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ESO-Based Control for 2-D Networked Control Systems With Markov-Type DoS Attacks

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IEEE Transactions on Network Science and Engineering 2025年

作者： Cheng, Peng He, Shuping Xiao, Gaoxi Zhang, Weidong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University 230601 China Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology School of Electrical Engineering and Automation Hefei 230601 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Shanghai Jiao Tong University Department of Automation Shanghai 200240 China

This article aims to investigate the issue of the extended state observer (ESO)-based controller design for a two-dimensional (2-D) networked control system (NCS), where the communication channel suffers from Markov-type denial-of-service (DoS) attacks. The concerned system is developed by the Fornasini-Marchesini second (FMII) model. Firstly, the evolution of the DoS attacks is characterized with the help of a Markov jump process with two modes, which can eliminate the need for an explicit energy description. Moreover, a 2-D information compensation mechanism is devised, which utilizes the most recently received bidirectional signals unaffected by DoS attacks to ensure continuous input to the ESO, thereby mitigating the impact of communication interruptions. With the aid of the ESO, the developed control scheme ensures system stabilization and guarantees ultimate uniform boundedness under DoS attacks. Finally, a simulation example is conducted to validate the soundness and effectiveness of the presented control strategy, and the analysis of the Markov-type DoS attack intensity on system stability is presented as well. © 2013 IEEE.

关键词： denial-of-service (DoS) attacks extended state observer (ESO) Fornasini-Marchesini second (FMII) model Networked control system (NCS) ultimate uniform boundedness

Periodicity Analysis of the Logistic Map over Ring Z3n

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arXiv 2023年

作者： Lu, Xiaoxiong Xie, Eric Yong Li, Chengqing School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Computer Science Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

Periodicity analysis of sequences generated by a deterministic system is a long-standing challenge in both theoretical research and engineering applications. To overcome the inevitable degradation of the Logistic map on a finite-precision circuit, its numerical domain is commonly converted from a real number field to a ring or a finite field. This paper studies the period of sequences generated by iterating the Logistic map over ring Z3n from the perspective of its associate functional network, where every number in the ring is considered as a node, and the existing mapping relation between any two nodes is regarded as a directed edge. The complete explicit form of the period of the sequences starting from any initial value is given theoretically and verified experimentally. Moreover, conditions on the control parameter and initial value are derived, ensuring the corresponding sequences to achieve the maximum period over the ring. The results can be used as ground truth for dynamical analysis and cryptographical applications of the Logistic map over various *** Codes 65P20 Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

Emotion Recognition of Virtual Reality Scenes Under Olfactory Stimulation: An ECG Study

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SSRN

SSRN 2023年

作者： Liu, Wanqing Zhong, Xinyue Feng, Weibin Xie, Jialan Luo, Yutong Guangyuan, Liu College of Electronic and Information Engineering Southwest University Chongqing400715 China Institute of Affective Computing and Intelligent Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

Virtual Reality (VR) technology enhanced by olfactory stimulation has introduced novel dimensions to human-computer interaction (HCI). While there have been studies utilizing electrocardiogram (ECG) signals to explore the impact of olfaction on the emotional experiences of traditional multimedia users, little research has delved into the influence of heightened olfactory stimulation on emotion recognition within virtual reality scenarios. To explore this issue in depth, we first initiated an olfactory stimulation experiment within a VR environment, collecting ECG data from 65 participants as they interacted with VR scenes and olfactory-enhanced virtual reality (OVR) scenes, each characterized by varying emotional valence and levels of emotional arousal. Subsequently, we extracted time-domain, frequency-domain, and nonlinear features from the ECG data. Finally, we employed three widely used machine learning classification models, namely, light gradient boosting machine, SVM, and adaptive enhancement algorithm, to discern the emotional valence and arousal levels in OVR and VR states, respectively. The results indicate that the classification recognition rates of all three classifiers are significantly higher (> 4%) within the OVR environment than in the VR context, both in terms of valence and arousal dimensions. The results are consistent with the analysis and comparison of behavioral data. This finding underscores the capacity of ECG data to effectively capture distinctions in emotional responses between OVR and VR experiences and highlights the contributory role of olfactory enhancement in VR-based emotion recognition. Our discoveries will contribute to the advancement of OVR emotion recognition research and offer fresh insights for enhancing the overall HCI emotional experience. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

Design of MEMS Step Impedance Microstrip Filter in Ku Band

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Design of MEMS Step Impedance Microstrip Filter in Ku Band

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tianyu Bai Guohui Yang Kuang Zhang Yingsong Li Tao Jiang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin China

According to the miniaturization characteristics of step impedance resonators, a step impedance bandpass filter with a center frequency of 16.55 GHz and a bandwidth of 500MHz is designed. The effects of parameter changes such as substrate resistivity and the shape of metal via etching on the performance of the filter are studied. Finally, the filter is fabricated and its performance is tested under MEMS technology.

