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Orthogonal Time Frequency Space with Sub-Band Index Modulation

IEEE Communications Letters 2025年第5期29卷 1047-1051页

作者： Zhang, Si-Yu Zhang, Yue-Xia Shahrrava, Behnam Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing100101 China University of Windsor Department of Electrical and Computer Engineerning Windsor N9B 3P4 Canada

This letter proposes an innovative orthogonal time-frequency space with sub-band index modulation (OTFS-SBIM) scheme. To overcome the complexity and spectral efficiency (SE) limitations inherent in the OTFS-IM, this study introduces a transmission mechanism where sub-carriers are equally divided into two sub-bands, and the index modulation (IM) operations are restricted to a single sub-band. To offset the potential SE loss, the proposed OTFS-SBIM employs a band-controlling bit β and performs IM independently in the in-phase and quadrature (I/Q) dimensions. These improvements ensure both the diversity gain and enhanced bit error rate (BER) performance. Further, a low-complex maximum likelihood (ML) detection is proposed for bit recovery. Finally, analytical expressions for average bit error probability (ABEP) and peak-to-average power ratio (PAPR) are derived and used to evaluate the OTFS-SBIM’s performance. Simulation results demonstrate that the proposed scheme outperforms the OTFS-IM scheme in terms of the BER and PAPR metrics while maintaining higher SE. © 1997-2012 IEEE.

关键词： Signal modulation

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

arXiv

引用

arXiv 2025年

作者： Kaypak, Ali Umut Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical and Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging 3D convex shape-based High-Order control Barrier Functions (HOCBFs) to address this issue. While prior works focused on using control Barrier Functions (CBFs) for human–robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with any nominal controller and ensure safety without significantly restricting the robots’ workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. © 2025, CC BY.

关键词： Robotic arms

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Physical Intrusion Attack Detection in Fieldbus Network With Passive Fail-Safe Biasing

引用

IEEE Transactions on Automation Science and engineering 2025年 22卷 14008-14019页

作者： Wang, Xiangming Liu, Yang Yu, Nanpeng Liu, Pengfei Deng, Nanyi Li, Yanan Liu, Ting Xi’an Jiaotong University School of Cyber Science and Engineering MoE KLINNS Laboratory Xi’an710049 China University of California at Riverside Department of Electrical and Computer Engineering RiversideCA92521 United States Xi’an Thermal Power Research Institute Company Ltd. Power Infrastructure Cyber Security Research Center Xi’an710054 China Electric Power Dispatch and Control Center of Guangdong Power Grid Company Ltd. Guangzhou510699 China

Fieldbus is widely used for real-time distributed control in Industrial control Systems (ICSs) due to its simplicity and stability. The real-world fieldbus network contains hundreds of interconnected devices, presenting a widespread network layout. Attackers can attach external intrusion devices to these communication lines to launch various attacks. In this paper, we model the fieldbus network’s channel fingerprint based on the signal’s amplitude and propose a detection method to identify potential attackers (silent intrusion devices that are eavesdropping) via channel fingerprint differences. Leveraging the passive fail-safe biasing voltage in the fieldbus network such as RS485, we can still detect the intrusion device when the fieldbus is idle (i.e., no devices are transmitting commands), which can significantly reduce the detection delay with lower sampling costs. Moreover, our method can adapt to environmental changes with little computational overhead by generating dynamic thresholds. Using a monitoring unit with stored channel fingerprints, our method can be easily deployed in fieldbus networks without occupying communication resources. The effectiveness and robustness of the proposed method have been demonstrated via extensive experiments on two real-world scenarios and one simulation scenario, where we can achieve 100% accuracy and 0% false alarm rates against various intrusion devices. © 2004-2012 IEEE.

关键词： Network intrusion

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

引用

3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints

Invariant Set Analysis and Control Synthesis of Multi-Equili...

引用

International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yuejiang Han Lixian Zhang Yihang Ding Yifei Dong Ye Liang School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results.

