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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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Distributed Observer Design over Directed Switching Topologies

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Wang, Bohui Wang, Jingcheng School of Control Science and Engineering Shandong University Jinan250061 China The School of Aerospace Science and Technology Xidian University Xi’an710071 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

The distributed observer design problem holds significant importance in cases in which the output information of a system is decentralized across different subsystems. Each subsystem has a local observer and access to one part of the measurement outputs and information exchanged through communication networks. This paper focuses on the design of distributed observer with jointly connected directed switching networks. The problem presents challenges due to passive switching modes and the open-loop unboundedness that results from local observability. To overcome these challenges, we develop a network transformation mapping method whereby each local observer can classify itself into an independent subgraph based on independent judgment. Next, an observable decomposition and reorganization method is developed for the digraph case to ensure that each subgraph possesses independent dynamic properties. Asymptotic omniscience is then proven using a developed recursive proof method. This paper includes many previous results as special cases, because most are only suitable for undirected switching topologies or fast-switching cases. An adaptive coupling gain design is proposed to simplify the calculation and verification of conditions that guarantee asymptotic omniscience. Finally, simulation results with the power system show the validity of the developed theory. © 2024, CC BY.

关键词： Continuous time systems

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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NeRF in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Thermoelectric Tracking control of Solid Oxide Fuel Cell System Using Adaptive Dynamic Programming

Thermoelectric Tracking Control of Solid Oxide Fuel Cell Sys...

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Chinese control and Decision Conference, CCDC

作者： Xiao-Long Wu Hongli Liu Hong Zhang Jinting Fu Bin Xie Han Hu Xi Li School of Information Engineering Nanchang University Nanchang Jiangxi School of Artificial and Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan

ISBN: (纸本)9781665478977

An adaptive optimal control strategy for solid oxide fuel cell power generation systems (SOFCs) is proposed in this paper. A new estimator is proposed in a simple form to solve the modeling uncertainty, and express the control of SOFCs as an optimal control problem. Then, a new system thermoelectric parameter tracking control strategy is proposed. The strategy uses data-driven to build the recursive neural network model of the system thermoelectric parameters. In order to ensure convergence, an adaptive law based on parameter estimation error is proposed to update the weights of neural networks online. The simulation results show that the data-driven model can effectively identify the power generation process of solid oxide fuel cell system. At the same time, the control method can realize the optimal tracking control of power, voltage, stack temperature and afterburner temperature with limited input.

关键词： Adaptation models Adaptive systems Fuel cells Process control Optimal control Solids Temperature control

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Knowledge-Based Scene Text Recognition for industrial Applications

Knowledge-Based Scene Text Recognition for Industrial Applic...

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IEEE International Conference on industrial technology (ICIT)

作者： Guowei Deng Jingzheng Tu Cailian Chen Jianping He Xinyi Le Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards’ rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method’s word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively.

关键词： Industries Correlation Text recognition Surveillance Semantics Knowledge based systems Data models

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Robust flocking for non-identical second-order nonlinear multi-agent systems

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Autonomous Intelligent Systems 2021年第1期1卷 81-90页

作者： Xiuxian Li Housheng Su Li Li Department of Control Science and Engineering College of Electronics and Information Engineeringand Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China Institute for Advanced Study Tongji UniversityShanghai 200092China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074China

This paper investigates the robust flocking problem for second-order nonlinear systems with a leader and external *** contrast with most of second-order systems in the literature,the intrinsic dynamics here are nonlinear and non-identical that depend not only on the velocity but also on the position,which is more ***,the interaction topology is undirected and *** that the leader’s velocity may be constant or time-varying,two distributed flocking control laws have been proposed for two cases to make the differences of the velocities between all followers and the leader approach to zero *** proposed distributed flocking control laws are both model-independent which results in the effectiveness of the controllers to cope with the different intrinsic dynamics of the followers and the leader under some assumptions on boundedness of several *** example is given to illustrate the validity of the theoretical results.

关键词： Flocking Multi-agent systems Second-order nonlinear systems Non-identical dynamics

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Online Scheduling in Virtualized TSN Networks via Joint Admission control and VNF Embedding

Online Scheduling in Virtualized TSN Networks via Joint Admi...

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IEEE International Conference on Communications in China (ICCC)

作者： Yajing Zhang Cailian Chen Chaoqun You Mingyan Li Xinping Guan Tony Q. S. Quek Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China School of Computer Science Fudan University College of Computer Science Chongqing University Chongqing P. R. China Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Introducing virtualization enhances the flexibility of time-sensitive networking (TSN), wherein applications manifest as service function chains comprising a series of virtual network functions (VNFs). However, such virtualized TSN realizes determinacy through global configuration, being too complicated to serve dynamic applications in time. To address this issue, we innovatively propose to achieve TSN scheduling by distributively executing admission control (AC), whereby the scheduling complexity is radically reduced. Specifically, we first build a two-way AC model that captures TSN multi-queue characteristics. Then, we define admissible regions of nodes and links, working as metrics for AC decision-making and enabling feasible TSN scheduling. Built upon this, we propose a joint AC and VNF embedding mechanism, Rapid Admission control (RapidAC), which consists of two algorithms. The first algorithm responds to dynamic applications rapidly and derives node-mapping solutions by judging nodes’ admissible regions. Based on this, the second algorithm augments the detailed VNF embedding solution according to admissible regions of links. Simulation results show that RapidAC reduces runtime by 90% compared with existing TSN scheduling algorithms.

关键词： Measurement Runtime Scheduling algorithms Service function chaining Heuristic algorithms Simulation Admission control

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Task Partition-Based Intelligent Offloading for Cache-Assisted Cloud-Edge Cooperation Networks

Task Partition-Based Intelligent Offloading for Cache-Assist...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Haizhen Luo Xiaoping Yang Haofei Xie Fangqing Tan Shu-Ming Tseng Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

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