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第43届中国控制会议

作者： Huannan Zheng Wei Zhu Ya Deng School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper studies the stability problem of nonlinear systems via hybrid delayed impulses,in which stabilizing impulses and destabilizing impulses can occur simultaneously. In order to analyze the hybrid delayed impulses,the average impulsive weight(AIW) is used. Together with average impulsive interval(AII) and average impulsive delay(AID),some Lyapunov-based conditions for globally asymptotically stable(GAS) are established. Results show that delays in impulses have positive effects to the desired performance. The effectiveness of the theoretical results is demonstrated through two numerical examples.

关键词： Stability Nonlinear systems Hybrid Delayed Impulses

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data-driven evacuation and rescue traffic optimization with rescue contraflow control

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Journal of Safety science and Resilience 2024年第1期5卷 1-12页

作者： Zheng Liu Jialin Liu Xuecheng Shang Xingang Li School of Management Engineering Zhengzhou University of AeronauticsZhengzhou 450046China Collaborative Innovation Center for Aviation Economy Development of Henan Province Zhengzhou University of AeronauticsZhengzhou 450046China School of Systems science Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China School of Transportation Engineering Shandong Jianzhu UniversityJinan 250101China

In response to local sudden disasters,e.g.,high-rise office or residential building fire disasters,road occupation can cause conflicts,and traffic directions may be opposite between evacuation vehicles and rescue vehicles;moreover,lane contraflow can be adopted to meet these surge traffic ***,lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation *** is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic ***,we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue ***,we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic ***,considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes,a three-stage solving algorithm is ***,a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network,and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven sta-tistical *** results show that by adjusting the optimal rescue traffic route,the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance,and can be used to coor-dinate evacuation and rescue traffic operation under rescue contraflow.

关键词： Evacuation and rescue traffic optimization Rescue contraflow control Multiobjective mixed integer linear programming Three-stage solving algorithm data-driven statistical analysis

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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FESSNC: fast exponentially stable and safe neural controller 24

FESSNC: fast exponentially stable and safe neural controller

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Proceedings of the 41st International Conference on Machine Learning

作者： Jingdong Zhang Luan Yang Qunxi Zhu Wei Lin School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems Fudan University China Research Institute of Intelligent Complex Systems and State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China and Shanghai Artificial Intelligence Laboratory China School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems and State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China and Shanghai Artificial Intelligence Laboratory China

In order to stabilize nonlinear systems modeled by stochastic differential equations, we design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning controllers. Our framework is parameterized by neural networks, and realizing both rigorous exponential stability and safety guarantees. Concretely, we design heuristic methods to learn the exponentially stable and the safe controllers, respectively, in light of the classical theory of stochastic exponential stability and our established theorem on guaranteeing the almost-sure safety for stochastic dynamics. More significantly, to rigorously ensure the stability and the safety guarantees for the learned controllers, we develop a projection operator, projecting to the space of exponentially-stable and safe controllers. To reduce the highly computational cost for solving the projection operation, approximate projection operators are delicately proposed with closed forms that map the learned controllers to the target controller space. Furthermore, we employ Hutchinson's trace estimator for a scalable unbiased estimate of the Hessian matrix that is used in the projection operator, which thus allows for reducing computational cost and, therefore, can accelerate the training and testing processes. More importantly, our approximate projection operations are applicable to the nonparametric control methods, improving their stability and safety performance. We empirically demonstrate the superiority of the FESSNC over the existing methods.

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Distributed solver for linear matrix inequalities: an optimization perspective

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Control Theory and Technology 2021年第4期19卷 507-515页

作者： Weijian Li Wen Deng Xianlin Zeng Yiguang Hong Department of Automation University of Science and Technology of ChinaHefei 230027AnhuiChina Key Lab of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a consensus solution in the intersection of all the feasible *** formulation is transformed into a distributed optimization problem by introducing slack variables and consensus ***,by the primal–dual methods,a distributed algorithm is proposed with the help of projection operators and derivative ***,the convergence of the algorithm is analyzed,followed by illustrative simulations.

关键词： Distributed computation Distributed optimization Linear matrix inequalities Primal-dual method

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Natural polyphenols as novel interventions for aging and age-related diseases:Exploring efficacy,mechanisms of action and implications for future research

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Chinese Herbal Medicines 2025年第2期17卷 279-291页

作者： Wenze Wu Yan Mi Qingqi Meng Ning Li Wei Li Pu Wang Yue Hou Liaoning Key Laboratory of Bioresource Research and Development National Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationCollege of Life and Health SciencesNortheastern UniversityShenyang 110169China Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City School of Traditional Chinese Materia MedicaShenyang Pharmaceutical UniversityShenyang 117004China Faculty of Pharmaceutical Sciences Toho UniversityMiyamaFunabashiChiba 274-8510Japan College of Pharmacy Shenzhen Technology UniversityShenzhen 518118China