关键词： Switched systems Robust control Switches Information processing control systems Linear matrix inequalities Closed loop systems Aircraft propulsion Intelligent control

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Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

arXiv

引用

arXiv 2025年

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This paper focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyperparameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Robust Sliding Mode control Based on Fractional Order Reaching Law for Rehabilitation Robots

引用

IEEE Access 2025年 13卷 75744-75753页

作者： Jallouli-Khlif, Rim Abdelhedi, Fatma Zayane, Chadia Nouri, Ahmed Said Derbel, Nabil University of Sfax Higher Institute of Computer Science and Multimedia of Sfax Tunisia King Abdulaziz University College of Engineering Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia National Engineering School of Gabes University of Gabes Research Laboratory of Numerical Control of Industrial Processes Tunisia Tunisia

Rehabilitation robots, particularly lower-limb exoskeletons, are transforming healthcare by assisting individuals with mobility impairments. This study introduces a novel Sliding Mode control (SMC) system based on a fractional-order reaching law, designed to enhance control performance and robustness. The proposed approach effectively manages the exoskeleton’s dynamic behavior, particularly during the transient regime, by reducing initial torque energy demand during start-up, ensuring precise trajectory tracking, and prioritizing patient safety and comfort. The method’s effectiveness is validated through MATLAB simulations and supported by a rigorous dual stability analysis, demonstrating asymptotic and finite-time convergence of the system in the reaching and sliding phases. A Comparison study with traditional SMC techniques proves that the FO-RL-SMC significantly improves energy efficiency during the transient phase and the overall dynamical behavior of the system. These results highlight the potential of the proposed FO-RL-SMC system to advance the performance of rehabilitation robots, emphasizing its value in addressing complex control challenges and improving patient outcomes. © 2013 IEEE.

关键词： Robustness (control systems)

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Multiagent Confrontation Method Based on Three-Party Dynamic Multistrategy Evolutionary Game

引用

IEEE Transactions on Computational Social Systems 2025年

作者： Jin, Shilong Wang, Yingjie Duan, Peiyong Zhang, Haijing Li, Gang Cai, Zhipeng Yantai University School of Computer and Control Engineering Yantai264005 China Qilu University of Technology Shandong Academy of Sciences Faculty of Electronics Electrical and Control Jinan250353 China Georgia State University Department of Computer Science AtlantaGA30303 United States

Unmanned agents represent a significant advancement in unmanned control and constitute an important element in the future agent warfare. Their autonomous decision-making capabilities are integral to accomplishing tasks independently. To address challenges inherent in multiparty game scenarios that traditional method struggle with and enhance the applicability and accuracy of game decision-making, this article proposes a novel multiagent confrontation method for unmanned vessels, tailored to a three-party dynamic multistrategy evolutionary game in incomplete information scenarios. The approach introduces a new incentive mechanism designed to enhance both individual and collective profits of agents. Using evolutionary game theory, a three-party model is developed, incorporating interactions among player, enemy, and neutral agents. The model tracks the evolution of strategies to identify stable equilibria across various perceptual conditions. Simulations validate the effectiveness of the proposed method in selecting optimal strategies for unmanned vessels in complex battlefield scenarios, demonstrating its potential for improving autonomous decision-making in multiparty confrontations. © 2014 IEEE.

关键词： Multi agent systems

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BAT Optimized SMC for Continuous Stirred Tank Reactor

BAT Optimized SMC for Continuous Stirred Tank Reactor

引用

IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Husam E. Elfadel Haroon Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Electronic Engineering Faculty of Engineering Omdurman Islamic University Khartoum Sudan

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This topic proposed a Sliding Mode control (SMC) strategy utilizing the BAT Algorithm (BAT-SMC) for regulating concentration and temperature in a Continuous Stirred Tank Reactor (CSTR) system. The SMC is designed to maintain concentration (CA) and temperature (T) within desired setpoints by adapting sliding surfaces in the presence of disturbances and system uncertainties. The BAT Algorithm enhances the SMC by optimizing control parameters, reducing chattering effects, and improving tracking accuracy. Simulations validate the efficacy of BAT-SMC, showing improved dynamic performance for both concentration and temperature control compared to other controllers.

关键词： Uncertainty Mechatronics Heuristic algorithms Optimal control Coolants Temperature control Continuous-stirred tank reactor Feeds Information technology Sliding mode control

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