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Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 219-230页

作者： Dahui Luo Yujuan Wang Yongduan Song the Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing UniversityChongqing 400044 the School of Automation Chongqing UniversityChongqing 400044 the Star Institute for Intelligent Systems Chongqing 400044 IEEE

This paper investigates the prescribed-time control(PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and nonvanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.

关键词： Adaptive control prescribed time control(PTC) strict-feedback systems tracking control

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A Undersampling-DoppelGANger based Data Generation Method for Unbalanced BGP Data 9

A Undersampling-DoppelGANger based Data Generation Method fo...

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9th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Yang, Yumeng Yang, Ximin Tang, Wan Li, Liuhuan South-Central Minzu University School of Computer Science Wuhan China State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing China

ISBN: (纸本)9798350304428

As one of the main border gateway protocol (BGP) security schemes at present, the research on machine learning-based BGP anomaly detection technology faces severe data imbalance due to the lack of BGP anomaly data. To solve this problem, this paper proposes an unbalanced BGP data generation method based on undersampling technology and DoppelGANger, called US-DPGAN. Firstly, random undersampling is performed on majority-class samples to reduce the imbalanced ratio of the dataset and reduce the bias impact of majority-class samples on the data generated by DoppelGANger. Then, we use the trained DoppelGANger model to generate anomaly samples that match the original data distribution and add these samples to the original dataset to create a balanced BGP dataset. Through qualitative and quantitative experiments, we demonstrate that the data generated by the proposed US-DPGAN method better fits the distribution of the original data compared with the original DoppelGANger method. In addition, the anomaly detection model constructed using the US-DPGAN generated data outperforms some other methods in terms of detection accuracy and F1-Score. © 2023 IEEE.

关键词： Border Gateway Protocol

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A double-layer crowd evacuation simulation method based on deep reinforcement learning

A double-layer crowd evacuation simulation method based on d...

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作者： Zhang, Yong Yang, Bo Zhu, Jianlin College of Computer Science South-Central Minzu University Wuhan China Key Laboratory of Cyber-Physical Fusion and Intelligent Computing South-Central Minzu University State Ethnic Affairs Commission Wuhan China

Existing crowd evacuation simulation methods commonly face challenges of low efficiency in path planning and insufficient realism in pedestrian movement during the evacuation process. In this study, we propose a novel crowd evacuation path planning approach based on the learning curve–deep deterministic policy gradient (LC-DDPG) algorithm. The algorithm incorporates dynamic experience pool and a priority experience sampling strategy, enhancing convergence speed and achieving higher average rewards, thus efficiently enabling global path planning. Building upon this foundation, we introduce a double-layer method for crowd evacuation using deep reinforcement learning. Specifically, within each group, individuals are categorized into leaders and followers. At the top layer, we employ the LC-DDPG algorithm to perform global path planning for the leaders. Simultaneously, at the bottom layer, an enhanced social force model guides the followers to avoid obstacles and follow the leaders during evacuation. We implemented a crowd evacuation simulation platform. Experimental results show that our proposed method has high path planning efficiency and can generate more realistic pedestrian trajectories in different scenarios and crowd sizes. © 2024 John Wiley & Sons Ltd.

关键词： Motion planning

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Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach 22

Dynamic Task Scheduling for Coordinated Truck-Drone Parcel D...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ruitai Yuan, Quan Liao, Tongxin Luo, Lijun Hu, Menglan Lai, Pan Zhang, Xiao South-Central Minzu University College of Computer Science China State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing China Huazhong University of Science and Technology School of Electronic Information and Communications China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper's contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency. © 2024 IEEE.

关键词： Poisson distribution

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Online Dynamic Scaling of Microservices with Fair Probabilistic Routing 22

Online Dynamic Scaling of Microservices with Fair Probabilis...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Yang Lin, Shisheng Hu, Yi Wang, Liangyuan Hu, Menglan Lai, Pan Xie, Yuanai College of Computer Science South-Central Minzu University China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China School of Electronic Information and Communications Huazhong University of Science and Technology China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy. © 2024 IEEE.

关键词： microservice request routing service deployment services computing

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Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification 24

Evolution-aware VAriance (EVA) Coreset Selection for Medical...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hong, Yuxin Zhang, Xiao Zhang, Xin Zhou, Joey Tianyi College of Computer Science South-Central Minzu University Wuhan China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission South-Central Minzu University Wuhan China XiDian University Xi'an China Singapore

ISBN: (纸本)9798400706868

In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limited environments such as remote medical facilities and mobile devices. This necessitates effective dataset compression techniques to reduce storage, transmission, and computational cost. However, existing coreset selection methods are primarily designed for natural image datasets, and exhibit doubtful effectiveness when applied to medical image datasets due to challenges such as intra-class variation and inter-class similarity. In this paper, we propose a novel coreset selection strategy termed as Evolution-aware VAriance (EVA), which captures the evolutionary process of model training through a dual-window approach and reflects the fluctuation of sample importance more precisely through variance measurement. Extensive experiments on medical image datasets demonstrate the effectiveness of our strategy over previous SOTA methods, especially at high compression rates. EVA achieves 98.27% accuracy with only 10% training data, compared to 97.20% for the full training set. None of the compared baseline methods can exceed Random at 5% selection rate, while EVA outperforms Random by 5.61%, showcasing its potential for efficient medical image analysis. © 2024 ACM.

