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Asymmetric Lyapunov-Krasovskii functional method on stability of time-delay systems

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第7期31卷 2847-2854页

作者： Sheng, Zhaoliang Lin, Chong Chen, Bing Wang, Qing-Guo Qingdao Univ Sch Automat Shandong Key Lab Ind Control Technol Inst Complex Sci Qingdao 266071 Peoples R China Beijing Normal Univ Zhuhai Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Zhuhai 519087 Peoples R China

This paper presents a new asymmetric Lyapunov-Krasovskii functional method for investigating the asymptotic stability of time-delay systems. Two resulting delay-dependent asymptotic stability conditions in the form of linear matrix inequalities are given. The advantages of the new approaches are verified theoretically and numerically.

关键词： asymmetric Lyapunov– Krasovskii functional stability time‐ delay systems

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Some Method of complex Structures Information Security Risk Assessment in Conditions of Uncertainty 13

Some Method of Complex Structures Information Security Risk ...

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13th International Conference Management of Large-Scale System Development, MLSD 2020

作者： Kozlov, Aleksandr Noga, Nikolai V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Complex Networks Lab Moscow Russia

The proposed method for information security risk assessment of complex structures based on fuzzy logic methods, allows us to take into account such hard-To-define parameters as threat levels, vulnerabilities, potenti... 详细信息

ISBN: (纸本)9781728110943

关键词： Security of data

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ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 19201-19212页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sci & Technol Commun Networks Lab Beijing 100088 Peoples R China State Key Lab Networking & Switching Technol Shijiahznang 050081 Hebei Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Vis Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. The code and models will be publicly available.

关键词： Roads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks

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Bayesian Game for Distributed Weighted K-Path Vertex Cover of Dynamic networks

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IEEE SYSTEMS JOURNAL 2025年

作者： Qi, Long Li, Xiang Fudan Univ Sch Informat Sci & Technol Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Fudan Univ Res Inst Intelligent Complex Syst Shanghai 200433 Peoples R China Tongji Univ Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

weighted k-path vertex cover (WVCPk) problem is a main branch of covering problems on dynamic networks with many numerous instances in real-world complex systems. A pivotal challenge in distributed systems for network covering optimization is designing decentralized schemes for autonomous decision-making agents. This article focuses on the distributed optimization of the WVCPk problem, where individual vertices, acting as rational agents, make decisions independently based on incomplete information. We formulate a Bayesian game model to capture the interactions among agents, who face the uncertainty to the statuses of their k-path neighbors and rely on communications to enhance their individual beliefs on the actual cover state. Our analysis delves into the Bayesian Nash equilibrium and the ex-post Pareto optimal cover state (POCS) within this framework. In addition, a Bayesian game-based perturbation parallel algorithm (BGPPA) is developed and shown to converge to the ex-post POCS set, even when agents are restricted to using only estimated expected utility. A series of numerical simulations indicate that the BGPPA delivers superior performance with rapid convergence across various networks.

关键词： Games Bayes methods Optimization Uncertainty Stability analysis Nash equilibrium complex systems Perturbation methods Pareto optimization Time complexity Bayesian Nash equilibrium (BNE) distributed optimization dynamic networks expected utility Pareto optimal cover state (POCS) weightedk-path vertex cover (WVCPk).

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Heterogeneous social difference in the interplay between epidemic and information spreading

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EPL 2022年第5期140卷 51001-51001页

作者： Dai, Jin-Ying LI, Cong LI, Xiang Fudan Univ Shanghai Sch Informat Sci & Technol Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Shanghai Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

Previous studies on epidemic-information coupled network assumed that individuals receive information with the same probability. In this work, considering imperfect vaccination and the heterogeneity of human activities, we explore a coupled susceptible-infected-vaccinated-recovered/unaware-aware-unaware (SIRV-UAU) model by introducing individual social difference into the interaction between the spread of epidemic and the diffusion of epidemic-related infor-mation. The individual social difference which indicates the heterogeneity of information diffused and accepted by individuals leads to different protective levels. The primary spreading rate and the social reinforcement strength are two decisive factors to measure the individual social differ-ence. We find that increasing the social reinforcement strength, the primary spreading rate, or the influence factor of uploading information can promote the diffusion of information and inhibit the spread of epidemic. Specifically, compared with the social reinforcement strength and the primary spreading rate, the influence factor of individuals uploading information has a significant impact on the spread of epidemic. Furthermore, we study the effect of the reduction factor that reduces the risk of infection and the social reinforcement strength on the spread of epidemic. The results show that, when the reduction factor is smaller, the proportion of recovered indi-viduals at the steady state decreases significantly with the increase of the social reinforcement *** (c) 2022 EPLA

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Non-Linear Measures of Dependence in the MIMO Systems Identification with Applications to NPP Efficiency Evaluation 28

Non-Linear Measures of Dependence in the MIMO Systems Identi...

