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Data-Driven Learning and control with Event-Triggered Measurements

IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Enabling Stateful TCP Performance Profiling with Key Event Capturing

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IEEE Transactions on Network and Service Management 2025年

作者： Geng, Ruopeng Lu, Jianyuan Fang, Chongrong Zhang, Shaokai Zhao, Jiangu Zong, Zhigang Lyu, Biao Zhu, Shunmin Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Alibaba Group Hangzhou310052 China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSights overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the server's load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment. © 2004-2012 IEEE.

关键词： Information management

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ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments

arXiv

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arXiv 2025年

作者： Xin, Jinghao Liang, Zhichao Zhang, Zihuan Wang, Peng Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly to control commands, thereby ensuring compatibility with unstructured environments. Under this formulation, a novel network, Transqer, is introduced. The Transqer enables online DRL learning from temporal transitions, substantially enhancing decision-making in dynamic scenarios. To facilitate scalable training of Transqer with diverse data, an efficient simulation platform E-Sparrow, along with a data augmentation technique leveraging symmetric invariance, are developed. Comparative evaluations against state-of-the-art methods, alongside assessments of generalizability, scalability, and real-time performance, were conducted to validate the effectiveness of ColorDynamic. Results indicate that our approach achieves a success rate exceeding 90% while exhibiting real-time capacity (1.2–1.3 ms per planning). Additionally, ablation studies were performed to corroborate the contributions of individual components. Building on this, the OkayPlan-ColorDynamic (OPCD) navigation system is presented, with simulated and real-world experiments demonstrating its superiority and applicability in complex scenarios. The codebase and experimental demonstrations have been open-sourced on our website1 to facilitate reproducibility and further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Average Filtering Error-Based Event-Triggered Fuzzy Filter Design with Adjustable Gains for Networked control systems

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IEEE Transactions on Fuzzy systems 2025年

作者： Pan, Yingnan Huang, Fan Li, Tieshan Lam, Hak-Keung Qingdao University School of Automation Shandong Qingdao266071 China Bohai University College of Control Science and Engineering Liaoning Jinzhou121013 China University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China King's College London Strand Department of Engineering LondonWC2R 2LS United Kingdom

This paper investigates the event-triggered H∞ filtering problem for interval type-2 fuzzy networked control systems with data loss. The goal of this paper is to design an event-triggered fuzzy filter with adjustable gains to further improve the H∞ performance of the filtering error system (FES) while saving communication resources. Firstly, an adaptive event-triggered mechanism (ETM) that utilizes the average filtering error to adaptively adjust the triggered threshold is proposed to mitigate network congestion. In contrast to the existing filtering achievements, the proposed adaptive ETM considers the impact of filtering error on data transmission which can achieve the effect of saving more communication resources. Then, an interval stability theory is applied to adjust the filter gains for achieving the purposes of changing the convergence speed of the FES and improving the Hinfty performance. Moreover, under the conditions of imperfect communication links, sufficient criteria for designing the H∞ filter with adjustable gains are obtained based on the interval stability theory and linear matrix inequalities technique. Finally, the simulation results are utilized for demonstrating the feasibility of the proposed method. © 1993-2012 IEEE.

关键词： Fuzzy filters

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Modeling and Calculation of Core Loss and Heat Distribution in Ultra-Thin Wound Core Reactor Based on Electromagnetic and Fluid-Thermal Coupling

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IEEE Transactions on Magnetics 2025年

作者： Yuan, Jiaxin Li, Xuzhe Zhou, Hang Muramatsu, Kazuhiro State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and Automation Wuhan University Wuhan430072 China School of Electrical Engineering and Automation Wuhan University Wuhan430072 China Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Wuhan University Wuhan China Department of Electrical and Electronic Engineering Saga University Saga840-8502 Japan

The anode saturable reactor (ASR) is a critical component in converter valves for protecting thyristors. In recent years, fault issues caused by abnormal heating have received widespread attention. However, the complex structure of the ASR presents a significant challenge for accurate temperature rise calculations. This study focuses on an ETT-type ASR and establishes a comprehensive electromagnetic-thermal-flow simulation model. The improved Bertotti loss separation model is applied to calculate core losses, and time-averaged losses are used as the heat source coupled to the temperature field to address the mismatch between the time scales of losses and temperature rise. The temperature distribution of multiple core pairs in the ASR is solved based on a fluid-thermal coupling method and compared with three other methods. Experimental results show that under normal operating conditions, the core temperature of the ASR is approximately 75°C, with slight temperature differences observed across the cores. The proposed heat calculation model provides more accurate results than other models. © 2015 IEEE.

关键词： Saturable core reactors

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Enhancing the performance of CaTiO3-GDC heterostructure electrolytes in LT-CFCs

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Ceramics International 2025年第15期51卷 20131-20142页

作者： Li, Dongchen Cui, Yingying Hong, Xiaofang Shah, M.A.K. Yousaf Mushtaq, Naveed Rauf, Sajid Arshad, Naila Irshad, Sultan Alodhayb, Abdullah N. Yousaf, Muhammad Lu, Yuzheng Department of Electrical and Automation Shandong Labor Vocational and Technical College Jinan250300 China Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and Environment Southeast University No.2 Si Pai Lou Nanjing210096 China College of Mechatronics and Control Engineering Shenzhen University Guangdong Province 518000 China School of Physics Electronics and Intelligent Manufacturing Huaihua University Hunan Huaihua418008 China College of Electronics and Information Engineering Shenzhen University Guangdong Province 518000 China Department of Physics and Astronomy College of Science King Saud University Riyadh11451 Saudi Arabia College of Chemistry and Environmental Engineering Shenzhen University Guangdong Province Shenzhen518055 China School of Electronic Engineering Nanjing Xiaozhuang University Nanjing211171 China