关键词： Medical imaging

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Graph Low-Rank Non-Negative Matrix Factorization with Auto-Encoders for Fault Detection 14

Graph Low-Rank Non-Negative Matrix Factorization with Auto-E...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Liu, Yabing Yu, Shanshan Guo, Wei Leung, Man-Fai School of Electronic and Information Engineering Southwest University Chongqing China South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing Wuhan430074 China School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9798350353334

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics T2 (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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GANDPS: Ensuring Both Detection Precision and Stability in GAN-based Anomaly Detection for Network Traffic 21

GANDPS: Ensuring Both Detection Precision and Stability in G...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Liuhuan Yang, Ximin Zhang, Xiao Yang, Yumeng Chen, Xuefeng Tang, Wan South-Central Minzu University School of Computer Science Wuhan China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China China Telecom Group GuiZhou Co. Ltd. Guiyang China

ISBN: (纸本)9798350329223

With the ever-growing network traffic and the vast amount of abnormal traffic being created, anomaly detection methods have attracted close attention in the cybersecurity domain. Generative adversarial networks (GANs) have been applied to network traffic anomaly detection, but obtaining high-precision and consistently stable detection results is still challenging for current methods. In the paper, we propose an improved GAN-based anomaly detection method called GANDPS for network traffic. The improved GAN introduces an encoder based on the original GAN, and a specialized layer is integrated into the encoder to process abnormal data. This improvement enables the encoder to effectively capture the features of normal data while distinguishing the features of abnormal data. In addition, an analogous reconstruction training strategy is employed between the generator and the encoder to balance the adversarial training between the generator and the discriminator, thereby improving the detection precision and stability of the GAN-based model. This comprehensive enhancement not only effectively elevates the detection performance of our method, but also ensures stable detection results. Experimental results on public datasets demonstrate that the proposed GANDPS outperforms multiple GAN-based methods in terms of detection precision, recall, and F1-score, and the detection results are stabilized within acceptable ranges. © 2023 IEEE.

关键词： Anomaly detection

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Low-Cost Beam Steering that Covers the Entire Azimuthal Plane Powered by Amplitude-Only Synthesis of a Quintuple-Dipole Source

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Electromagnetic Science 2023年第3期1卷 100-113页

作者： Da Yi Zhiyang Qi Mingchun Tang Dajiang Li Yilin Lang Xingchang Wei The Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Microelectronics and Communication EngineeringChongqing UniversityChongqing 400044China Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing UniversityChongqing 400044China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China

A new amplitude-only synthesis approach in quintuple electric dipoles(EDs)is proposed to enable the regulation of power flow in the azimuthal *** regulation methodology is investigated analytically with double ED,triple ED and quintuple ED ***,when the phase difference between the EDs switches,the double ED model,acting as a reconfigurable source,can work in two modes with different power flow directions,i.e.,unidirectional mode and bidirectional ***,from the triple ED model,it is verified that the power flow in the two working modes can be regulated to the desired direction in the azimuthal plane with high precision by controlling the feeding amplitudes of the ***,a quintuple ED model is developed to enhance the symmetry and consistency of the power flow regulation in the ***,a prototype of the azimuthal beam steering system,including digitally controlled radio-frequency(RF)frontend tuning circuits,optimized practical feeding elements,and an end-fire radiator,is designed,fabricated and *** unidirectional and bidirectional modes are observed,and satisfactory single-beam and dual-beam steering performance in the azimuthal plane is *** most phased arrays that realize beam steering by configuring phases,the beam steering of the proposed reconfigurable source in each mode can be arbitrarily synthesized only by the amplitudes of the quintuple feeding elements,which provides a new route for realizing low-cost and multifunctional beam-steering systems.

关键词： Electric dipole Reconfigurable devices Amplitude-only synthesis Beam steering

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*** attention GAN for anomaly detection

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International Journal of Systems, Control and Communications 2025年第1期16卷 17-32页

作者： Sun, Hanlin Liu, Yefeng Ma, Yihang Na, ChongZheng Zhang, Qichun School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Liaoning Key Laboratory of CNC Machine Tool Cyber-Physical Fusion and Intelligent Manufacturing Shenyang Institute of Technology Shenfu Demonstration Zone Liaoning 113122 China School of Creative and Digital Industries Buckinghamshire New University High WycombeHP11 2JZ United Kingdom

Generative adversarial network (GAN) has low discrimination ability for abnormal samples or noise data in anomaly detection tasks, which affects the detection effect. This paper proposes an improved model based on Skip-GANomaly network, which adds a self-attention module to the encoder of the generator to improve the model’s ability to capture long-distance dependence of input data, so as to better understand the overall structure and local details of the image. Moreover, it dynamically focuses on the most important parts of the anomaly detection task, such as the shape, texture and colour of the object, so that the data representation ability and anomaly detection accuracy of the model are significantly improved. MvTec public dataset and KolektorSDD dataset were used for experimental verification. The results show that compared with AnoGAN, GANomaly, and Skip-GANomaly, it has better performance in terms of receiver operating characteristic (ROC) area under the curve (AUC), which proves the effectiveness of this model and has a certain application prospect. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Generative adversarial networks

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