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Adaptive Fuzzy Secure Containment control for Fully Heterogeneous Nonlinear Systems Under Switching Topologies and DoS Attacks

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1691-1697页

作者： Jiang, Dongxue Wen, Guoguang Ifqir, Sara Rahmani, Ahmed Sueur, Christophe Huang, Tingwen Centrale Lille Institute CRIStAL UMR CNRS 9189 Villeneuve d'Ascq59650 France Beijing Jiaotong University School of Mathematics and Statistics Beijing100044 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This article proposes a novel fuzzy secure containment control strategy in an adaptive framework for fully heterogeneous nonlinear multiagent systems (FHNMASs), which can resist the combined impacts of randomly switching topologies and denial-of-service (DoS) attacks. Unlike existing containment control protocols that rely on multiple leaders sharing the same system matrices, our approach is applicable to more general systems, where multiple leaders and followers are allowed to be equipped with nonidentical system matrices and even state dimensions. The switching signal of communication graphs is regulated by the random Markov process, and the connectivity assumption is relaxed by merely requiring the union graph of possible subgraphs to be connected. First, new adaptive observers are constructed that can observe leaders' states without access to global topology information, even under randomly switching topologies and DoS attacks. Second, based on fuzzy logic systems and output regulation methods, fuzzy system state estimators are introduced to address challenges associated with unknown nonlinear functions and unmeasurable states of followers. Further, a new fuzzy controller is developed to guarantee the achievement of flexible secure output containment, where containment coefficients no longer depend on the Laplacian matrix and can be flexibly preset to accommodate various tasks. Finally, the theoretical algorithm is validated through simulation results. © 1993-2012 IEEE.

关键词： Markov processes

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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Visible-Infrared Person Re-identification with Real-world Label Noise

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4857-4869页

作者： Zhang, Ruiheng Cao, Zhe Huang, Yan Yang, Shuo Xu, Lixin Xu, Min Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control School of Mechatronical Engineering Beijing100081 China Chinese Academy of Sciences New Laboratory of Pattern Recognition State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China University of Technology Sydney School of Electrical and Data Engineering Faculty of Engineering and Information Technology UltimoNSW2007 Australia

In recent years, growing needs for advanced security and traffic management have significantly heightened the prominence of the visible-infrared person re-identification community (VI-ReID), garnering considerable attention. A critical challenge in VI-ReID is the performance degradation attributable to label noise, an issue that becomes even more pronounced in cross-modal scenarios due to an increased likelihood of data confusion. While previous methods have achieved notable successes, they often overlook the complexities of instance-dependent and real-world noise, creating a disconnect from the practical applications of person re-identification. To bridge this gap, our research analyzes the primary sources of label noise in real-world settings, which include a) instantiated identities, b) blurry infrared images, and c) annotators' errors. In response to these challenges, we develop a Robust Hybrid Loss function (RHL) that enables targeted recognition and retrieval optimization through a more fine-grained division of the noisy dataset. The proposed method categorises data into three sets: clean, obviously noisy, and indistinguishably noisy, with bespoke loss calculations for each category. The identification loss is structured to address the varied nature of these sets specifically. For the retrieval sub-task, we utilize an enhanced triplet loss, adept at handling noisy correspondences. Furthermore, to empirically validate our method, we have re-annotated a real-world dataset, SYSU-Real. Our experiments on SYSU-MM01 and RegDB, conducted under various noise ratios of random and instance-dependent label noise, demonstrate the generalized robustness and effectiveness of our proposed approach. © 1991-2012 IEEE.

关键词： Information management

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Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering

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IEEE Transactions on Multimedia 2025年

作者： Yan, Weiqing Yang, Tingyu Tang, Chang Yantai University School of Computer and Control Engineering Yantai261400 China China University of Geosciences School of Computer Science Wuhan430074 China

