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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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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Dynamic Color Transform Networks for Wheat Head Detection

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Plant Phenomics 2022年第1期4卷 395-408页

作者： Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of EducationSchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina

Wheat head detection can measure wheat traits such as head density and head *** wheat breeding largely relies on manual observation to detect wheat heads,yielding a tedious and inefficient *** emergence of affordable camera platforms provides opportunities for deploying computer vision(CV)algorithms in wheat head detection,enabling automated measurements of wheat *** wheat head detection,however,is challenging due to the variability of observation circumstances and the uncertainty of wheat head *** this work,we propose a simple but effective idea—dynamic color transform(DCT)—for accurate wheat head detection.

关键词： Wheat breeding wheat

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

arXiv

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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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Res-Net-Based Modeling and Morphologic Analysis of Deep Medullary Veins Using Multi-Echo GRE at 7 T MRI

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NMR in Biomedicine 2025年第6期38卷 e70042页

作者： Li, Zhixin Liang, Li Zhang, Jinyuan Fan, Xueyi Yang, Yishuang Yang, Hua Wang, Qianyao An, Jing Xue, Rong Zhuo, Yan Qian, Hairong Zhang, Zihao State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences Beijing China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Chinese Academy of Sciences Beijing China Navy Clinical College The Fifth School of Clinical Medicine Anhui Medical University Anhui Hefei China School of Medicine South China University of Technology Guangdong Guangzhou China Siemens Shenzhen Magnetic Resonance Ltd. Shenzhen China Beijing Institute of Brain Disorders Capital Medical University Beijing China Department of Neurology The Sixth Medical Center of PLA General Hospital Beijing China

The pathological changes in deep medullary veins (DMVs) have been reported in various diseases. However, accurate modeling and quantification of DMVs remain challenging. We aim to propose and assess an automated approach for modeling and quantifying DMVs at 7 Tesla (7 T) MRI. A multi-echo-input Res-Net was developed for vascular segmentation, and a minimum path loss function was used for modeling and quantifying the geometric parameter of DMVs. Twenty-one patients diagnosed as subcortical vascular dementia (SVaD) and 20 condition matched controls were included in this study. The amplitude and phase images of gradient echo with five echoes were acquired at 7 T. Ten GRE images were manually labeled by two neurologists and compared with the results obtained by our proposed method. Independent samples t test and Pearson correlation were used for statistical analysis in our study, and p value © 2025 John Wiley & Sons Ltd.

关键词： Neurodegenerative diseases

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A novel method of constructing high-dimensional digital chaotic systems on finite-state automata

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Chinese Physics B 2020年第9期29卷 233-243页

作者： Jun Zheng Han-Ping Hu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.

关键词： high-dimensional digital chaotic system dynamical degradation anti-control pseudorandom number generator

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Highly dynamic and sensitive NEMOer calcium indicators for imaging ER calcium signals in excitable cells

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Nature Communications 2025年第1期16卷 1-12页

作者： Gu, Wenjia Chen, Jia-Hui Zhang, Yiyin Wang, Zhirong Li, Jia Wang, Sijia Zhang, Hanhan Jiang, Amin Zhong, Ziyi Zhang, Jiaxuan Xu, Ze Liu, Panpan Xi, Chao Hou, Tingting Gill, Donald L. Li, Dong Mu, Yu Wang, Shi-Qiang Tang, Ai-Hui Wang, Youjun Beijing Key Laboratory of Gene Resource and Molecular Development College of Life Sciences Beijing Normal University Beijing China Department of Anatomy School of Basic Medicine Anhui Medical University Anhui Hefei China State Key Laboratory of Membrane Biology College of Life Sciences Peking University Beijing China Institute of Neuroscience State Key Laboratory of Neuroscience Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China Department of Cellular and Molecular Physiology Pennsylvania State University College of Medicine Hershey PA United States Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Hefei National Research Center for Physical Sciences at the Microscale Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Key Laboratory of Cell Proliferation and Regulation Biology Ministry of Education College of Life Sciences Beijing Normal University Beijing China

The Endoplasmic/sarcoplasmic reticulum (ER/SR) is central to calcium (Ca2+) signaling, yet current genetically encoded Ca2+ indicators (GECIs) cannot detect elementary Ca2+ release events from ER/SR, particularly in m...

