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Distributed Identification With Input Design for Network systems

IEEE Transactions on control of Network systems 2025年

作者： Mao, Xiangyu He, Jianping Fang, Chongrong Shanghai Jiao Tong University The Dept. of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

The distributed identification of network systems under local observation has recently become one of the research hot spots. Due to the unobservable influence of other subsystems and the noise, it is difficult for local subsystems to achieve convergent identification under local observations. This paper proposes a distributed identification algorithm for local subsystems to identify their parameter matrices under local observation with a guarantee of convergence. We obtain the necessary and sufficient conditions for the convergence of local identification. We prove that by injecting an independent random signal to the local subsystem, which is easy to implement, using a simple least-squares method to identify local systems can achieve convergent identification for a certain probability. Meanwhile, we give the minimal level of variance of the injected signal that can still guarantee the convergence of the identification. Next, we add the designed input as a vector of Gaussian noises with decreasing variance to a known controller, proving that it can converge to the original controller. Simulations demonstrate the effectiveness of the proposed algorithms. © 2025 IEEE.

关键词： Least squares approximations

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3D measurement of scattering media based on self-supervised fringe domain transformation

3D measurement of scattering media based on self-supervised ...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Tan, Ji Niu, Haipeng Luo, Suofan Liu, Jia He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration Ministry of Education Guangzhou510006 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

The scattering media measurement for structured light techniques remains challenging due to the subsurface scattering that destroys fringe patterns and introduces phase error. Recently, deep learning has been successfully applied to fringe analysis. Unfortunately, collecting a large scale of labeled data is difficult, especially in scattering media measurement scenarios. Inspired by image style transfer, we proposed a high-precision scattering medium measurement method based on self-supervised fringe domain transformation. Specifically, by treating degraded fringe and ideal fringe as two pattern styles, a cycle generation strategy is developed to achieve fringe enhancement by translating between two fringe domains. Two generators and two discriminators are used to form cycle-consistency constraints for self-supervised learning with unpaired dataset. Additionally, we combine numerical and physical constraints to jointly optimize network parameters, effectively suppressing phase-shift errors and improving phase quality. Since the proposed approach eliminates the need for label data, it exhibits great potential for applying deep learning to scattering medium measurement. Experiments verify that our method can obtain high-quality 3D morphology of translucent objects. © 2025 SPIE.

关键词： Velocity measurement

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Extended State Observer-based Hybrid Dynamic Event-Triggered Consensus control with External Disturbances 24

Extended State Observer-based Hybrid Dynamic Event-Triggered...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Pei, Wenliang Hua, Changchun Cui, Hailong Zhao, Guanglei Ma, Zhuang The Institute of Electrical Engineering The Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Hebei China Tangshan University Hebei China

ISBN: (纸本)9798400710681

This paper solves the event-triggered robust consensus problem of disturbed multi-agent systems (MASs) in fully distributed fashion. First, a distributed extended state observer (DESO) is developed for appropriately estimating system states and external disturbances. Then, by utilising these estimates, a fully distributed event-triggering control protocol, that composes of an adaptive distributed controller to avoid using global information while maintaining desired consensus property, and a novel hybrid dynamic event-triggering mechanism (ETM) to save communication resources, is put forward for the MASs. It is notable that an auxiliary differential equation is incorporated in the triggering condition, with which, positive minimum interevent times (MIETs) are available to ease the hardware implementation, while being with adaptive coefficients, the proposed hybrid dynamic ETM can be independent of any global information. Finally, the obtained main results are testified via a numerical example. © 2024 Copyright held by the owner/author(s).

