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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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Optimal synchronization control formulti-agent systems with input saturation:a nonzero-sum game

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Frontiers of Information Technology & Electronic Engineering 2022年第7期23卷 1010-1019页

作者： Hongyang LI Qinglai WEI School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a novel optimal synchronization control method for multi-agent systems with input *** multi-agent game theory is introduced to transform the optimal synchronization control problem into a multi-agent nonzero-sum ***,the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman(HJB)equations with nonquadratic input energy terms.A novel off-policy reinforcement learning method is presented to obtain the Nash equilibrium solution without the system models,and the critic neural networks(NNs)and actor NNs are introduced to implement the presented *** analysis is provided,which shows that the iterative control laws converge to the Nash *** results show the good performance of the presented method.

关键词： Optimal synchronization control Multi-agent systems Nonzero-sum game Adaptive dynamic programming Input saturation Off-policy reinforcement learning Policy iteration

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Multilevel Heterogeneous Domain Adaptation Method for Remote Sensing Image Segmentation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Liang, Chenbin Cheng, Bo Xiao, Baihua Dong, Yunyun Chen, Jinfen Shaanxi Normal Univ Northwest Land & Resources Res Ctr Xian 710119 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Aerosp Informat Res Inst Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Due to more abundant data sources, more various objects of interest, and more time-consuming annotations, there is a large amount of out-of-distribution (OOD) data in the remote sensing field, on which the performance of high-accuracy image segmentation models trained under ideal experimental conditions generally degrades dramatically. Domain adaptation (DA) consequently comes into being, which aims to learn the predictor for the label-scarce target domain of interest with the help of the label-sufficient source domain in the presence of the distribution difference, namely, domain shift, between the two domains. However, the off-the-shelf DA methods for image segmentation not only struggle to cope with the more complex domain shift problems in remote sensing imagery but also almost cannot process heterogeneous data directly without information loss. While the current heterogeneous DA methods mostly still rely on some supervision information from the target domain, which is typically inaccessible in the real world. To overcome these drawbacks, we propose the multilevel heterogeneous unsupervised DA (UDA) method, termed MHDA, which unifies the instance-level DA based on cycle consistency, the feature-level DA based on contrastive learning, and the decision-level DA based on task consistency into a framework to more effectively handle the complex domain shift and heterogeneous data. After that, extensive DA experiments are conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) dataset, the BigCity dataset constructed by ourselves, and the Wuhan University (WHU) dataset, to explore the effect of each module in MHDA, the necessity of heterogeneous DA, and the effectiveness of multilevel DA. And the results demonstrate that MHDA can achieve superior performance on the remote sensing image segmentation task, compared with several state-of-the-art DA methods.

关键词： Remote sensing Image segmentation Task analysis Sensors Adaptation models Training Spatial resolution Contrastive learning cycle consistency domain adaptation (DA) semantic segmentation task consistency

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A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random *** a result,pedestrian trajectory prediction is rather *** existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many *** this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian *** RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling *** temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different *** spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current *** randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random *** conduct extensive experiments on several public *** results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model trajectory prediction

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TAO-Based Data Governance in Intelligent Transportation systems 26

TAO-Based Data Governance in Intelligent Transportation Syst...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Liang, Xiaolong Li, Juanjuan Qin, Rui Ding, Wenwen Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Collaborat Lab Intelligent Sci & Syst Beijing 100190 Peoples R China

ISBN: (纸本)9798350399462

Intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

关键词： Intelligent Transportation systems True Autonomous Organization Parallel Intelligent Mechanism Design Parallel Governance

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Parallel Intelligence: Belief and Prescription for Edge Emergence and Cloud Convergence in CPSS

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第5期7卷 1105-1110页

作者： Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Welcome to the new issue of IEEE Transactions on Computational Social systems (TCSS). First of all, on behalf of the Board of Governors and Prof. Enrique Herrera Viedma, Vice President for Publication, of the IEEE SMCS, I would like to announce and introduce the new Editor-in-Chief of the IEEE TCSS, Prof. Bin Hu, our Associate Editor since 2017 and a member of BoG since 2018. Currently, Bin Hu is the Director of the Gansu Provincial key laboratory of Wearable Computing, Lanzhou University, Lanzhou, China, and an Adjunct Professor with the Computing Department, The Open University, Milton keynes, U.K. He is the Chair of the IEEE SMC Technical Committee on Computational Psychophysiology at IEEE SMC and the Vice- Chair of the China Committee of the International Society for Social Neuroscience. He is also serving as an Associate Editor for IEEE Transaction on Affective Computing. I am sure that IEEE TCSS will march to a new level of excellence under Prof. Bin Hu’s leadership. Congratulations to him and TCSS for the beginning of a new chapter!