The Endoplasmic/sarcoplasmic reticulum (ER/SR) is central to calcium (Ca²⁺) signaling, yet current genetically encoded Ca²⁺ indicators (GECIs) cannot detect elementary Ca²⁺ release events from ER/SR, particularly in muscle cells. Here, we report NEMOer, a set of organellar GECIs, to efficiently capture ER Ca²⁺ dynamics with increased sensitivity and responsiveness. NEMOer indicators exhibit dynamic ranges an order of magnitude larger than G-CEPIA1er, enabling 2.7-fold more sensitive detection of Ca²⁺ transients in both non-excitable and excitable cells. The ratiometric version further allows super-resolution monitoring of local ER Ca²⁺ homeostasis and dynamics. Notably, NEMOer-f enabled the inaugural detection of Ca²⁺ blinks, elementary Ca²⁺ releasing signals from the SR of cardiomyocytes, as well as in vivo spontaneous SR Ca²⁺ releases in zebrafish. In summary, the highly dynamic NEMOer sensors expand the repertoire of organellar Ca²⁺ sensors that allow real-time monitoring of intricate Ca²⁺ dynamics and homeostasis in live cells with high spatiotemporal resolution. © The Author(s) 2025.

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A novel linear search-linear programming method for non-convex multi-objective sizing of standalone microgrids

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Energy Conversion and Management 2025年 337卷

作者： Zishan Yin Jiashu Jin Linghong Zeng Xiyun Tang Zhuo Wang Xi Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 Hubei PR China Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 Hubei PR China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan 430074 Hubei PR China

Both the increased utilization of photovoltaic (PV) and the consequent expansion of energy storage, leading to higher costs, are critical factors in the development of a techno-economic microgrid. To address this trade-off, optimal sizing design of a multi-energy microgrid is essential. This paper proposes a novel linear search-linear programming method for optimizing the sizing of microgrids with diverse energy storage systems, including battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). The method minimizes both the non-linear and non-convex life cycle cost and the PV curtailment rate. Simulation results demonstrate the feasibility and superiority of the proposed method, achieving enhanced computational efficiency: 52 times faster for PV/HESS microgrids, 124 times for PV/BESS, and 5 times for PV/HESS/BESS. Moreover, more comprehensive and accurate optimal configurations were identified, including 4289 PV units for both the PV/BESS and PV/HESS/BESS microgrids, and 6239 PV units along with 37 fuel cell units for the PV/HESS microgrid. Based on the advanced optimization outcomes, a techno-economic analysis shows that the PV/HESS/BESS microgrid optimally combines the advantages of both HESS and BESS, substantially improving overall system performance.

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Short-Term Residential Load Forecasting via Pooling-Ensemble Model with Smoothing Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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Evolutionary attraction–repulsion algorithm embedded with LLM for UAV task allocation

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Advanced Engineering Informatics 2025年 66卷

作者： Bowen Wu Renbin Xiao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan 430074 China

Labor division provides an adaptive and scalable technique for unmanned systems. However, its designing process usually relies heavily on the manually crafted paradigms, leading to inefficiencies and insufficient performance. To overcome these challenges, this paper introduce an evolutionary approach to labor division algorithm design. Drawing inspiration from the regulatory behaviors observed in natural lion populations, we propose a bipolar feedback-guided attraction–repulsion mechanism. A formal model is developed to encapsulate this mechanism, and we provide an in-depth analysis of the algorithm’s evolution through the framework of systems science. In addition, we leverage prompt engineering techniques to integrate a large language model (LLM) with our proposed mechanism, facilitating a bidirectional enhancement between the two. This integration culminates in the development of the ARE-LLM framework, a robust and adaptive solution for designing labor division algorithms. To demonstrate the practical applicability of our approach, we apply the ARE-LLM framework to the UAV-based task allocation problem for enemy air defense suppression. The experimental results indicate that our method outperforms several advanced approaches. In a scenario involving 50 UAVs tasked with completing 300 assignments, the total distance traveled is only 9 % of that required by the ALPDA algorithm, while the processing time is reduced to 8 % of that used by ALPDA.

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