关键词： State estimation

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AMA-Net: Adaptive Masking Attention Network for Agricultural Crop Classification From UAV Images

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

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Multi-stage image deblurring method based on deep convolutional neural networks

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Journal of Electronic Imaging 2025年第2期34卷

作者： Zhao, Liquan Lin, Yongpei Jia, Yanfei Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Jilin China Beihua University College of Electrical and Information Engineering Jilin China

Due to factors such as relative motion between the imaging device and the target object or out-of-focus optical system, some images may become blurred, severely affecting subsequent image processing tasks. We propose an image deblurring method based on deep convolutional neural networks to recover clear images from blurred ones. The network consists of three structurally similar sub-networks. Each sub-network comprises the multi-scale feature extraction module, single-scale feature extraction module, fast encoding-decoding module, encoding-decoding feature fusion module, and fusion strategy we proposed. The network can extract multi-scale features from input images of different scales, fusing coarse and fine-scale features and ultimately enhancing the clarity of the blurred images. In addition, we introduce the content loss function and frequency reconstruction loss function into the conventional loss function to better measure the quality of deblurred images. Experiments were conducted on the GoPro and HIDE datasets. The HIDE dataset is divided into distant and close-up scenes, allowing for a better evaluation of the generalization ability of deblurring methods in different scenarios. Compared with the existing methods, the experimental results demonstrate that the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index measure values, resulting in clearer and more natural deblurred images. Furthermore, the proposed algorithm exhibits faster image processing speed and smaller model size. The model performs better in terms of real-time capability. © 2025 SPIE and IS&T.

关键词： image deblurring image processing multi-stage neural network

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Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

arXiv

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arXiv 2025年

作者： Wang, Weizhen He, Jianping Duan, Xiaoming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Policy gradient methods are one of the most successful methods for solving challenging reinforcement learning problems. However, despite their empirical successes, many SOTA policy gradient algorithms for discounted problems deviate from the theoretical policy gradient theorem due to the existence of a distribution mismatch. In this work, we analyze the impact of this mismatch on the policy gradient methods. Specifically, we first show that in the case of tabular parameterizations, the methods under the mismatch remain globally optimal. Then, we extend this analysis to more general parameterizations by leveraging the theory of biased stochastic gradient descent. Our findings offer new insights into the robustness of policy gradient methods as well as the gap between theoretical foundations and practical implementations. © 2025, CC BY.

关键词：

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A Synthetic Inertia control Scheme for Inverter Utilizing Its DC Side Capacitor 2nd

A Synthetic Inertia Control Scheme for Inverter Utilizing It...

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2nd International Conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

作者： Zhang, Mingmin Luo, Ziyi Chen, Wanxing Yu, Jiaqi Guo, Wenming Li, Yong Hunan Province University Key Laboratory of Energy Storage Power System Cyber-Physical Control Changsha University Changsha China College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9789819624553

Insufficient inertia has retrained the progress toward a one hundred percent inverter-based power generation. To solve this issue, a promising way is to exploit synthetic inertia based on the power electronic inverters. Taking advantage of energy stored in the DC side capacitor, this paper proposes a synthetic inertia control scheme for inverters without a DC side battery. The basic mechanism of the synthetic inertia control is derived by comparing the working principle between a synchronous generator and a three-phase inverter. Thereafter, the control algorithm of the inverter is designed by three parts, including synthetic inertia control loop, reactive power control loop and line oscillation damping control loop. Finally, simulation results based on a simplified microgrid are presented to verify the self-grid synchronization and grid-supportive characteristics of the inverter. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Synchronous generators

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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Cross-Modal Ship Grounding: Towards Large Model for Enhanced Few-Shot Learning 27th

Cross-Modal Ship Grounding: Towards Large Model for Enhanced...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Quan Chen, Li Feng, Zhida Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781124

A growing body of research indicates that employing large models for adaptation to downstream tasks often yields remarkable performance. However, in the domain of ship detection, the potential of these large models is frequently underutilized due to domain shift issues. This paper introduces the Cross-Modal Ship Grounding (CSG) model, which leverages an efficient Cross-Modal Adapter (CMA) technology to transfer the general detection capabilities of large models to ship images, addressing domain shift with minimal training costs. To mitigate the challenges posed by complex and variable background interference, the Water-Land Separation (WLS) module is proposed to focus specifically on the water area. This module effectively addresses the issue of background target interference, thereby enhancing the model’s accuracy in complex scenes. Empirical evaluations on both private and public datasets demonstrate that the CSG model surpasses all state-of-the-art models in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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