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H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

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A Distributed Learning Algorithm for RBF Neural Networks 6

A Distributed Learning Algorithm for RBF Neural Networks

引用

6th International Conference on Computational Intelligence and Applications, ICCIA 2021

作者： Dong, Jing Yang, Liu Luo, Xiaoqing Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9781665439336

Training a radial basis function (RBF) neural network on a single processor is usually challenging due to the limited computation and storage sources, especially for data with large and multi-dimensional features. In addition, in real applications, large-scale data may be collected in a distributed manner, which also makes it difficult to handle the data only with a single processor. To address these issues, we propose a distributed learning algorithm for RBF neural networks. In this algorithm, RBF neural networks can be trained in parallel using multiple processors. Specifically, the large-scale training data is divided into groups and each processor is associated with only one group. By introducing a shared output weight vector, training can be carried out simultaneously on different processors. The formulated optimization problem is addressed with alternating direction method of multipliers (ADMM) framework. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Digital storage

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Analysis of Moving Target Defense in Unbalanced and Multiphase Distribution Systems Considering Voltage Stability

Analysis of Moving Target Defense in Unbalanced and Multipha...

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2021 IEEE International Conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Liu, Mengxiang Zhao, Chengcheng Zhang, Zhenyong Deng, Ruilong Cheng, Peng College of Control Science and Engineering Zhejiang University State Key Lab. of Industrial Control Technology Hangzhou China College of Computer Science and Technology Guizhou University State Key Lab. of Public Big Data Guiyang China

ISBN: (纸本)9781665415026

Moving Target Defense (MTD) is a new technology to defend against the false data injection attack (FDIA) on distribution system state estimation (DSSE). It works by proactively perturbing the branch reactance. However, due to the challenges induced by the nonlinear dynamics and the coupling phases in the three-phase AC DSSE model, the analysis on the effectiveness and hiddenness of MTD, which are two essential performance metrics, has not yet been conducted. In this paper, we attempt to optimize the effectiveness and hiddenness of MTD while considering voltage stability. Firstly, we quantify the two metrics with approximated measurement residuals. Based on the quantified metrics, we formulate an optimization problem to maximize the effectiveness with guaranteed hiddenness and ensure voltage stability by minimizing the voltage variation induced by MTD. The original problem is transformed to a polynomial optimization problem based on the observation that the alteration of the projection matrix caused by reactance perturbation is neglectable, such that the near-optimal result can be obtained. Finally, extensive simulations are conducted on the IEEE 13-bus test feeder to evaluate the performance of the proposed MTD. © 2021 IEEE.

关键词： Network security

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Research on APSO-WNN and its Application in Vibration Fault Diagnosis of Hydroelectric Generating Units

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2021年第2期42卷 163-172页

作者： Xu, Yan-Chun Xia, Hai-Ting Fang, Shao-Chen Lu, Mi Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station China Three Gorges University Yichang443001 China Department of Electrical & Computer Engineering Texas A& M University College Station91207 United States

Based on the research of wavelet neural network (WNN), an adaptive particle swarm optimization (APSO) is proposed to solve the complex nonlinear relationship between the vibration characteristics and fault types of hydropower systems. This algorithm combines characteristics of evolutionary compute and swarm intelligences. It can change inertia weight according to the states of the particle adaptively. APSO rises the training speed of wavelet neural network, and improves network training accuracy. Experiments indicate that wavelet neural network based on APSO contains a higher precision and faster speed of diagnosis, compared with back propagation (BP) neural network and wavelet neural network. The algorithm is a new method for fault diagnosis of hydroelectric generating units (HGU), and it can be effectively applied to practical engineering. © 2021, Chinese Mechanical engineering Society. All right reserved.

关键词： Failure analysis

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A Survey on Security Issues in Cloud Systems

A Survey on Security Issues in Cloud Systems

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Roedunet International Conference (RoEduNet)

作者： Diyar Jamal Hamad Khirota Gorgees Yalda Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

“The cloud“ points out to servers, the databases and software that run on those servers. The Internet has always been constructed of infrastructure, servers and clients. Clients send their requests to servers, and servers reply, but cloud computing is distinct in the form that cloud servers are not just replying to requests with on-demand processing but they are saving data and running programs instead of the client. Cloud computing is a technology that is crossing a great expansion today. In cloud computing you can dynamically expand resources without knowledge of a new infrastructure, without developing new software or preparing new staff and to access such great technology the only things that are asked for are an Internet connection and an Internet browser. The goal of this survey is to present the different cloud security threats and recognize the proper security mechanism used to reduce them.

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FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7 14

FPGA Implementation of SIMON-128 Cryptographic Algorithm Usi...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor El Gmati, Issam Pricop, Emil Ziadia, Marwa Laval University Department of Electrical and Computer Engineering Quebec Canada Laval University Department of Computer Science and Software Engineering Quebec Canada Majmaah University Department of Medical Equipment Technology Majmaah Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Umm Al Qura Saudi Arabia Petroleum-Gas University of Ploiesti Automatic Control Computers and Electronics Department Ploiesti Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON's block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

Exploiting Inductive Bias in Transformer for Point Cloud Cla...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Zihao Li Pan Gao Hui Yuan Ran Wei Manoranjan Paul College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing School of Control Science and Engineering Shandong University Jinan Science and Technology on Electro-optic Control Laboratory Luoyang Charles Sturt University Bathurst

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://***/jiamang/IBT

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Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar

arXiv

引用

arXiv 2025年

作者： Tang, Hui Yang, Zhan Rong, Yu Chai, Li Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430081 China The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising non-contact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-to-noise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation interference, this paper proposes a cancellation-before-estimation strategy. Firstly, we present the adaptive extensive cancellation algorithm (ECA) to suppress respiratory and its low-order harmonics. Then, we propose an adaptive harmonic enhanced trace (AHET) method to avoid intermodulation interference by refining the HR search region. Various experimental results validate the effectiveness of the proposed methods, demonstrating improvements in accuracy, robustness, and computational efficiency compared to conventional approaches based on the FMCW (Frequency Modulated Continuous Wave) system. © 2025, CC BY-NC-ND.

关键词： Intermodulation

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Dynamic Strongly Convex Sparse Operator with Learning Mechanism for Sparse Large-Scale Multi-Objective Optimization

Dynamic Strongly Convex Sparse Operator with Learning Mechan...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Xiaodong Huang Hanyu Hu Jian Wang Bin Yuan Caili Dai Sergey V. Ablameyk College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of science China University of Petroleum (East China) Qingdao China School of Petroleum Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Sparse large-scale multi-objective optimization problems (SLSMOPs) hold significant practical relevance across various domains. However, the efficacy of existing evolutionary algorithms (EAs) in tackling these optimization challenges is limited due to the high-dimensional search space and the sparsity inherent in Pareto optimal solutions. To overcome these difficulties, a dynamic strongly convex sparse operator with learning mechanism (DSCSOLM) is proposed. We design a novel strongly convex function that can effectively generate sparse solutions and enable the newly generated sparse solutions to learn knowledge from the Pareto optimal solutions, making the obtained sparse solutions more in line with the sparse distribution of the Pareto optimal solutions. Moreover, dynamic parameter is used within the proposed strongly convex function during the execution of the algorithm. Experimental results of benchmark and neural network training problems validate that DSCSOLM outperforms state-of-the-art (SOTA) comparative algorithms.

关键词： Learning systems Training Heuristic algorithms Neural networks Evolutionary computation Pareto optimization Benchmark testing Convex functions Complex systems Optimization

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

引用

IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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