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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

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A Tabletop Nano-CT Image Noise Reduction Network Based on 3-Dimensional Axial Attention Mechanism

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Computers, Materials & Continua 2024年第7期80卷 1711-1725页

作者： Huijuan Fu Linlin Zhu ChunhuiWang Xiaoqi Xi Yu Han Lei Li Yanmin Sun Bin Yan Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering UniversityZhengzhou450000China

Nano-computed tomography(Nano-CT)is an emerging,high-resolution imaging ***,due to their low-light properties,tabletop Nano-CT has to be scanned under long exposure conditions,which the scanning process is *** 3D reconstruction data,this paper proposed a lightweight 3D noise reduction method for desktop-level Nano-CT called AAD-ResNet(Axial Attention DeNoise ResNet).The network is framed by theU-net *** encoder and decoder are incorporated with the proposed 3D axial attention mechanism and residual dense *** layer of the residual dense block can directly access the features of the previous layer,which reduces the redundancy of parameters and improves the efficiency of network *** 3D axial attention mechanism enhances the correlation between 3D information in the training process and captures the long-distance *** can improve the noise reduction effect and avoid the loss of image structure *** results show that the network can effectively improve the image quality of a 0.1-s exposure scan to a level close to a 3-s exposure,significantly shortening the sample scanning time.

关键词： Deep learning tabletop Nano-CT image denoising 3D axial attention mechanism

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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Co-salient object detection with iterative purification and predictive optimization

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虚拟现实与智能硬件（中英文） 2024年第5期6卷 396-407页

作者： Yang WEN Yuhuan WANG Hao WANG Wuzhen SHI Wenming CAO Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related ***,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the *** issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of *** To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD ***,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing *** on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

关键词： Co-salient object detection Saliency detection Iterative method Predictive optimization

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A Portrait Segmentation Algorithm Combining MobileNeXt and Attention Mechanism 24

A Portrait Segmentation Algorithm Combining MobileNeXt and A...

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2024 International Conference on computing, Machine Learning and Data Science, CMLDS 2024

作者： Cheng, Yan Huang, Zou Wang, Guixi Lai, Jianming Xiong, Yuqi School of Software Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China School of Digital Industry Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China

ISBN: (纸本)9798400716393

Existing portrait segmentation methods are easily affected by the background. To address this challenge, we propose a simple convolution-based portrait segmentation algorithm to solve the problems of complex background, low accuracy and poor efficiency in real environments. Combined with multi-scale convolution, we design a multi-scale sandglass module structure (MultiScale-Sandglass-CA, MSC), which expands the depth and width of the network without increasing the number of parameters, and enhances the feature learning ability of the model for complex backgrounds;and the MSC module is used for feature extraction, and the introduction of coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the network's ability to learn the key information. The introduction of the coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the focusing of the network on the key information;at the same time, the introduction of the hybrid loss function, which helps to obtain clear portrait edges. Experiments are conducted on Matting and EG1800 public datasets, and the results show that the algorithm can significantly improve the mean intersection and merger ratio (mIoU) and pixel accuracy (Acc), etc. The mIoU can reach up to 97%, and the pixel accuracy can reach up to 10%. Among them, the mIoU index can reach 97.91% (Matting) and 94.72% (EG1800). Compared with the mIoU predicted by PortraitNet, it is 1.29% and 1.19% higher, respectively. In addition, experiments are conducted for the MSC structure proposed in this paper, which proves the superiority of the model in this paper. © 2024 ACM.

关键词： Convolution

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Video Group Activity Recognition Based On Multidimensional Spatio-Temporal Relationships 17

Video Group Activity Recognition Based On Multidimensional S...

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17th IEEE International Conference on Signal processing, ICSP 2024

作者： Li, Yanshan Zong, Hailin He, Suixuan Wei, Miaomiao Shenzhen University Institute of Intelligent Information Processing Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9798350387384

Spatio-temporal relationships play an important role in group activity recognition. The spatio-temporal relationships are complex and varied, containing relationships between individuals, between individuals and subgroups, and between subgroups. However, there is no unified method for measuring these multidimensional spatio-temporal relationships. Therefore, this paper proposes a set of metric methods for multidimensional spatio-temporal relationships based on geometric algebra, including the metric method based on positional features and the metric method based on appearance features. And this paper proposed a deep network for video group activity recognition based on multidimensional spatio-temporal relationships. The experimental results show that the proposed algorithm can effectively improve the accuracy of group activity recognition. © 2024 IEEE.

