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control Conference (ANZCC), Australian and New Zealand

作者： Xiao Wang Jun Huang Yong-Lin Tian Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyberphysical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on parallel intelligence. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence, as well as swarm intelligence and collective intelligence. By leveraging these forms of parallel intelligence, the integration of AGI can create more responsive and proactive infrastructure, personalized and efficient services, and foster greater community engagement. The metaverse acts as a platform for simulating real-world scenarios and testing and refining AGI models before deployment in the physical world, while also facilitating the development and coordination of parallel intelligence. However, ethical considerations must be taken into account to ensure responsible deployment and avoid exacerbating existing societal inequalities or infringing upon individual privacy rights. This report provides a comprehensive framework for the integration of AGI and parallel intelligence into metaverse-based digital and smart organizations, while emphasizing the importance of human ethics, social responsibility, and ecological sustainability.

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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Chinese Automation Congress (CAC)

作者： Hongyang Li Qinglai Wei Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method.

关键词： Automation control systems Regulation

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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第35届中国控制与决策会议

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Engineering Laboratory for Intelligent Industrial Vision CAS State Grid Intelligent Technology Co.Ltd. State Grid Shandong Electric Power Company Jinan Power Supply Company

In order to meet the operational needs in some narrow environments,the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of *** present,the research of hyper-redundant manipulator mainly focuses on the straight push *** the limitation of working space,this paper designed a hyper-redundant manipulator delivered by a curled *** to the characteristics of turntable delivery,a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are ***,with the algorithm applied to the physical robot we made,we successfully realized the tipfollowing motion control of the hyper-redundant manipulator.

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Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

arXiv

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arXiv 2024年

作者： Zhao, Yong Zhu, Zhengqiu Gao, Chen Wang, En Huang, Jincai Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers’ limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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Event-Triggered Energy Optimization of Wireless Sensor Networks

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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IEEE International Conference on Networking, Sensing and control

作者： Lu Liu Ruizhuo Song Qinglai Wei School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665472449

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste.

关键词： Wireless sensor networks Energy consumption Adaptive systems System performance Optimal control Optimal scheduling Switches

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Responses of dissolved organic nitrogen to varied nitrogen deposition: A 16-year nitrogen-addition and cessation experiment in a subtropical forest

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Science of the Total Environment 2025年 975卷 179284页

作者： Yang, Dongxing Xie, Dani Mulder, Jan Yu, Qian Huang, Yongmei Duan, Lei State Key Laboratory of Regional Environment and Sustainability School of Environment Tsinghua University Beijing100084 China Shanxi Key Laboratory of Land Consolidation School of Land Engineering Chang'an University Shanxi 710064 China Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Box 5003 ÅsNO-1432 Norway State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment Nanjing University 163 Xian in Ave. Jiangsu Nanjing210023 China Faculty of Geographical Science Beijing Normal University Beijing100875 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Tsinghua University Beijing100084 China

The leaching of dissolved organic nitrogen (DON) signals N loss in forest ecosystems, connecting terrestrial and aquatic ecosystems. The response of DON to varied inorganic N (Nin) deposition remains unclear. A 16-year continuous monitoring of DON in throughfall, soil water, and stream water was conducted under a field N manipulation (10-year Nin addition and subsequent cessation of Nin addition) in a subtropical forest in China. Under reference conditions, the average organic N (Nor) deposition was 23.0 ± 4.9 kg N ha−1 yr−1, with approximately one-third leaching at a soil depth of 30 cm, a value that was higher than those reported for temperate forests. Over a 16-year period, DON concentrations tended to increase in throughfall, soil solutions and stream water. The long-term increase in DON concentrations in soil solutions was primarily driven by increasing Nor deposition and indirectly influenced by decreasing acid deposition. Due to a doubling of monthly Nin input, either as sodium nitrate or as ammonium nitrate, the soil DON leaching doubled, causing the soil to transition from a DON sink to a DON source. After Nin addition ceased, the DON leaching returned to natural levels, and the soil reverted to being a DON sink. This study reveals the importance of Nor deposition and decreasing acid deposition as drivers of long-term trends in DON concentrations in soil solutions, in addition to elucidating the response patterns of DON to increasing and decreasing Nin deposition. © 2025 Elsevier B.V.

关键词： Chronic nitrogen addition Dissolved organic nitrogen Nitrogen deposition Soil acidification recovery Water chemistry

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Data-Driven Learning and control with Event-Triggered Measurements

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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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Improved Interpolation with Sub-Pixel Relocation Method for Strong Barrel Distortion

SSRN

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SSRN 2022年

作者： Yuwen, Xuan Zhang, Silong Chen, Long Zhang, Hui School of Transportation Science and Engineering Beihang University Beijing102206 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we propose an improved interpolation method with sub-pixel relocation method to correct fisheye images with strong barrel distortion. The proposed method takes as inputs the fisheye image and the projection model, and outputs the corrected perspective image. In the method, we project the fisheye image pixels to perspective image plane using the backward projection. The projected pixels are treated as scatter data and their location is refined by the sub-pixel relocation method. The method is designed to aim at the subpixel bias phenomenon of the projected *** sub-pixel relocation method relocates the projected pixel centers to the centers of the projected pixels. Then the relocated points are meshed by the Voronoi diagram method to select the neighbors of the interpolation points. The weights of the neighbors are calculated using the inverse distance method. The index of the neighbor and their weights are stored in a look-up-table structure for reutilization. For each fisheye image, the proposed method can quickly find the neighbors of the interpolation points and the weight of each neighbor through the look-up-table. Test results illustrate that the proposed method achieves the best accuracy among the tested method for fisheye images with strong barrel distortion. © 2022, The Authors. All rights reserved.

关键词： Interpolation

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Talent Prediction Based on Switching Strategy Algorithm of Residual Grey Model

Talent Prediction Based on Switching Strategy Algorithm of R...

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Chinese control and Decision Conference, CCDC

作者： Yuxin Pan Xihong Fei Bo Shan Hao Shen Jing Wang Kang Wang School of Electrical and Information Engineering Anhui University of Technology Ma’Anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’Anshan China Instrumentation Technology and Economy Institute Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The surge in demand for medical equipment necessitates accurate prediction of talent demand in the medical industry, becoming a crucial support for the strategic planning of talent and human resources in universities. However, the difficulty in effectively collecting talent data in the medical equipment industry results in limited availability of talent demand data. At the same time, existing research focuses only on the linear or nonlinear single relationship of talent demand sequence data, thereby reducing the accuracy and robustness of predictions. In this paper, we propose a Switching Strategy Algorithm based on Residual Grey Model (SSA-RGM) to address the above issue. SSA-RGM is a hybrid model that includes a novel residual grey model, commonly used simple linear regression model, and a switching strategy based on optimal fitness. In this model, we introduce a residual operation based on the grey model and, for the first time, incorporate a penalty factor to mitigate the impact of linear shifts in small-sample data within nonlinear systems. Furthermore, we adopt the principle of optimal fitness to select the best model in the hybrid model. Experimental results on six real-world datasets show that SSA-RGM achieves the best talent prediction performance in various settings.

关键词： Industries Biomedical equipment Accuracy Switches Predictive models Strategic planning Prediction algorithms

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