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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture management and control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics

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ECLINICALMEDICINE 2022年 46卷 101380-101380页

作者： Fang, Mengjie Tian, Jie Dong, Di Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst B CAS Key Lab Mol ImagingBeijing Key Lab Mol Imagi 95 Zhongguancun East Rd Beijing 100190 Peoples R China Beihang Univ Sch Engn Med Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing Peoples R China

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Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

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Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1142-1157页

作者： Fei-Yue WANG Jianbo GUO Guangquan BU Jun Jason ZHANG The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Grid Cooperation of China Beijing 100031China China Electric Power Research Institute Beijing 100192China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine *** need for using human-machine HAI in complex systems is then explained in *** concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid *** is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

关键词： complex systems Human-machine knowledge automation Parallel systems Bulk power grid dispatch Artificial intelligence Internet of Minds(IoM)

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AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction

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NEUROCOMPUTING 2021年 445卷 298-308页

作者： Zhou, Hao Ren, Dongchun Xia, Huaxia Fan, Mingyu Yang, Xu Huang, Hai Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin Peoples R China Meituan Beijing Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Predicting pedestrian trajectories in the future is a basic research topic in many real applications, such as video surveillance, self-driving cars, and robotic systems. There are two major challenges in this task, the complex interaction modeling among pedestrians and the unique motion pattern extraction for each pedestrian. Regarding the two challenges, an attention-based interaction-aware spatio-temporal graph neural network is proposed for predicting pedestrian trajectories. There are two components in the proposed method: spatial graph neural network for interaction modeling, and temporal graph neural network for motion feature extraction. Spatial graph neural network uses an attention mechanism to capture the spatial interactions among all the pedestrians at each time step. Meanwhile, temporal graph neural network uses an attention mechanism to capture the temporal motion pattern of each pedestrian. Finally, a time-extrapolator convolutional neural network is used in the temporal dimension of the aggregated graph features to predict the future trajectories. Experimental results on two benchmark pedestrian trajectory prediction datasets demonstrate the competitive performances of the proposed method in terms of both the final displace error and the average displacement error metrics as compared with state-of-the-art trajectory prediction methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Pedestrian trajectory prediction Graph neural networks Spatio-temporal graph Graph attention

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization 26

Towards Integrated Traffic Control with Operating Decentrali...

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

作者： Yao, Shengyue Yu, Jingru Yu, Yi Xu, Jia Dai, Xingyuan Li, Honghai Wang, Fei-Yue Lin, Yilun Shanghai AI Lab Urban Comp Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Res & Dev Ctr Transport Ind Autonomous Driving Te Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

ISBN: (纸本)9798350399462

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.

关键词： Integrated control

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A Survey on Social Manufacturing: A Paradigm Shift for Smart Prosumers

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第5期10卷 2504-2522页

作者： Xiong, Gang Tamir, Tariku Sinshaw Shen, Zhen Shang, Xiuqin Wu, Huaiyu Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Dongguan 523808 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China

The intelligent manufacturing is a complex engineering system, and the cyber-physical systems (CPSs) and the industrial Internet are the preliminary infrastructures. When cyber-physical-social systems (CPSSs) are formed by extending CPS into the social aspect, Societies 5.0 era is coming. In the Societies 5.0 era, social manufacturing (SM) is an innovative manufacturing solution for intelligent manufacturing. In this article, a survey on SM is introduced. It includes the definition and theory of SM, and comparison between SM and other manufacturing paradigms. Moreover, the key supporting technologies are presented, which can be used to realize SM, such as blockchain, 3-D printing, and big data. Then, the applications of SM to industries are illustrated. The SM has broad application prospects in the high-end customized, distributed manufacturing, and other intelligent manufacturing. Finally, the challenges and future trends are discussed.