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28th Mediterranean Conference on control and Automation (MED)

作者： Chemyshov, Kirill R. Jharko, Elena Ph Sakrutina, Ekaterina A. Russian Acad Sci VA Trapeznikov Inst Control Sci Lab Syst Identificat Moscow Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Lab Distributed Informat & Control Syst Moscow Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Lab Complex Networks Moscow Russia

ISBN: (纸本)9781728157429

The paper deals with the problem of input/output system identification in the light of selecting representative outputs of the system under study, accompanied with its application to the analysis of the nuclear power plant efficiency. Features of the approach are based on an extension of the Renyi axioms for measures of dependence to the case of the multivariate dependence and applying an information-theoretic measure of dependence originated from a symmetric Tsallis divergence of probabilistic distributions.

关键词： Automated process control system Hellinger-Tsallis divergence Information task Measures of dependence Multi-input/multi-output description Mutual information Nuclear power plant System identification Technical and economical indexes

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Dynamic-Fusion-Based Federated Learning for COVID-19 Detection

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IEEE INTERNET OF THINGS JOURNAL 2021年第21期8卷 15884-15891页

作者： Zhang, Weishan Zhou, Tao Lu, Qinghua Wang, Xiao Zhu, Chunsheng Sun, Haoyun Wang, Zhipeng Lo, Sin Kit Wang, Fei-Yue China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China CSIRO Data61 Sydney NSW 2015 Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Southern Univ Sci & Technol Inst Future Networks Shenzhen 518055 Peoples R China PCL Res Ctr Networks & Commun Peng Cheng Lab Shenzhen 518066 Peoples R China

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

关键词： Data models COVID-19 Training Collaborative work Analytical models Servers Predictive models AI COVID-19 CT federated learning image processing machine learning X-Ray

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Impact of Circular Field in Underwater Wireless Sensor networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第8期11卷 218-223页

作者： Mohsan, Syed Agha Hassnain Khan, Mushtaq Ali Mahmood, Arfan Akhtar, Muhammad Hammad Amjad, Hussain Islam, Asad Mazinani, Alireza Shah, Syed Muhammad Tayyab COMSATS Univ Islamabad CUI Dept Elect Engn Islamabad Pakistan Zhejiang Univ Ocean Coll Zhoushan Peoples R China Bhanghai Jiao Tong Univ Complex Networks & Control Lab Shanghai Peoples R China Natl Univ Sci & Technol NUST Islamabad Pakistan Beihang Univ BUAA Beijing Peoples R China

Underwater Wireless Sensor networks (UWSNs) face challenges regarding high propagation delay, limited bandwidth, 3D topology and excessive energy consumptions. In this paper, a routing scheme with circular field is proposed for an efficient collection of data packets by using two mobile sinks in UWSNs. Results of proposed scheme are compared with previous implemented schemes which are used to measure the usage of mobile sink in the collection of data packets. In this study, we have compared the proposed scheme with current state-of-the-art routing protocols. The statistical significance of this work was analyzed in MATlab. Marked observations to emerge from obtained results include an improvement in lifetime, increased throughput, increment in alive nodes and balanced energy consumption. In our view, these results strengthen the validity of proposed circular field. A significant increase in received packets is observed because maximum nodes are alive till 1500 rounds which provide maximum communication and less chance of creating void holes.