关键词： Band-pass filters Micromechanical devices Microstrip filters Shape Resonator filters Metals Conductivity

A Weighted Flat Lattice Transformer-based Knowledge Extraction Architecture for Chinese Named Entity Recognition

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A Weighted Flat Lattice Transformer-based Knowledge Extracti...

International Conference on Computer Supported Cooperative Work in Design

作者： Hengwei Zhang Yuejia Wu Jian-Tao Zhou Inner Mongolia University College of Computer Science Hohhot China Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Named Entity Recognition (NER) is one of the contents of Knowledge Extraction (KE) that transforms data into knowledge representation. However, Chinese NER faces the problem of lacking clear word boundaries that limit the effectiveness of the KE. Although the flat lattice Transformer (FLAT) framework, which converts lattice structure into a flat structure including a set of spans, can effectively improve this problem and obtain advanced results, there still exist the problems of insensitivity to entity importance weights and insufficient feature learning. This paper proposes a weighted flat lattice Transformer architecture for Chinese NER, namely WFLAT. The WFLAT first adds a weight matrix into self-attention calculation, which can obtain finer-grained partitioning of entities to improve experimental performance, and then adopts a multi-layer Transformer encoder with each layer using a multi-head self-attention mechanism. Extensive experimental results on benchmarks demonstrate that our proposed KE model can obtain state-of-the-art performance for the Chinese NER task.

关键词： Representation learning Computational modeling Memory management Lattices Named entity recognition Transforms Knowledge representation

Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

ACCURATELY RECOVER GLOBAL QUASIPERIODIC SYSTEMS BY FINITE POINTS

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arXiv 2023年

作者： Jiang, Kai Zhou, Qi Zhang, Pingwen Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Wuhan University Wuhan430072 China School of Mathematical Sciences Peking University Beijing100871 China

Quasiperiodic systems, related to irrational numbers, are space-filling structures without decay nor translation invariance. How to accurately recover these systems, especially for non-smooth cases, presents a big challenge in numerical computation. In this paper, we propose a new algorithm, finite points recovery (FPR) method, which is available for both smooth and non-smooth cases, to address this challenge. The FPR method first establishes a homomorphism between the lower-dimensional definition domain of the quasiperiodic function and the higher-dimensional torus, then recovers the global quasiperiodic system by employing interpolation technique with finite points in the definition domain without dimensional lifting. Furthermore, we develop accurate and efficient strategies of selecting finite points according to the arithmetic properties of irrational numbers. The corresponding mathematical theory, convergence analysis, and computational complexity analysis on choosing finite points are presented. Numerical experiments demonstrate the effectiveness and superiority of FPR approach in recovering both smooth quasiperiodic functions and piecewise constant Fibonacci quasicrystals. While existing spectral methods encounter difficulties in accurately recovering non-smooth quasiperiodic *** Codes 11J68, 65D05, 65D15, 68W25 © 2023, CC BY-NC-ND.

关键词： Interpolation

SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

ON THE APPROXIMATION OF QUASIPERIODIC FUNCTIONS WITH DIOPHANTINE FREQUENCIES BY PERIODIC FUNCTIONS

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arXiv 2023年

作者： Jiang, Kai Li, Shifeng Zhang, Pingwen Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Wuhan University Wuhan430072 China School of Mathematical Sciences Peking University Beijing100871 China

We present an analysis of the approximation error for a d-dimensional quasiperiodic function f with Diophantine frequencies, approximated by a periodic function with the fundamental domain [0, L1) × [0, L2) × · · · × [0, Ld). When f has a certain regularity, its global behavior can be described by a finite number of Fourier components and has a polynomial decay at infinity. The dominant part of periodic approximation error is bounded by (formula presented), where Lj belongs to the best simultaneous approximation sequence and sj is the number of different irrational elements in j-th dimension component of Fourier frequencies, respectively. Meanwhile, we discuss the approximation rate. Finally, these analytical results are verified by some examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural frequencies