since thus connectivity constraint does not impose to preserve some certain edges, the self-deployment strategy developed in this paper reserves a sufficient degree of freedom for the motion of robots. Theoretical analysis demonstrates that each pair of neighbor robots can finally reach the largest objective distance from each other while the group keeps connected all the time, which is also shown by simulations.

关键词： coverage distributed cooperative control minimal-time consensus eigenvalue estimation

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication system

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IEEE Wireless Communications Letters 2025年

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Short-Term Traffic Flow Forecasting Method and Its Applications

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 156-163页

作者：刘思妍李德伟席裕庚汤奇峰 Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiaotong University

Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast's precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network(HPSO-NN) with error compensation *** HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average(ARMA) model, can forecast traffic flow with a higher accuracy. What's more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.

关键词： short-term velocity forecast intelligent transportation systems(ITS) neural network(NN) hybrid particle swarm optimization-neura

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