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Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 878-892页

作者： Honghai Wang Qing-Long Han the College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang 110819China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state *** the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay *** that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the *** this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first *** allowable regions of consensus protocol parameters are *** necessary and sufficient conditions for determining effective protocol parameters are *** designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent ***,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are ***,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

关键词： Consensus protocol Hurwitz stability multi-agent systems quasi-polynomials time delay

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Universal and Efficient Adversarial Training Framework With Membership Inference Resistance

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IEEE Internet of Things Journal 2025年第12期12卷 18665-18677页

作者： Yan, Ran Du, Ruiying He, Kun Chen, Jing Li, Qiao Wu, Cong Wuhan University School of Cyber Science and Engineering Wuhan430072 China Nanyang Technological University College of Computing and Data Science Nanyang Singapore

Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this article, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailormade example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime. © 2014 IEEE.

关键词： Adversarial machine learning

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Research on traffic sign detection method based on FLB-YOLOv8 5

Research on traffic sign detection method based on FLB-YOLOv...

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5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Cai, Yichong Min, Rui Huang, Jinyu School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (纸本)9781510681842

In this paper, the proposed FLB-YOLOv8 model address issues in current traffic sign recognition, such as leakage, false detection, low accuracy, and excessive model parameters. Firstly, a small target detection layer is added to improve semantic information and feature expression for small targets. Secondly, the FasterNet Block structure replaces the Bottleneck structure in the C2f module of YOLOv8, reducing parameters and computation while maintaining accuracy. The introduction of the LSK module adjusts the spatial receptive field dynamically, improving feature extraction capability. Lastly, a weighted bidirectional feature pyramid network enables efficient bidirectional cross-scale connection and feature fusion, balancing feature information of different scales. Experimental results on the CCTSDB dataset show that the improved model achieves 78.3% mean average precision (mAP), a 4.7 percentage point improvement over the original model. It also reduces model parameters by 56% and computation by 26%. This algorithm effectively improves detection accuracy, reduces model complexity, and achieves better performance in traffic sign detection. © 2024 SPIE.

关键词： Traffic signs

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of Computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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Adaptive whale optimization based clustering method for K- anonymization in social networks

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Multimedia Tools and Applications 2024年 1-21页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of CSE SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of computing technologies School of computing College of engineering and technology SRM Institute of science and technology Kattankulathur603203 India

The usage of online social networks (OSNs) has been significantly swelling over the years. Data from SNs has been shared publicly for the purpose of deeper understanding user behaviour and data mining tasks. However, securing individual identification from the publicly published data is the primary issue in sharing social network databases. K-anonymization is a well-known technique for preventing individual identification of publically available datasets. According to this method, each record in the anonymized dataset must be identical to at least k-1 other records in terms of a set of privacy-related attributes. Although anonymizing the original dataset to meet the k-anonymity criteria is straightforward, the anonymized dataset must maintain as much information from the original dataset as possible. Recently, clustering methods have been used to apply K-anonymization to the social dataset successfully. This paper applies a swarm-based clustering technique, called adaptive whale optimization algorithm (AWOA) for clustering social data. The WOA algorithm replicates humpback whale behavior and employs fewer internal parameters to support a better balance of exploitation and exploration. The method is tested on three publicly available data sets: Yelp and Facebook dataset. The experimental findings show that the proposed method attained the minimum information loss of 11% for Yelp dataset and 10% for facebook dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anonymity

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing211189 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2002-2012 IEEE.

关键词： Federated learning

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KubeSPT: Stateful Pod Teleportation for Service Resilience with Live Migration

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IEEE Transactions on Services computing 2025年第3期18卷 1500-1514页

作者： Zhang, Hansheng Wu, Song Fan, Hao Huang, Zhuo Xue, Weibin Yu, Chen Ibrahim, Shadi Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong China

Container orchestration systems, such as Kubernetes, streamline containerized application deployment. As more and more applications are being deployed in Kubernetes, there is an increasing need for rescheduling - relocating a running pod to different nodes - due to system upgrades, node failures, and load-balancing optimizations. Live migration, which transfers services from source nodes to target nodes with minimal downtime, is the ideal support for rescheduling. However, implementing live migration for pods that run stateful services is challenging, because Kubernetes manages pods as stateless. First, the current pod's network namespace initialization process causes a mismatch in the network state between the migrated pod and internal containers. Second, migrating the memory state results in extended downtime. Third, Kubernetes operations on pods do not consider preserving the state of the pods. Therefore, we propose KubeSPT to achieve live migration of stateful pods in rescheduling scenarios. Firstly, we synchronize the network state of pods and internal containers by controlling packet flow and implement fast service redirection. Secondly, we introduce a Hot Data and Lazy-Restore method for memory restoration to reduce migration downtime. Finally, we decouple pod migration operations from other Kubernetes operations to ensure compatibility with live migration. Experimental results show that KubeSPT reduces downtime by 86%-93% compared to current rescheduling methods. © 2008-2012 IEEE.