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent *** far as we know,the studies of natural polyphenols in aging and aging-related diseases have never been extensively *** the present paper,we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural *** results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***,structural modifications,and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related *** conclusion,natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

关键词： aging cancer cardiovascular disease natural polyphenols neurodegenerative diseases

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Study on the Effect of Structural Scale of Hollow Particles and Core-Shell Particles on the Properties of Magnetorheological Fluids

Study on the Effect of Structural Scale of Hollow Particles ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Shun Wang Wenjiao Han Bin He Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody Systems Dynamics Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Sedimentation resistance is undoubtedly the most relevant rheological property of magnetorheological fluids. Typically, researchers use hollowing or coating of magnetic particles to reduce the density difference between the particles and the carrier liquids, thereby improving the anti-sedimentation properties of magnetorheological fluids. However, the treated particles lose their magnetic properties, which in turn reduces the yield stress of magnetorheological fluids. In order to study the effects of coating thickness and hollow radius on yield stress and settlement velocity, the expressions of relative shear stress and relative settlement velocity of particles under the two structures were established based on the two-particle configuration, and the mechanical properties and anti-settlement properties of the two particle structures were compared. It is found that there is a scale range, and when the hollowness and coating thickness of the particles are within this range, the hollow structure not only exhibits better anti-sedimentation but also has less loss of yield stress. In other words, within this scale range, hollow structures are better than coating structures. This research has a theoretical guide for the preparation of high-performance magnetorheological fluids.

关键词： Resistance Fluids Magnetic particles Magnetomechanical effects Magnetic cores Magnetic liquids Coatings

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Classification of Cut-in Intentions at Unsignalized Intersections Based on Electroencephalography data and Adaptive Neuro-Fuzzy Inference System 3

Classification of Cut-in Intentions at Unsignalized Intersec...

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3rd International Conference on Digital Society and Intelligent systems, DSInS 2023

作者： Yu, Jixiang Guan, Wei Qi, Geqi An, Yaning Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China School of Systems Science Beijing Jiaotong University Beijing China Key Laboratory of Brain-Machine Intelligence for Information Behavior- Ministry of Education Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9798350331387

para>Driver decision-making is an outcome of interactions with the traffic environment, and is motivated by specific intentions. At unsignalized intersections with ambiguous right-of-way, accurately and early recognizing driving cut-in intentions could greatly enhance the traffic safety by avoiding conflict-point collisions. Therefore, this paper proposes a method for classifying cut-in intentions at unsignalized intersections using electroencephalography (EEG) data and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The approach involves the collection and preprocessing of EEG data, followed by wavelet packet transformation and Principal Component Analysis (PCA) for dimensionality reduction to derive EEG features. By attenuating the absoluteness of each feature value, ANFIS provides an advantage in processing EEG data for classification. As a result, the proposed model achieves a classification accuracy of 92.14% for driving cut-in intentions, surpassing other commonly used classifiers and indicating its feasibility. © 2023 IEEE.

关键词： Fuzzy inference

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Graph-based Driving Style Recognition from Electrophysiological Analysis during Car Following 3

Graph-based Driving Style Recognition from Electrophysiologi...

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3rd International Conference on Digital Society and Intelligent systems, DSInS 2023

作者： An, Yaning Guan, Wei Qi, Geqi Yu, Jixiang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China School of Systems Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Brain-Machine Intelligence for Information Behavior- Ministry of Education Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9798350331387

para>Driving styles can be used to characterize the behavioral characteristics of drivers, and provide a personalized foundation for the development of intelligent driving systems. The electrophysiological analysis is one of the emerging technologies for driving style recognition. However, most studies focused on classifying driving styles based on electroencephalogram signals have predominantly extracted the time-frequency features, which ignore the spatially coordinated activities of multiple brain regions. Therefore, this paper proposes a graph-based driving style classification method by implementing electrophysiological analysis on brain functional connectivity networks. By adopting graph convolution network on the time-frequency and functional connectivity features, the constructed model attains an accuracy of 88.3% in recognizing three car-following driving styles. This approach can help to promote the electrophysiological recognition of driving styles with a network perspective. © 2023 IEEE.

关键词： Brain

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An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute data

SSRN

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SSRN 2022年

作者： Khuat, Thanh Tung Gabrys, Bogdan Complex Adaptive Systems Laboratory Data Science Institute University of Technology SydneyNSW Australia

General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data. The existing approaches are not suitable for online learning algorithms working in the dynamically changing environments without ability to retrain or access full historical data, which are usually required for many real world applications. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. It uses the change in the entropy values of categorical features of the samples contained in a hyperbox to determine if the current hyperbox can be expanded to include the categorical values of a new training instance. An extended architecture of the original GFMMNN and its new membership function are introduced for mixed-attribute data. Important mathematical properties of the proposed learning algorithms are also presented and proved in this paper. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms forthe GFMM model. © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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