Multi-view clustering, which identifies shared semantics from different perspectives and classifies data samples into distinct categories using unsupervised methods, is gaining increasing interest. This task primarily focuses on learning consistent multi-view feature representations and clustering labels. Current approaches for achieving consistent multi-view feature representations often use techniques such as cascading, weight fusion, and attention mechanism fusion. These methods reconstruct features based on original low-level features via encoder-decoder, which often contain visual private information, leading to misleading feature representations. Furthermore, in the clustering label learning process, many methods use a two-stage approach: first, they achieve consistent feature representations, and then they apply hard labeling methods like K-means or spectral clustering to obtain clustering labels. Single-stage methods typically derive consistent labels through a linear coding layer based on consistent representation learning. These methods do not fully utilize the multi-view view semantic information, and consistent representation learning may be impaired when some low-quality views are present, leading to the generation of inaccurate semantic labels. To address these issues, we propose a Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering. Specifically, we introduce a consensus high-level feature learning module that uses a shared MLP layer to transform low-level features into a high-level feature space. To enhance the consistency between high-level features from different views, we maximize mutual information between these features and introduce the U-Projection module, which improves the expressive power of the consensus feature via resampling the features and concatenating the fused features before and after sampling operations. Additionally, we propose a self-supervised semantic label learning module that employs a dual-br

关键词： Self-supervised learning

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A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Ran, Weizhi Nantogma, Sulemana Yang, QingZhuo Zhang, Shangyan Wang, Yan Xu, Yang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Xi'an University of Architecture and Technology School of Information and Control Engineering China

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid pre-set solutions, and limited interoperability. This paper presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as a Markov Decision Process (MDP), with parameters instantiated using semantic information and rules. A novel Feedback-based Reinforcement Learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation. © 2014 IEEE.

关键词： Smart homes

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Ptychographic characterization of spatiotemporal wavepackets

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Optica 2025年第4期12卷 498-504页

作者： Liu, Yuanyuan Xu, Yingming Zhang, Nianjia Liu, Dawei Wei, Lijin Cao, Qian Shi, Junhui Zhan, Qiwen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory 100 Haike Road Shanghai201204 China Research Center for Novel Computational Sensing and Intelligent Processing Zhejiang Lab. Hangzhou311100 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

Exciting advances in spatiotemporal wavepackets have shown great potential in a range of applications, including particle manipulation, time-resolved imaging, and unique light–matter interactions. In particular, spatiotemporal optical vortice (STOV) pulse beams characterized by a helical phase structure in space and time are a recently emerging type of complex structured ultrashort pulse. However, the high-resolution measurement and manipulation of ultrashort spatiotemporal wavepackets still face major challenges due to their inseparable properties in space and time. In this study, based on spectra-encoded ptychography, we implement a simple, compact, reference-free STOV characterization technique with a temporal resolution of 6 fs and a spatial resolution of 11.05 µm. This technique can be employed to analyze various intricate spatiotemporal structural beams, contributing to the growing field of spatiotemporally engineered ultrafast laser pulse beams. Additionally, as a lensless measurement technique, this characterization method may be extended to other spectral ranges, including the extreme ultraviolet and X-ray domains. © 2025 Optica Publishing Group.

关键词： Wave packets

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High fault-tolerant DNA image storage system based on VAE

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Lu, Yuyang Zhang, Zhihao Yang, Jing Zhang, Cheng The School of control and computer engineering North China Electric Power University Beijing102206 China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

DNA-based storage has emerged as a promising storage paradigm due to its immense storage potential. However, the error-prone nature of DNA sequencing and synthesis processes limits this potential. Image data is typically compressed before storage, and even a single mismatch can lead to catastrophic error propagation during decompression, rendering the image unrecoverable. To reduce the error rate of DNA storage-based image compression, we have designed a high fault-tolerant DNA image storage system and applied it to image compression for DNA storage. This system achieves significant improvements in both image data compression ratio and resilience through three key innovations: 1) Using a Variational Autoencoder (VAE) to compress the image into uniformly sized latent variable blocks, followed by further compression via Singular Value Decomposition (SVD);2) Quantizing the floating-point numbers in the latent variable blocks and applying rotational coding to the resulting ternary sequences, effectively ensuring positive constraints on homopolymer run lengths and GC content;3) Optimizing the error-correction scheme to best recover each type of error by quantizing it back to its original value. Through image scaling, we adjust the compression ratio, and the comparative results of image compression simulations demonstrate the performance of the proposed model, highlighting its superiority in fault tolerance and storage density. © 2002-2011 IEEE.

关键词： Data compression ratio

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Online Flow Scheduling in Virtualized Time-Sensitive Networks: A Joint Admission control and VNF Embedding Approach

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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