Designing ceramic fuel cells (CFCs) with fast ionic conduction and superior electrochemical performance at low temperatures is critical for advancing hydrogen applications. Here, we developed a heterostructure electrolyte by combining the ionic conductor Gd0.1Ce0.9O2 (GDC) with the semiconductor material CaTiO3 (CTO) to shape into the composite heterostructure (semiconductor ionic heterostructure) CTO-GDC. The designed heterostructure with different fermi levels established the depletion zone due to the redistribution of charges at the interface, leading to enhancement of the ionic conduction;meanwhile, the established built-in electric field suppresses the electronic conduction speed of the ion's transportation at the interface of the constructed heterostructure (CTO-GDC). The formation of heterostructure introduces a number of oxygen vacancies, as confirmed via x-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR), benefiting the transportation of ions in the composite heterostructure. The prepared composite heterostructure shows the highest power output of 810 mW/cm2 at a low operational temperature of 520 °C. The CTO-GDC shows high ionic conductivity of 0.17 S cm−1 at 520 °C and low activation energy of 0.785 eV at 520-420 °C. The composite containing a 50 % molar ratio of GDC and CTO demonstrated exceptional performance by successfully inhibiting electron transit, improving ionic conductivity, and achieving outstanding power output. The enhanced ionic conductivity with low activation energy of composite heterostructure suggests that designing a heterostructure seems an efficient approach to fuel cell application. © 2025

关键词： Paramagnetic resonance

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Pathological axonal enlargement in connection with amyloidosis,lysosome destabilization,and bleeding is a major defect in Alzheimer’s disease

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Neural Regeneration Research 2026年第2期21卷 790-799页

作者： Hualin Fu Jilong Li Chunlei Zhang Guo Gao Qiqi Ge Xinping Guan Daxiang Cui Institute of Nano Biomedicine and Engineering School of Sensing Science and EngineeringSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghaiChina National Center for Translational Medicine Shanghai Jiao Tong UniversityShanghaiChina Department of Automation Shanghai Jiao Tong UniversityShanghaiChina The Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina

Alzheimer’s disease is a multi-amyloidosis disease characterized by amyloid-βdeposits in brain blood vessels,microaneurysms,and senile *** amyloid-βdeposition affects axon pathology has not been examined *** used immunohistochemistry and immunofluorescence staining to analyze the forebrain tissue slices of Alzheimer’s disease *** axonal amyloidosis with distinctive axonal enlargement was observed in patients with Alzheimer’s *** average,amyloid-β-positive axon diameters in Alzheimer’s disease brains were 1.72 times those of control brain ***,axonal amyloidosis was associated with microtubule-associated protein 2 reduction,tau phosphorylation,lysosome destabilization,and several blood-related markers,such as apolipoprotein E,alpha-hemoglobin,glycosylated hemoglobin type A1C,and *** destabilization in Alzheimer’s disease was also clearly identified in the neuronal soma,where it was associated with the co-expression of amyloid-β,Cathepsin D,alpha-hemoglobin,actin alpha 2,and collagen type *** suggests that exogenous hemorrhagic protein intake influences neural lysosome ***,the data showed that amyloid-β-containing lysosomes were 2.23 times larger than control ***,under rare conditions,axonal breakages were observed,which likely resulted in Wallerian *** summary,axonal enlargement associated with amyloidosis,micro-bleeding,and lysosome destabilization is a major defect in patients with Alzheimer’s *** finding suggests that,in addition to the well-documented neural soma and synaptic damage,axonal damage is a key component of neuronal defects in Alzheimer’s disease.

关键词： Alzheimer’s disease amyloid-β amyloidosis axonal enlargement hemoglobin hemorrhage lysosome destabilization neuropil thread tau Wallerian degeneration

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

引用

Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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Sharing energy in wider area: A two-layer energy sharing scheme for massive prosumers

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Applied Energy 2025年 392卷

作者： Su, Yifan Yang, Peng Kang, Kai Wang, Zhaojian Qi, Ning Liu, Tonghua Liu, Feng State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing400044 China School of Electronics and Information Xi'an Polytechnic University Xi'an710048 China State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States

The popularization of distributed energy resources transforms end-users from consumers into prosumers. Inspired by the sharing economy principle, energy sharing markets for prosumers are proposed to facilitate the utilization of renewable energy. This paper proposes a novel two-layer energy sharing market for massive prosumers, which can promote social efficiency by wider-area sharing. In this market, there is an upper-layer energy sharing market (U-ESM) in the distribution system and numerous lower-layer energy sharing markets (L-ESMs) in local communities. Prosumers in the same community share energy with each other in the L-ESM, which can be uncleared. The energy surplus and shortage of L-ESMs are cleared in the U-ESM. Thanks to the wider-area two-layer structure, the market outcome is near-social-optimal in large-scale systems. However, the proposed market forms a complex mathematical program with equilibrium constraints (MPEC). To solve the problem, we propose an efficient and hierarchically distributed bidding algorithm. The proposed two-layer market and bidding algorithm are verified on the IEEE 123-bus system with 11,250 prosumers, which demonstrates the practicality and efficiency for large-scale markets. © 2025 Elsevier Ltd

关键词： Game theory

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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