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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

IEEE Transactions on Information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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Bioinspired Interfacial Design of Robust Aramid Nanofiber Composite Films for High-Performance Moisture-Electric Generators

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Advanced Energy Materials 2025年

作者： Zhou, Jinman Ren, Zixuan Cui, Xuechun Liu, Xianyuan Lu, Xianyong Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education School of Chemistry Beihang University Beijing100191 China

The burgeoning field of moisture-electric generators (MEGs) for wearable electronics has garnered significant interest due to their capability to harness energy from atmospheric moisture. Nevertheless, achieving an optimal balance between mechanical resilience and energy generation efficiency in MEGs materials remains a substantial challenge. Herein, the study reports a highly resilient and flexible nanocomposite film comprising activated aramid nanofibers and sodium alginate (aASA), designed via biomimetic methodologies and advanced interfacial activation techniques to enhance power generation efficiency. The aASA film exhibits exceptional mechanical properties, including a toughness of 30.5 MJ m−3, and exhibits superior impact resistance compared to conventional aramid nanofiber films. The asymmetric sandwich-structured MEG fabricated using the aASA film (termed ASMEG) achieves sustained voltage and current output of 1.25 V and 2.52 µA cm−2 over 100 h with minimal degradation at 80% RH, showcasing outstanding performance among existing MEGs. Furthermore, the ASMEG device effectively demonstrates practical utility in self-powered sensing applications, providing structural protection alongside real-time self-monitoring capabilities during dynamic impact scenarios. This work presents an innovative strategy for designing high-performance moisture-electric generation materials specifically tailored for wearable electronics. © 2025 Wiley-VCH GmbH.

关键词： Flexible electronics

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Stackelberg Game-Based Dynamic Event-Triggered Distributed Model Predictive Control Approach for Multi-Agent Systems under Joint Deception Attacks

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IEEE Internet of Things Journal 2025年

作者： Song, Hongchao Wang, Zhenlei East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China

A dynamic event-triggered distributed model predictive control framework based on the Stackelberg game is proposed to address the stability challenges of nonlinear multi-agent systems operating under network resource constraints and the threat of deception attacks. To enhance the efficacy of the event-triggering mechanism and optimize communication resource utilization, a dynamic event-triggering mechanism incorporating an adaptive bias component is introduced. Furthermore, to emphasize the dynamic characteristics of deception attacks within network communication, a Stackelberg game framework is proposed to analyze the strategic interactions between deception attacks and control strategies. Additionally, the predictive model information of agents is integrated into the distributed model predictive control to mitigate performance degradation resulting from asynchronous triggering in multi-agent systems. The theoretical conditions necessary to ensure the actual stability of the input-state of the closed-loop system are delineated. Finally, a numerical example is presented to validate the effectiveness and superiority of the proposed algorithm. © 2025 IEEE.

关键词： Predictive control systems

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Energy-constrained optimal stealthy attack against state estimation with multi-channel protocol

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IEEE Transactions on Automatic Control 2025年

作者： Mao, Dancheng Niu, Yugang Xu, Kun Ministry of Education Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and Technology Shanghai200237 China

Research on attack design provides a reference for attack defense. To this end, this work considers the design problem of an optimal energy-constrained stealthy attack strategy orienting remote estimation. The signals from smart sensors to remote estimators are scheduled via an extended Round-Robin protocol (RRP), which sends data through each channel in a specific order, and allows more than one channel to be transitable at each instant. To achieve the optimal attack strategy that can maximize the impact on estimation performance, a new attack model with the constraints of stealthiness and energy limitations is constructed. Furthermore, an equivalent two-step optimization problem is suggested to obtain the optimal attack matrix and optimal attack scheduling, respectively. Finally, the optimality and stealthiness of the proposed energy-constrained attack strategy are verified via simulation results. © 1963-2012 IEEE.

关键词： Cyber attacks

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Fault detection method based on a copula probability model built from historical normal and failure samples

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Chemometrics and Intelligent laboratory Systems 2025年 263卷

作者： Tian, Yuyang Zhang, Yifan Li, Shaojun Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and Technology Ministry of Education Shanghai 200237 China

As modern information technology continues to advance, industrial processes are increasingly defined by their digital, informational, and intelligent characteristics. Control systems not only store a wealth of operational data reflecting normal system functioning but also retain data related to malfunctions. Unfortunately, due to the constraints of the computational framework, this fault-related information is often overlooked during the conventional fault detection process. As a result, the fault detection model may become overly sensitive to minor variations in normal operating conditions while lacking the necessary sensitivity to detect genuine fault signals. To address this issue, this paper improves the parameter estimation process of the fault detection method based on vine copula dependency description (VCDD). Traditionally, VCDD estimates parameters for individual bivariate copula functions sequentially. In contrast, this paper introduces a novel vine copula-based fault detection method (VCDD-PCFD), which utilizes a global parameter estimation approach. This approach employs an intelligent search algorithm that calculates penalties based on fault data. A training dataset is constructed from a historical database, comprising both normal operational data and fault operational data. The model structure is determined through a stepwise VCDD solution process, utilizing only the normal data from the training set. Subsequently, the Particle Swarm Optimization (PSO) algorithm is applied to identify the optimal parameters for all bivariate copulas, using the complete training set with normal data to assess the first part of fitness, while fault data is used to compute penalties. The integration of the VCDD model with the VCDD-PCFD model has strengthened the model's stability and improved the detection rate of unknown faults. The application of this dual-model detection method to a numerical example and the Tennessee Eastman benchmark process (TE process) has demonstr

关键词： Boolean functions

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Multi-Hop Attention Diffusion Graph Neural Networks For Multimodal Fake News Detection

Multi-Hop Attention Diffusion Graph Neural Networks For Mult...

