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Can Digital Intelligence and Cyber-Physical-Social systems Achieve Global Food Security and Sustainability?

IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Target tracking algorithm with adaptive learning rate complementary filtering

Target tracking algorithm with adaptive learning rate comple...

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第三十九届中国控制会议

作者： Yulei Pan Yongqiang Bai Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations such as motion blur and lighting changes. A tracking algorithm based on color statistical features has a good effect on the rotation and translation of *** Staple algorithm combines the two algorithms to track using complementary fusion, but it also does not handle the occlusion and other issues well. In this paper, based on the Staple algorithm, the average peak correlation energy（APCE） and the maximum response are introduced. The value is used as the tracking confidence, and a detector using a support vector machine（SVM） is *** the tracking confidence is low, the target is blocked or moved violently. At this time, the detector works,and the search area is expanded around the original area for the target. At the same time, because the traditional tracking algorithm uses a fixed learning rate to update the template, this paper uses an adaptive tracking learning rate. When the tracking confidence is low, the update speed of the target model is reduced, which can effectively deal with the occlusion deformation in the tracking process. OTB100 benchmark experiments show that this method can solve the occlusion problem during target tracking. The degree of change is robust and stability.

关键词： Target Tracking Complementary Filtering Re-detection Adaptive Learning Rate

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Event-triggered attitude tracking for rigid spacecraft

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Science China(Information Sciences) 2019年第12期62卷 178-193页

作者： Deheng CAI Hengguang ZOU Junzheng WANG Yuan HUANG Dawei SHI State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology China Academy of Space Technology

This study aims to investigate the problem of attitude control for a spacecraft with inertial uncertainties, external disturbances, and communication restrictions. An event-triggered active disturbance rejection control approach is proposed for attitude tracking of the spacecraft. An event-triggered mechanism is introduced together with an extended state observer to jointly monitor the system states and total disturbances. The observation error is proved to be uniformly bounded. Based on the proposed control scheme,the integrated tracking system is shown to be asymptotically stable, implying successful attitude tracking of the spacecraft for the desired motion. Numerical results illustrate the effectiveness of the control strategy in achieving satisfactory tracking performance with a reduced data-transmission cost.

关键词： spacecraft attitude tracking active disturbance rejection control event-triggered control extended state observer

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Cooperative Search Strategy of Multiple UAVs Based on Matrix Game 19

Cooperative Search Strategy of Multiple UAVs Based on Matrix...

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19th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2022

作者： Song, Dalei Wang, Xiaoqian Dai, Aoliang Ma, Hongjun Wei, Donghui Shenyang University of Technology School of Information Science and Engineering Shenyang China Northeastern University School of Information Science and Engineering Shenyang China South China University of Technology School of Information Science and Engineering Guangdong China Beijing Research Institute of Mechanical & Electrical Technology Key Laboratory of Complex System Control and Intelligent Collaboration Technology Beijing100074 China

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

For the regional search problem of multiple unmanned air vehicles (UAVs) ,a cooperative search strategy of multiple UAVs is proposed based on action matrix game (AMG) for intelligent moving targets on the ground in this paper. Based on the proposed AMG, the yaw angle and position information of the target is described in probability matrix, and the matrix is updated by Nash equilibrium and receding horizon control in every control cycle. Simulation results show that this strategy deduces the searching time for revisiting the reference target, and improves the success rate for moving targets searching. © 2022 IEEE.

关键词： Game theory

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Distributed stochastic projection-free solver for constrained optimization

arXiv

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arXiv 2022年

作者： Jiang, Xia Zeng, Xianlin Xie, Lihua Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The School of Electronic and Information Engineering Tongji University Shanghai200082 China

This paper proposes a distributed stochastic projection-free algorithm for large-scale constrained finite-sum optimization whose constraint set is complicated such that the projection onto the constraint set can be expensive. The global cost function is allocated to multiple agents, each of which computes its local stochastic gradients and communicates with its neighbors to solve the global problem. Stochastic gradient methods enable low computational cost, while they are hard and slow to converge due to the variance caused by random sampling. To construct a convergent distributed stochastic projection-free algorithm, this paper incorporates a variance reduction technique and gradient tracking technique in the Frank-Wolfe update. We develop a sampling rule for the variance reduction technique to reduce the variance introduced by stochastic gradients. Complete and rigorous proofs show that the proposed distributed projection-free algorithm converges with a sublinear convergence rate and enjoys superior complexity guarantees for both convex and non-convex objective functions. By comparative simulations, we demonstrate the convergence and computational efficiency of the proposed algorithm. © 2022, CC BY.

关键词： Stochastic systems

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-Agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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Distributed Cooperative decision-Making of Cross-Domain Swarm

Distributed Cooperative Decision-Making of Cross-Domain Swar...

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Chinese Automation Congress (CAC)

作者： Xingyu Liu Cheng Yuwen Wangcheng Zhang Mingyang Sun Ding Jian Ziyang Zhen State Key Laboratory of Mechanics and Control for Aerospace Structures College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China CASIC Research Institute of Intelligent Decision Engineering Wuhan China UAV System Company Limited Aviation Industry Corporation of China Chengdu China

This paper investigates a cooperative decision-making problem of the cross-domain swarm system involving unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). Different from the distributed optimization of multi-agent systems, the cooperative decision-making problem considers the payoff functions that depend on the states of all agents. Each agent can only adjust its own state. To address the cooperative decision-making problem, a two-time scale system is constructed, where the fast model includes an average consensus for gradient estimation of the global function and the slow model incorporates a feedback control of all USVs and UAVs. By the Lyapunov analysis, the optimal solution is globally exponentially stable. Finally, a cooperative encirclement task is utilized to verify the effectiveness of the proposed algorithm.

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson 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 Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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