关键词： Restoration

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Design and Development of Defected Ground Structure Fractal Antenna based on Osgood Curve for C-band Modern Wireless Communications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 23-29页

作者： Singh, Gurmeet Kumar, Ashish School of Computing Science and Engineering Galgotias University Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Multifunctional behavioral antennas are one of the significant features of the modern wireless communication systems. This is due to the fact that the miniaturized sizes of the devices demand compact and low-profile antennas that cover limited space and provide multi-frequency functionalities over a wide range of the spectrum. In view of these prerequisites, this paper presents the two design approaches utilizing Osgood Curve Fractal structure as the antenna element with different feeding techniques. The proposed antennas resonate at multiple frequencies covering S-and C-band with excellent impedance matching. The designs have been optimized in terms of scaling factor and position of microstrip feed line with peak gain of 4.5 dBi. Design 1 has significant bandwidth for required applications;however, design 2 possesses low bandwidth with small frequency ratio. Both the proposed designs are fabricated, and results are quite promising. The proposed designs can be utilized for various sub-6 GHz 5G automotive applications as it can easily integrated in the various parts of the modern passenger car. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Bandwidth

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Hierarchical detection of unsafe conditions in banks based on YOLOv8 5

Hierarchical detection of unsafe conditions in banks based o...

引用

5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Jiang, Shuping Li, Yuansong School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (纸本)9781510681842

This study proposes an improved YOLOv8 algorithm based on the network model framework in response to issues in grading accuracy, slow speed, high false alarm rate, and the excessive workload of monitoring staff encountered in the tiered monitoring of unsafe banking conditions. These challenges lead to less effective monitoring results. The study utilized a dataset that includes PASCAL VOC2007 and a section of a self-built dataset, collectively comprising of 8,511 images. Initially, a Partial Convolutions module was introduced to the backbone section, enhancing the ability to recognize occluded objects in images and improving the resistance to interference. Following that, an enhancement was made to the Bottleneck structure, integrating Large Kernel Attention (LSKA) and enhancing its detection capabilities for small targets. For large target detection, Multi-Scale Dilated attention was employed to enhance the model's processing efficiency and detection accuracy. Lastly, an ASFF head was incorporated. Overall, the improvements resulted in a 3.0% increase in mAP@0.5, boosting detection accuracy and speeding up the detection process. © 2024 SPIE.

关键词： Image enhancement

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Lightweight YOLOv8 for grape leaf lesion detection 3

Lightweight YOLOv8 for grape leaf lesion detection

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2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Ren, Yuhao Huang, Jinyu School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In the field of smart agriculture, the rapid and accurate detection of grape leaf diseases is crucial, especially for early-stage small lesions. To enhance the efficiency of detecting grape leaf diseases in resource-limited environments, this study presents an optimized lightweight model based on the YOLOv8n framework, named YOLO-BMCA. By integrating the upgraded ADown module and incorporating CGBlock enhancements, the model not only improves the efficiency of capturing both local and global information but also significantly reduces the consumption of computational resources. The adoption of BiFPN strengthens the multi-level feature fusion, while the MLCA mechanism focuses on enhancing the capture of key features, thereby increasing accuracy. In target detection of grape leaf diseases, YOLO-BMCA achieved an accuracy rate of up to 94.7%, with improvements of 2%, 1.1%, and 3.3% in the recall rate, mAP50, and mAP50-90 respectively, compared to the original algorithm. Meanwhile, the model's parameter volume and size were also significantly reduced by 58.6% and 55%, respectively, demonstrating the strong applicability of the optimized model in resource-constrained environments. © 2024 SPIE.

关键词： Diseases and disorders Performance modeling Feature extraction Data modeling Detection and tracking algorithms Convolution Feature fusion Deep learning Agriculture Target detection

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