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Krein space-based approach to dynamic event-triggered H∞ filtering for a class of nonlinear discrete time systems

Automatica 2025年 177卷

作者： Zhong, Maiying Zhu, Xiaoqiang Liu, Shuai Han, Qing-Long Zhou, Donghua College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China School of Control Science and Engineering Shandong University Jinan250061 China School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC 3122 Australia

This paper investigates the problem of H∞ filtering for a class of nonlinear discrete time systems under a dynamic event-triggered scheme. First, a new general form of event-triggered filters is considered so that the filtering error system achieves full decoupling from the so-called event-triggered transmission error, which is the major concern of H∞ filtering performance degradation caused by an event-triggering mechanism. Moreover, the design of the event-triggered H∞ filter and event-triggering mechanism can be carried out independently. Second, the event-triggered H∞ nonlinear filtering is formulated as a problem of indefinite quadratic form minimum, and a Krein space-based approach is proposed for the design of the event-triggered H∞ filter. Based on the first-order Taylor approximation, sufficient and necessary conditions for the existence of a dynamic event-triggered H∞ filter are derived by employing Krein space projection, and a feasible solution is given in terms of Riccati recursions for guaranteeing the prescribed H∞ performance. Third, an algorithm with time-update and event-update recursions is also provided to perform the dynamic event-triggered H∞ nonlinear filtering. It is shown that the proposed Krein space-based approach is computational attractive and easy to implement. Finally, a three-tank system is considered as an illustrative example to demonstrate the effectiveness of the proposed approach. © 2025 Elsevier Ltd

关键词： Nonlinear filtering

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Task Offloading in Internet of Vehicles: A DRL-based Approach with Representation Learning for DAG Scheduling

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IEEE Transactions on Mobile computing 2025年第6期24卷 5045-5060页

作者： Deng, Xiaoheng Yang, Haoyu Zhang, Jingjing Gui, Jinsong Lin, Siyu Wang, Xin Min, Geyong Central South University School of Electronic Information Changsha410075 China Central South University Shenzhen Research Institute China Beijing Jiaotong University School of Electronic and Information Engineering The State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention Networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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An Entropy-Integrated Adaptive Coding and Scheduling Framework for Optimized Data Transmission in Fog-Cloud IoT Architectures

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IEEE Internet of Things Journal 2025年

作者： Lu, Yang Zang, Yuting Bian, Ziyi Zhao, Shuai Zheng, Yan Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Normal University College of Computer and Information Engineering Harbin China La Trobe University School of Computing Engineering and Mathematical Sciences MelbourneVIC Australia

Entropy-driven network coding has been used as a basis for optimizing data transmission and enhancing resource utilization in Fog-Cloud IoT architectures, including applications in smart cities, industrial automation, environmental monitoring, and healthcare. In Fog-Cloud IoT architectures, conventional data transmission protocols are inefficient because they cannot adapt to dynamic entropy levels, resulting in underutilized bandwidth, increased latency, and higher energy consumption. In this paper, we propose an Enhanced Entropy-Driven Network Coding (E-EDNC) framework to address these problems. Our framework integrates real-time entropy estimation with adaptive coding strategies and employs a hybrid evolutionary-reinforcement learning (HE-RL) algorithm to dynamically optimize coding parameters and scheduling decisions. Experimental results demonstrate that E-EDNC improves bandwidth utilization by 25%, reduces latency by 25%, and decreases energy consumption by 12%, thereby enhancing overall data transmission efficiency and reliability in Fog-Cloud IoT environments. © 2014 IEEE.

关键词： Network coding

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AoI Minimization for Air-Ground Integrated Sensing and Communication Networks with Jamming Attack

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IEEE Transactions on Vehicular Technology 2025年

作者： Mei, Hao Zhang, Haixia Zhou, Xiaotian Wang, Jing Shandong University School of Control Science and Engineering Shandong Jinan250061 China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan250061 China Shandong University School of Information Science and Engineering Shandong Qingdao266237 China

Air-ground integrated sensing and communication (AG-ISAC) networks utilize unmanned aerial vehicles (UAVs) equipped with ISAC units as aerial platforms in next-generation wireless systems, which can sense ground status information to the base stations (BSs) for further decision-making. Given that effective decisions rely on timely sensed data, the Age of Information (AoI) serves as a metric that precisely reflects the timeliness and effectiveness of the sensing and communication (S&C) process. However, recent research has paid little attention to AoI minimization in AG-ISAC networks, particularly in the presence of malicious attackers attempting to interfere S&C operations. To address this research gap, this paper considers AG-ISAC networks with S&C jamming attacks, a mission ISAC UAV senses tasks and transmits the sensed data to the BS, while a mobile UAV attacker sends jamming signals to degrade S&C performance and increase AoI. To solve this, we jointly design the trajectory and S&C transmit power of the mission UAV to mitigate S&C performance degradation to minimize the AoI. Nonetheless, the NP-hard and dynamic long-term nature of the formulated problem poses significant challenges. To tackle this troublesome problem, we develop an efficient solution using the deep reinforcement learning (DRL) technique. Specifically, we propose a twin-delayed deep deterministic policy gradient (TD3)-based trajectory design and power control algorithm (TD3-TP). In addition to this, we incorporate a reward-shaping mechanism to speed up the convergence of the algorithm. Simulation results demonstrate our algorithm achieves the fastest training speed and minimum AoI under attacks relative to four benchmark methods. © 1967-2012 IEEE.