关键词： Component underwater wireless sensor networks mobile sink routing scheme circlular field

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Reducing Long-Standing Surface Ozone Overestimation in Earth System Modeling by High-Resolution Simulation and Dry Deposition Improvement

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JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2025年第3期17卷

作者： Gao, Yang Kou, Wenbin Cheng, Wenxuan Guo, Xiuwen Qu, Binglin Wu, Yubing Zhang, Shaoqing Liao, Hong Chen, Deliang Leung, L. Ruby Wild, Oliver Zhang, Junxi Lin, Guangxing Su, Hang Cheng, Yafang Poeschl, Ulrich Pozzer, Andrea Zhang, Leiming Lamarque, Jean-Francois Guenther, Alex B. Brasseur, Guy Liu, Zhao Lu, Haitian Li, Chenlin Zhao, Bin Wang, Shuxiao Huang, Xin Pan, Jingshan Liu, Guangliang Liu, Xin Lin, Haipeng Zhao, Yuanhong Zhao, Chun Meng, Junlei Yao, Xiaohong Gao, Huiwang Wu, Lixin Ocean Univ China Frontiers Sci Ctr Deep Ocean Multispheres & Earth Minist Educ Qingdao Peoples R China Ocean Univ China Key Lab Marine Environm Sci & Ecol Minist Educ Qingdao Peoples R China Laoshan Lab Qingdao Peoples R China Ocean Univ China Frontiers Sci Ctr Deep Ocean Multispheres & Earth Qingdao Peoples R China Ocean Univ China Key Lab Phys Oceanog Minist Educ Qingdao Peoples R China Ocean Univ China Coll Ocean & Atmospher Sci Qingdao Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Technol Res Ctr Environm Cleaning Mat Collaborat Innovat Ctr Atmospher Environm & Equipm Sch Environm Sci & EngnJiangsu Key Lab Atmospher Nanjing Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing Peoples R China Univ Gothenburg Dept Earth Sci Gothenburg Sweden Pacific Northwest Natl Lab Atmospher Climate & Earth Sci Div Richland WA USA Univ Lancaster Lancaster Environm Ctr Lancaster England Zhejiang Lab Hangzhou Peoples R China Chinese Acad Sci Inst Atmospher Phys Int Ctr Climate & Environm Sci Beijing Peoples R China Chinese Acad Sci Inst Atmospher Phys Key Lab Atmospher Environm & Extreme Meteorol Beijing Peoples R China Environm & Climate Change Canada Air Qual Res Div Sci & Technol Branch Toronto ON Canada Natl Ctr Atmospher Res Boulder CO USA Univ Calif Irvine Dept Earth Syst Sci Irvine CA USA Max Planck Inst Meteorol Hamburg Germany Natl Supercomp Ctr Wuxi Wuxi Peoples R China Tsinghua Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing Peoples R China State Environm Protect Key Lab Sources & Control Air Pollut Complex Beijing 100084 Peoples R China Nanjing Univ Sch Atmospher Sci Nanjing Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA USA Ocean Univ China Coll Ocean & Atmospher Sci Qingdao Peoples R China Univ Sci & Technol China Sch Earth & Space Sci Hefei Peoples R China

The overestimation of surface ozone concentration in low-resolution global atmospheric chemistry and climate models has been a long-standing issue. We first update the ozone dry deposition scheme in both high- (0.25 degrees) and low-resolution (1 degrees) Community Earth System Model (CESM) version 1.3 runs, by adding the effects of leaf area index and correcting the sunlit and shaded fractions of stomatal resistances. With this update, 5-year-long summer simulations (2015-2019) using the low-resolution CESM still exhibit substantial ozone overestimation (by 6.0-16.2 ppbv) over the U.S., Europe, eastern China, and ozone pollution hotspots. The ozone dry deposition scheme is further improved by adjusting the leaf cuticle conductance, reducing the mean ozone bias by 19%, and increasing the model resolution further reduces the ozone overestimation by 43%. We elucidate the mechanism by which model grid spacing influences simulated ozone, revealing distinctive pathways in urban versus rural areas. In rural areas, grid spacing mainly affects daytime ozone levels, where additional NOx emissions from nearby urban areas result in an ozone boost and overestimation in low-resolution simulations. In contrast, over urban areas, daytime ozone overestimation follows a similar mechanism due to the influence of volatile organic compounds from surrounding rural areas. However, nighttime ozone overestimation is closely linked to weakened NO titration owing to the redistribution of urban NOx to rural areas. Additionally, stratosphere-troposphere exchange may also contribute to reducing ozone bias in high-resolution simulations, warranting further investigation. This optimized high-resolution CESM may enhance understanding of ozone formation mechanisms, sources, and changes in a warming climate.

关键词： high-resolution Earth system model surface ozone simulation ozone bias in global models ozone dry deposition

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