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Flocking in Multi-Agent Systems with Multiple Virtual Leaders Based Only on Position Measurements

Communications in Theoretical Physics 2012年第5期57卷 801-807页

作者：苏厚胜 Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and Technology

Most existing flocking algorithms assume one single virtual leader and rely on information on both relative positions and relative velocities among neighboring *** this paper,the problem of controlling a flock of mobile autonomous agents to follow multiple virtual leaders is investigated by using only position information in the sense that agents with the same virtual leader asymptotically attain the same velocity and track the corresponding virtual leader based on only position measurements.A flocking algorithm is proposed under which every agent asymptotically attains its desired velocity,collision between agents can be avoided,and the final tight formation minimizes all agents' global potentials.A simulation example is presented to verify and illustrate the theoretical results.

关键词： distributed control flocking multi-agent system multiple virtual leaders

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Uniform Solution to QSAT by P Systems with Proteins

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电子学报(英文版) 2012年第4期21卷 667-672页

作者： LU Chun SHI Xiaolong Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

P systems are distributed parallel computing models in the area of membrane computing, which are inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. P systems with proteins on membranes are a class of P systems, which have proved to be very efficient computing devices. Specifically, it was known that the Quantified satisfiability problem (QSAT) of a Boolean formula can be solved by a semi-uniform family of P systems with proteins on membranes and with membrane division. However, it remains open whether a uniform families of P systems with proteins on membranes can solve in polynomial time exactly the class of problems PSPACE. In this work, we present a uniform solution to QSAT problem by P systems with proteins on membranes in a linear time with respect to both the number n of Boolean variables and the number m of clauses of the instance, which answers the above open problem.

关键词：蛋白质膜 P系统分布式并行计算多项式时间 PSPACE 高级结构细胞膜计算模型 Membrane computing, Membrane protein, PSPACE-complete problem, Quantified satisflability problem (QSAT problem), Uniform solution.

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Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection

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Journal of Information and Computational Science 2011年第4期8卷 662-670页

作者： Han, Hong Fan, Youjian Jiao, L.C. Chen, Zhichao 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

An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of each sub-band coefficients, and then the texture feature can be extracted from the co-occurrence matrixes of the lowest-frequency sub-band coefficients, all the extracted features are concatenated as the final feature vector of the images. All the training and test data are from the INRIA and MIT human dataset. The classification results with test data show that the proposed feature extraction method is suitable for human detection. From the detection results, it can be seen that, the detection accuracy of the proposed method is higher, meanwhile the false positive rate is lower than the Histograms of Oriented Gradients (HOG) method. Copyright © 2011 Binary Information Press.

关键词： Feature extraction

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Taming Consistency Distillation for Accelerated Human image Animation

arXiv

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arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Yuan, Hangjie Wei, Yujie Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Fudan University China

Recent advancements in human image animation have been propelled by video diffusion models, yet their reliance on numerous iterative denoising steps results in high inference costs and slow speeds. An intuitive solution involves adopting consistency models, which serve as an effective acceleration paradigm through consistency distillation. However, simply employing this strategy in human image animation often leads to quality decline, including visual blurring, motion degradation, and facial distortion, particularly in dynamic regions. In this paper, we propose the DanceLCM approach complemented by several enhancements to improve visual quality and motion continuity at low-step regime: (1) segmented consistency distillation with an auxiliary light-weight head to incorporate supervision from real video latents, mitigating cumulative errors resulting from single full-trajectory generation;(2) a motion-focused loss to centre on motion regions, and explicit injection of facial fidelity features to improve face authenticity. Extensive qualitative and quantitative experiments demonstrate that DanceLCM achieves results comparable to state-of-the-art video diffusion models with a mere 2-4 inference steps, significantly reducing the inference burden without compromising video quality. The code and models will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

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Immunodominance and clonal selection inspired multiobjective clustering

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Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of intelligent Perception and image Understanding of the Ministry of Education of China, Institute of intelligent Information processing, Xidian University, Xi’an 710071, China [a]Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Changbing Pan, Linchao Chen, Jie Liu, Yang Lai, Jingang The College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China The School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China The Hangzhou School of Automation Zhejiang Normal University Hangzhou311231 China The College of Electronic Engineering National University of Defense Technology Hefei230037 China The School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Education Ministry of China China Huazhong University of Science and Technology Wuhan430074 China

To execute a variety of collaborative tasks, the cooperation for unmanned aerial vehicles (UAVs) with complicated interactions under dynamic environments is a challenging and critical issue. This paper studies the cooperation issue for UAVs under dynamic environments through an approach of game theory-reinforcement learning (GT-RL) approach, which combines the advantages of both game theory and reinforcement learning. First, to cope with the complicated interactions of UAVs, the cluster of UAVs is modeled as a public goods game with asymmetrical environmental feedback. Then, reinforcement learning is adopted to optimize decision-making of UAVs under unknown and dynamic environments through comparing the dynamic behaviors of UAVs, where a novel dynamics system offers a more comprehensive understanding on cooperative behavior among UAVs. Finally, the simulation results show that the GT-RL approach can effectively promote cooperation among UAVs in completing the collaborative tasks. © 1967-2012 IEEE.

关键词： Adversarial machine learning

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng 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 [a]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

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

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Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling

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中国物理快报（英文版） 2009年第6期26卷 59-62页

作者： CHEN Jian-Rui JIAO Li-Cheng WU Jian-She WANG Xiao-Hua Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information ProcessingXidian UniversityXi'an 710071 College of Science Inner Mongolia University of TechnologyInner Mongolia 01 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information ProcessingXidian UniversityXi'an 710071

A new general network model for two complex networks with time-varying delay coupling is *** we investigate its synchronization *** two complex networks of the model differ in dynamic nodes,the number of nodes and the coupling *** using adaptive controllers,a synchronization criterion is *** examples are given to demonstrate the effectiveness of the obtained synchronization *** study may widen the application range of synchronization,such as in chaotic secure communication.

关键词： No English keywords

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UniAnimate-DiT: Human image Animation with Large-Scale Video Diffusion Transformer

arXiv

引用

arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Tang, Longxiang Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology United States Alibaba Group China Tsinghua University China

This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the robust generative capabilities of the original Wan2.1 model, we implement Low-Rank Adaptation (LoRA) technique to fine-tune a minimal set of parameters, significantly reducing training memory overhead. A lightweight pose encoder consisting of multiple stacked 3D convolutional layers is designed to encode motion information of driving poses. Furthermore, we adopt a simple concatenation operation to integrate the reference appearance into the model and incorporate the pose information of the reference image for enhanced pose alignment. Experimental results show that our approach achieves visually appearing and temporally consistent high-fidelity animations. Trained on 480p (832x480) videos, UniAnimate-DiT demonstrates strong generalization capabilities to seamlessly upscale to 720P (1280x720) during inference. The training and inference code is publicly available at https://github. com/ali-vilab/UniAnimate-DiT. Copyright © 2025, The Authors. All rights reserved.

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