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第25届中国控制与决策会议

作者： Kun Hu Lei Liu Yongji Wang Zhishen Wang Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Department of Control Science & Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467355322

The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.

关键词： Terminal Guidance, Nonlinear Regulator, State-dependent Riccati Equation

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Event-triggered Consensus Of Nonlinear Multi-Agent Systems With Nonlinear Interconnections

Event-triggered Consensus Of Nonlinear Multi-Agent Systems W...

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第三十二届中国控制会议

作者： YIN Xiuxia YUE Dong Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

ISBN: (纸本)9781479900305

This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.

关键词： Consensus, Multi-Agent System (MAS), Nonlinear Interconnection, Event-Triggered Control, LMI.

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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Underwater Perception and Autonomous Operation for Underwater Vehicle-Manipulator Systems: Methodology and Experiment

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IEEE/ASME Transactions on Mechatronics 2025年

作者： Jiang, Chaicheng Xiong, Xinyang Xiang, Xianbo Zeng, Zhigang Zhang, Fumin Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan 430074 China Huazhong University of Science and Technology School of Naval Architecture and Ocean Engineering Wuhan 430074 China Huazhong University of Science and Technology School of Naval Architecture and Ocean Engineering School of Artificial Intelligence and Automation State Key Lab of Intelligent Manufacturing Equipment and Technology Wuhan 430074 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The Education Ministry of China Wuhan 430074 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong Hong Kong

Camera-equipped underwater vehicle-manipulator systems (UVMS) can achieve autonomous underwater operations through visual servoing. However, current methods excessively rely on auxiliary means for target's visual information and do not sufficiently consider the target's pose when designing visual servoing schemes. As a result, UVMS can only perform dynamic positioning or grasp simple-shaped targets like sea urchins. In this article, we are devoted to making the UVMS autonomously complete more complex underwater operations, such as valve switching and pipe handling. First, an underwater target perception system is proposed to address the issue of discontinuous target position estimation caused by various interferences. Second, a deep neural network-based grasping mechanism controller is proposed to enable the manipulator to grasp targets in various initial poses using the target image. Third, a decoupled visual servoing scheme is proposed for the motion control of the UVMS, and in collaboration with a designed task supervisor to ensure that the UVMS can complete the underwater operation autonomously. To validate the effectiveness of the proposed methods, underwater experiments are conducted based on a UVMS prototype. The UVMS succeeds to autonomously switch different types of valves and handle the pipe. Experiments conducted under dynamic currents validated the reliability and robustness of the proposed methods. © 1996-2012 IEEE.

关键词： Autonomous underwater operations deep neural network underwater vehicle-manipulator systems (UVMS) visual perception visual servoing

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Consensus in second-order multi-agent systems via impulsive control using position-only information with heterogeneous delays

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IET Control Theory and Applications 2015年第3期9卷 336-345页

作者： Wang, Yan-Wu Yi, Jing-Wen School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science AndTechnology Ministry of China China

This study investigates the consensus problem of second-order multi-agent systems (MASs) via impulsive control using position-only information with communication delays. The communication delays between any two distinct agents are different which can be larger than one impulsive period. A distributed impulsive consensus protocol is designed, in which only the delayed sampled relative positions to neighbours and the relative position to the last sampling state are utilised. By introducing some virtual subgraphs and performing three steps of model transformation, the consensus problem of original continuous-time system is converted to the stability problem of a discrete-time expanded error system. Some necessary and sufficient criteria are derived to grantee the dynamic average-consensus of the MAS. Furthermore, a special case, that is, the delays less than one impulsive period, is discussed, and the bounds of impulsive periods for the dynamic average-consensus are obtained in explicit expressions for both the undirected and directed communication graphs, respectively. Two numerical simulation examples are given to illustrate the effectiveness of theoretical results. © 2015 The Institution of Engineering and Technology.

关键词： Multi agent systems

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Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

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Computers, Materials & Continua 2024年第10期81卷 1391-1407页

作者： Ke Li Xiaofeng Wang Hu Wang College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

In the realm of data privacy protection,federated learning aims to collaboratively train a global ***,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global *** shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized ***,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model *** tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local ***,FedFCD employs a hierarchical optimization strategy to flexibly optimize model *** experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of *** instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning ***,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

关键词： Federated learning data heterogeneity feature alignment decision fusion hierarchical optimization

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Second-Order Controllability of Multi-Agent Systems with Multiple Leaders

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Communications in Theoretical Physics 2016年第5期65卷 585-592页

作者：刘波史运涛苏厚胜韩潇 College of Science North China University of Technology Key Laboratory of Beijing for Field-Bus Technology & Automation North China University of Technology School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaHuazhong University of Science and Technology

This paper proposes a new second-order continuous-time multi-agent model and analyzes the controllability of second-order multi-agent system with multiple leaders based on the asymmetric *** paper considers the more general case:velocity coupling topology is different from location coupling *** sufficient and necessary conditions are presented for the controllability of the system with multiple *** addition,the paper studies the controllability of the system with velocity damping *** results are given to illustrate the correctness of theoretical results.

关键词： controllability second-order multi-agent systems multiple leaders asymmetric topology

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Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Science in China(Series F) 2008年第8期51卷 1064-1082页

作者： GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

关键词： multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms

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A multiobjective immune clustering ensemble technique applied to unsupervised SAR image segmentation 10

A multiobjective immune clustering ensemble technique applie...

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ACM International Conference on image and Video Retrieval, ACM-CIVR 2010

作者： Liu, Ruochen Zhang, Wei Jiao, Licheng Liu, Fang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781450301176

In the past few years, multiobjective clustering has been one of the most successful techniques in the field of computer vision and data clustering. This paper proposes a novel unsupervised approach for synthetic aperture radar (SAR) image segmentation, namely, multiobjective immune clustering ensemble technique (MICET). The new technique first divides the image into several regions, and a certain number of pixels are picked out from these regions to form the clustering dataset. Second, artificial immune system (AIS) and multiobjective optimization (MOO) are introduced to generate multiple clustering results, which are then combined together for the following ensemble process. Multiple runs of the multiob-jective clustering method with different randomly selected image features are performed to ensure high quality components as well as necessary diversity for an efficient ensemble. Finally, each datum is assigned to one cluster according to the relationship with the clustering dataset. Experimental results show that interesting segmentation performances on SAR images can be achieved by the proposed technique despite its completely unsupervised nature. Copyright © 2010 ACM.

关键词： Multiobjective optimization

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A change detection algorithm based on object feature for SAR image

A change detection algorithm based on object feature for SAR...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhang, Xiaohua Li, Hongfeng Jiao, Licheng Institution of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In the detection of change objects of SAR images, regard to the images containing high intensity noise, traditional methods will produce a considerable false alarm. This paper presents a new method of change detection based on directional information. Direction measure is proposed to suppress noises and brighten object contours. Then region blocks which contain changing objects are extracted according to the difference map of direction measure. Finally, changed objects can be obtained using the OTSU threshold method for each region block. The experimental results on SAR images demonstrate the efficiency and accuracy of the proposed method. ©2009 IEEE.

关键词： Synthetic aperture radar

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