关键词： Algebra

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A method of embedding a high-resolution image into a large field-of-view image

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Multimedia Tools and Applications 2025年第14期84卷 13037-13056页

作者： Dong, Yanmei Pei, Mingtao Jia, Yunde Key Laboratory of Intelligent Information Processing and Graph Processing Tus College of Digit Guangxi University of Science and Technology Liuzhou545006 China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China

Images captured by wide-angle cameras or fisheye cameras are with large Field-of-View (FOV) but low resolution, while images captured by conventional cameras are with high-resolution but limited FOV. To handle this contradiction, in this paper, we propose to improve the resolution of a large FOV image by embedding a high-resolution image into it. We present an image embedding method based on non-rigid transformations and mesh optimization. Without any radial distortion correction on large FOV images, we utilize non-rigid transformations to model the mathematical transformation relationship between the high-resolution image and the large FOV image. Meanwhile, we exploit mesh optimization to generate evenly distributed control points for maintaining the shape and content of the large FOV image. To verify the effectiveness of the proposed method, we capture some image pairs from a conventional camera and a large FOV camera, and exploit several classic image alignment methods to implement four baseline methods. The experimental results show that our method preserves the shape and content of the original FOV image from 58% to 82%, which significantly outperforms all baseline methods. The results indicate that the proposed method can effectively embed a high-resolution image into a large FOV image as well as preserve the shape and content of the large FOV image. In other words, the resolution of a large FOV image can be improved via high-resolution image embedding. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Position Optimization for Mobile Relay Communications with Constrained Energy over a Whole Horizontal Plane

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China Communications 2023年第5期20卷 340-350页

作者： Haijun Tan Ning Xie Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

This paper concerns the position optimization problem of a mobile relay over a whole horizontal *** problem is important because the position of a mobile relay directly affects the end-to-end performance,e.g.,reliability,connectivity,and data *** this paper,we propose a new position optimization scheme of a mobile relay over a whole horizontal plane based on the one-bit feedback information from the destination node,which improves the performance over the prior scheme whose position of the mobile relay is optimized over a fixed *** the proposed scheme,the mobile relay is equipped with merely one single onboard ***,no prior information about the positions of both the source node and the destination node is ***,the proposed scheme can work at low network resources scenario,which is particularly suitable for mobile relay communication with constrained energy,e.g.,the communications in a disaster area where the infrastructure is heavily damaged,volcano monitoring,and wireless powered communication *** to the characteristics of the proposed scheme,we further design two heuristic implementations to calculate the optimal position of a mobile relay over a whole horizontal *** first implementation has better steady performance whereas the second implementation has better convergence *** implement the proposed scheme and conduct an extensive performance comparison between the proposed scheme and prior schemes to verify the advantages of the proposed scheme.

关键词： mobile relay position optimization one bit feedback information whole horizontal plane energy constrained

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PRPFD: Polynomial Resampling Particle Filter Based Deconvolution Approach for Resting-State BOLD-fMRI Data Analysis 24

PRPFD: Polynomial Resampling Particle Filter Based Deconvolu...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Luo, Yunying Zeng, Weiming Key Laboratory for Digital Imaging Processing and Intelligent Computing School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400717826

Functional Magnetic Resonance Imaging (fMRI) stands out in brain science research due to its non-invasive, non-intrusive, radiation-free, high spatial resolution, and precise localization advantages. However, the BOLD-fMRI signals it generates exhibit a several-second delay compared to neural activity, making it unable to reflect brain neural activity directly. This discrepancy may lead to differences from the actual situation. Therefore, this study aims to enhance the deconvolution of fMRI by refining existing methods to reconstruct the brain’s neural signals more faithfully. Building upon the foundation of particle filtering deconvolution, this paper introduced a novel approach to deconvolving resting-state fMRI data: the polynomial resampling particle filtering deconvolution (PRPFD). They adapted the particle distribution better to match the actual state through weight computation and resampling. We performed a simulation-based experiment to validate the feasibility of our approach. Besides, we validated the performance of our method using group data, focusing on both effective connectivity and functional connectivity, providing strong evidence for the usability and stability of our approach, and compared our method with other deconvolution techniques, demonstrating improvements in accuracy and computational efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Polynomials

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Audio-guided self-supervised learning for disentangled visual speech representations

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Frontiers of Computer Science 2024年第6期18卷 277-279页

作者： Dalu FENG Shuang YANG Shiguang SHAN Xilin CHEN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

1 *** visual speech representations from talking face videos is an important problem for several speech-related tasks,such as lip reading,talking face generation,and audiovisual speech separation[1,2].The key difficulty lies in tackling speech-irrelevant factors presented in the videos,such as lighting,resolution,viewpoints,and head motion.

关键词： such dis visual

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