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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2022 Chinese Automation Congress, CAC 2022

作者： Si, Zeze Zhao, Hongxia Tang, Haina Zhu, Fenghua Lv, Yisheng Lu, Hao Institute of Autamation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266000 China

ISBN: (纸本)9781665465335

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction. © 2022 IEEE.

关键词： Long short-term memory

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Improved interpolation with sub-pixel relocation method for strong barrel distortion

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SIGNAL PROCESSING 2023年 203卷

作者： Xuan, Yuwen Zhang, Silong Chen, Long Zhang, Hui Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R 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 sub-pixel bias phenomenon of the projected pixels. The sub-pixel bias phenomenon is that there is a sub-pixel bias between the projected pixel center point and the center point of the projected pixel. This phenomenon is caused by the uneven distortion of the fisheye image plane, and it will af-fect the accuracy of the interpolation method. The 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. The mesh is used to select the neighbors of the interpolation points as natural neigh-bor selection method. The weights of the neighbors are calculated using the inverse distance method. The indexes of the neighbors and their weights are stored in a look-up-table structure for reuse. 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, and the perspective image can be calculated in real-time. Test results illustrate that the proposed method achieves the best accuracy among the tested methods for fisheye images with strong barrel distortion.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Fisheye image correction Interpolation Voronoi diagram Sub-pixel

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A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 8078-8088页

作者： Wang, Fang Xiong, Gang Fang, Qihang Shen, Zhen Wang, Di Dong, Xisong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Dongguan 523808 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and struggles to effectively handle imbalanced defect data. In this article, we propose a defect detection method called dual neural network, which involves detecting defects in materials before the printing process to prevent resource wastage and serious consequences. Specifically, to handle the highly imbalanced class distribution problem in online DLP defect detection, dual neural network utilizes a domain learner and balance learner to effectively balance the information of the minority class and learn the generalization knowledge from the imbalanced defect dataset. Experimental results demonstrate the effectiveness of our proposed method, which has also been applied to real-world production equipment successfully.

关键词： Defect detection Monitoring Neural networks Cameras Training Optical imaging Optical interferometry digital light processing (DLP) imbalanced learning

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Storm surge damage interpretation by satellite imagery: case review

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NATURAL HAZARDS 2022年第1期112卷 349-365页

作者： Liu, Xunan Zhang, Yao Liang, Chenbin Yang, Yayu Huang, Wanru Jia, Ning Cheng, Bo Minist Nat Resources Natl Marine Hazard Mitigat Serv Beijing 100000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100000 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100000 Peoples R China

Rapid response and restoration after storm surge events are critical for the resilient development of coastal areas. Remote sensing has become a practical tool for postdisaster impact assessment since the modern satellite era. This paper presents the methodology and case review of damage detection using high-resolution satellite imagery for vulnerable targets with limited accessibility on land or water during China's recent typhoon landfalls. Multiple sources of remotely sensed data are acquired and processed in a broad geographic scope. The study demonstrates how storm damage may be explicitly revealed by interpreting the texture difference or reflectance spectrum contrast for various target types, including flotation facilities, green vegetation, sandy coast and coastal protections. The damage state in the same area may show rapid variations in space due to local differences in the shielding effectiveness with respect to the storm track. The technical features, advantages, and drawbacks of remote sensing applications to marine disaster assessment are discussed from a future perspective. The study results provide a comprehensive reference to better integrate satellite observations into postdisaster surveys, vulnerability analyses, and risk predictions for coastal communities that will withstand future marine hazards.

关键词： Storm surge Damage assessment Remote sensing NDVI Texture extraction

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