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

作者： Huang, Zhongqiang Lu, Dongli Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798350368741

Fake news often spreads rapidly on social media in various forms, exposing users to a large amount of misinformation and disinformation. Recently, promising results have been achieved in fake news detection. However, most of the approaches only consider one-hop neighborhood information in cross-modal mapping relationships or focus mainly on pairwise interactions of multimodal fake news. However, the fine-grained feature mapping of multimodal fake news may go beyond binary relations. To address these issues, we propose a Multi-Hop Attention Diffusion Graph Neural Networks in this work to capture and complement the multivariate mapping relationships between fine-grained features, while extracting cross-modal higher-order alignment representations for multimodal fake news detection via cross-modal contrastive learning and modal matching. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in the multimodal fake news detection task. © 2025 IEEE.

关键词： attention diffusion fake news graph neural networks multivariate mapping relationships

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Regularized Decoupling Transformer-Based Variable Selection and Soft Sensor Modeling for Fermentation Processes

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yao, Zongyu Jiang, Qingchao Gu, Xingsheng East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China

Soft sensor modeling of fermentation processes is crucial to enhance yield and quality. However, the nonlinear and dynamic characteristics of these processes, coupled with variable redundancy, often degrade model performance. A variable selection and soft sensor modeling scheme based on regularized decoupling Transformer (RDTF) is proposed in this study to address these issues. First, a network framework comprising an embedding network, positional encoding, and a multilayer Transformer (TF) encoder is employed to extract dynamic nonlinear features. Second, a decoupling layer with mean and variance networks ensures that the latent variables are representative and can independently reflect the information from the original process variables. Third, a regression model is established between these latent variables and quality indicators. Regularization technology, namely, a gradient penalty term in the loss function, allows the model to select the most critical process variables for predicting quality indicators, addressing feature redundancy and collinearity, and improving the model’s performance and robustness. Last, the effectiveness of the proposed RDTF is verified through a simulated penicillin fermentation process case and real penicillin and erythromycin production cases. Across multiple real fermentation cases, the model achieves R2 values exceeding 0.95 and consistently outperforms existing models in terms of accuracy and stability. These results highlight RDTF’s potential for industrial applications. © 1963-2012 IEEE.

关键词： Redundancy

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Network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Exploring Source-Free X-ray Prohibited Item Detection with An Assistant-Teacher Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jia, Wanying Jia, Tong Ma, Bowen Huang, Cunhe Tian, Yixin Wang, Hao Chen, Dongyue Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Data Analysis and Optimization for Smart Industry Ministry of Education Northeastern University China

Security scanners are important for maintaining long-term social stability and safety. However, different security scanners exist endogenous domain shifts. Traditional unsupervised methods require access to the source domain for adaptation during training. But in practical deployment, data privacy restricts access to data between different security scanners. To exploring the this issue, Assistant-Teacher Network (ATN) is proposed to address the endogenous domain shift of X-ray prohibited item detection in the case of source-free domain adaption. In particular, to suppress the problem of false positives and loss of important information in the source-free domain adaption, we propose a common cognitive mechanism. To further prevent the model from optimizing towards the wrong direction under false positive labels, an external ATN loop is designed. In addition, an image de-stylization mechanism is proposed to alleviate the endogenous domain shift, allowing the network to learn the essential features of the X-ray image. By utilizing internal and external loops, ATN can adaptively apply to multiple security scanners in a source-free domain adaptation. The comprehensive experimental results on X-ray images and natural images indicate that our method is effective. Especially in X-ray prohibited item detection, our method has achieved state of the art results compared to recent methods. Our code will be available on https://***/JiaWanying/***. © 1963-2012 IEEE.

关键词： Self-supervised learning

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Safety Control of Switched Systems via Multiple Barrier Functions

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IEEE Transactions on Automatic Control 2025年

作者： Long, Lijun Huang, Chunxiao Sun, Zhendong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University China

This paper is concerned with the problem of safety control for switched systems, where different safe sets are allowed for different subsystems and safety is not necessarily possessed for subsystems. A necessary and sufficient condition is presented for safety of switched systems by exploiting a multiple barrier functions (MBFs) method. To reveal the relationship about safety between subsystems and switched systems, an extended Nagumo's Theorem in the switched systems framework is derived. Also, several switched control design conditions are respectively provided to ensure safety of switched systems. Based on the union of MBFs and multiple Lyapunov functions (MLFs), a novel safety-critical control framework is established to simultaneously ensure safety and stability of switched systems. Also, an auxiliary cost function is introduced to transform the safety-critical optimal control (SCOC) problem of switched systems into the one of non-switched systems. Finally, three examples are illustrated to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Lyapunov functions

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