关键词： Manufacturing Production Industries Market research Solid modeling Big Data Three-dimensional printing 3-D printing cyber-physical-social systems (CPSSs) intelligent manufacturing social manufacturing (SM) Societies 5 0

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A Cable-Driven Hyperredundant Manipulator Obstacle-Avoidance Path Planning and Tension Optimization

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2022年第3期29卷 107-126页

作者： Xu, Dawei Li, En Guo, Rui Liu, Jiaxin Liang, Zize North China Elect Power Univ Dept Automat Baoding 071003 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 Beijing 100190 Peoples R China State Grid Shandong Elect Power Co Elect Power Res Inst Jinan 250000 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 110000 Peoples R China

Manipulators with a hyperredundant and large aspect ratio are becoming more commonly used to detect complex and narrow spaces. The hyperredundant feature confers advantages for such manipulator types in comparison to traditional manipulators. However, they also introduce path-planning challenges. Due to the characteristics of hyperredundancy, there are countless inverse kinematics solutions, and the path-planning method that is used for traditional manipulators cannot be directly applied to these hyperredundancy manipulators in terms of the number of calculations or adaptability of the algorithm.

关键词： Manipulators Trajectory Collision avoidance Robots Optimization Kinematics Propulsion

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Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第10期8卷 4319-4330页

作者： Yang, Qingyuan Ai, Yunfeng Teng, Siyu Gao, Yu Cui, Chenglin Tian, Bin Chen, Long Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Hong Kong Baptist Univ Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Cooperative trajectory planning for autonomous vehicles has garnered significant attention in structured environments, but corresponding methodologies for unstructured environments remains relatively underexplored. The unloading area, an integral component of open-pit mines, exemplifies a quintessential unstructured environment. Implementing cooperative planning for autonomous mining trucks (AMTs) within these unloading areas is crucial as the optimization of processes in these areas substantially enhances the overarching safety, productivity, and cost-effectiveness of mining operations. Hence, enhancing the operational efficiency of AMTs in the unloading area can considerably elevate productivity levels of open-pit mines. This article focuses on the real-time cooperative trajectory planning problem for AMTs in such areas, which is challenging due to i) small and irregular space ii) complex operations iii) need for path stability and speed flexibility. We propose a decoupled multi-vehicle trajectory planning (MVTP) method that decomposes trajectory planning into path planning and speed planning. Specifically, we present driving behavior enhanced path planning and sequential real-time cooperative speed planning methods. Our method is compared with several state-of-the-art MVTP methods and proves to be both secure and efficient.

关键词： Mining industry autonomous automobiles intelligent vehicles

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Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information

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IEEE TRANSACTIONS ON POWER systems 2023年第5期38卷 4432-4446页

作者： Zhao, Pengfei Li, Shuangqi Hu, Paul Jen-Hwa Gu, Chenghong Cao, Zhidong Xiang, Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY England Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Utah David Eccles Sch Business Salt Lake City UT 84112 USA Sichuan Univ Coll Elect Engn Chengdu 610044 Sichuan Peoples R China

The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

关键词： Carbon reduction energy management renewable energy sources water-energy-carbon nexus system

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Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

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IEEE TRANSACTIONS ON NEURAL systems AND REHABILITATION ENGINEERING 2023年 31卷 1019-1029页

作者： Zhang, Chutian Yang, Hongjun Fan, Chen-Chen Chen, Sheng Fan, Chenyu Hou, Zeng-Guang Chen, Jingyao Peng, Liang Xiang, Kexin Wu, Yi Xie, Hongyu Macau Univ Sci & Technol Macau Inst Syst Engn Macau 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 100049 Peoples R China Fudan Univ Huashan Hosp Dept Rehabil Med Shanghai 200040 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Laboratoryof Intelligence Sci & T Macau Peoples R China

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

关键词： Functional near-infrared spectroscopy Three-dimensional displays Feature extraction Convolutional neural networks Electroencephalography Task analysis Fans Convolutional neural networks (CNN) functional near-infrared spectroscopy (fNIRS) mild cognitive impairment (MCI) multi-dimensional feature evaluation multilayer perceptron (MLP)

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