关键词： Jamming

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Integral Sliding Mode control for Automated Vehicles under Coding-Decoding Mechanisms with Constrained Bit Rate

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IEEE Internet of Things Journal 2025年

作者： Zhang, Mingming Ding, Derui Shen, Bo Ge, Xiaohua University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai200093 China Donghua University College of Information Science and Technology Shanghai201620 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate. The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the bit rate assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim's simulation instances. © 2014 IEEE.

关键词： Decoding

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End-Point Drive and Reverse Enhanced Decoding-Based Traffic Participants Trajectory Prediction Under Bird's Eye View

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Liu, Chunsheng Xie, Jincan Chang, Faliang Li, Tuo Li, Shuang Wang, Yinhai Shandong University School of Control Science and Engineering Jinan250061 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Company Ltd Jinan250101 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Company Ltd China Qilu University of Technology School of Electronic and Information Engineering Jinan250316 China University of Washington Department of Civil and Environmental Engineering SeattleWA98195 United States

Trajectory prediction under Bird's Eye View (BEV) refers to predicting the movement intention of agents based on the historical observation trajectories, which is of great significance for autonomous driving, driving safety and social navigation. The traffic prediction trajectory is multimodality with multiple reasonable trajectories and various prediction time, which often suffer complex agents interaction, cumulative errors and a large number of agents. To overcome these problems, we explore the BEV-based traffic participants trajectory prediction problem and propose the novel End-point Drive and Reverse Enhanced Decoding Network (EDRED-TPNet), based on the mechanisms of end-point driving and reverse enhanced decoding. Firstly, the Encoder based on Dynamic Spatio-temporal Graph and Multimodality Coding Fusion (ST-MC-Encoder) are constructed to effectively represent complex traffic scenario with changeable agents, encode social interactions with historical trajectories, social interaction, future trajectory and multimodality. Secondly, the End-Point Drive Module is proposed to predict the end point before predicting the complete trajectory, thus providing more accurate trajectory prediction;Lastly, to further improve the long-term prediction performance, the Reverse Enhanced Decoder (RE-Decoder) is proposed to fuse forward and reverse hidden state vectors to obtain diverse trajectories that conform to physical and social acceptability rules. We build the first AAV-captured Trajectory Prediction Dataset (UTP-Dataset) for traffic participants trajectory prediction. Experimental results show that the proposed methods can fulfill the multi-target trajectory prediction task in complex traffic scenarios and achieve high performance. © 2025 IEEE.

关键词： Air navigation

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SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

arXiv

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arXiv 2025年

作者： Bai, Runci Xu, Guibao Shi, Yanze Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100080 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China

Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_Dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Health risks

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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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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Joint Vehicle Pairing, Spectrum Assignment and Power control for Sum-Rate Maximization in NOMA-Based V2X Underlaid Cellular Networks

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IEEE Internet of Things Journal 2025年

作者： Xue, Tong Zhang, Haixia Ding, Hui Yuan, Dongfeng Shandong University of Science and Technology College of Electronic and Information Engineering Shandong Qingdao266590 China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan250061 China Shandong University School of Control Science and Engineering Shandong Jinan250061 China

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffers catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware non-orthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum-rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a non-deterministic polynomial-time (NP)-hard problem, and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i. e., 1) joint vehicle pairing and spectrum assignment subproblem, and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a three-dimensional geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum-rate and outperforms the baseline algorithms around 24%-53% within a specific range. © 2014 IEEE.

关键词： Polynomial approximation

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Frame-by-Frame Multi-object Tracking-Guided Video Captioning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Luo, Huilan Cai, Xia Shark, Lik-Kwan Jiangxi University of Science and Technology Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000 China University of Central Lancashire School of Engineering and Computing PrestonPR1 2HE United Kingdom

Video captioning through deep learning presents a multifaceted challenge that encompasses the extraction of complex spatio-temporal visual features and the synthesis of meaningful natural language descriptions. Most of the existing deep learning models can be broadly grouped as either convolution-based or transformer-based encoder-decoder networks, with video captions generated from features encoded at the pixel level for the former, and from features encoded at grid, frame, or video levels depending on encoder complexity for the latter. This paper advocates frame-level features as a more balanced and compact representation for fast caption generation, and introduces the Tracking-guided Information Augmentation for Captioning (Track4Cap) model, which integrates tracking-guided information augmentation to enhance frame-level features without relying on complex architectures or additional data modalities. Specifically, Track4Cap employs the Frame-by-Frame Multi-object Tracking module (FMoT) to identify the most relevant objects in the input video and the Object Relation Encoder (ORE) to model inter-object relationships as supplementary high-level cues for caption generation. By avoiding time-consuming end-to-end training and leveraging compact representations, Track4Cap achieves computational efficiency while improving captioning performance. Extensive experiments on two commonly used benchmark datasets demonstrate that Track4Cap not only achieves faster inference times but also outperforms state-of-the-art convolution-based and transformer-based video captioning models. The implementation of our method is publicly available at https://***/ccc000-png/Tracker4Cap. © 1991-2012 IEEE.

关键词： Deep learning

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