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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Ding, Wen Wen Liang, Xiaolong Hou, Jiachen Wang, Ge Yuan, Yong Li, Junqing Wang, Fei-Yue Institute of Systems Engineering Macau University of Science and Technology China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works. © 2021 IEEE.

关键词： Smart contract

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A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat

arXiv

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arXiv 2022年

作者： Chai, Jiajun Chen, Wenzhang Zhu, Yuanheng Yao, Zong-Xin Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China China

Unmanned combat air vehicle (UCAV) combat is a challenging scenario with continuous action space. In this paper, we propose a general hierarchical framework to resolve the within-vision-range (WVR) air-to-air combat problem under 6 dimensions of degree (6-DOF) dynamics. The core idea is to divide the whole decision process into two loops and use reinforcement learning (RL) to solve them separately. The outer loop takes into account the current combat situation and decides the expected macro behavior of the aircraft according to a combat strategy. Then the inner loop tracks the macro behavior with a flight controller by calculating the actual input signals for the aircraft. We design the Markov decision process for both the outer loop strategy and inner loop controller, and train them by proximal policy optimization (PPO) algorithm. For the inner loop controller, we design an effective reward function to accurately track various macro behavior. For the outer loop strategy, we further adopt a fictitious self-play mechanism to improve the combat performance by constantly combating against the historical strategies. Experiment results show that the inner loop controller can achieve better tracking performance than fine-tuned PID controller, and the outer loop strategy can perform complex maneuvers to get higher and higher winning rate, with the generation evolves. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

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Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton's method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

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Social manufacturing pattern in the intelligent reform of the shoes and clothing industry 1

Social manufacturing pattern in the intelligent reform of th...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Cao, Yansong Wang, Yutong Dai, Juwu Zhu, Xihao Wang, Fei-Yue Macau Institute of Systems Engineering Macau University of Science and Technology China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Ningbo Sanbody Intelligent Technology Co. LTD Ningbo China University of Nottingham Faculty of Science and Engineering Ningbo China

ISBN: (纸本)9781665433372

Along with economic globalization, the shoes and clothing market is undergoing huge changes and is facing increasing individualized demands. To realize personalized customization, we propose a social manufacturing pattern for the shoes and clothing industry. On the customer side, we collect costumer information with the help of 3D scanning and virtual fitting. On the production side, we replace human labor with intelligent production equipment. On the enterprise management side, we construct a cloud platform to realize source maximization. Through this social manufacturing pattern, we expect to accelerate the intelligent reform of the shoes and clothing industry. © 2021 IEEE.

关键词： Shoe manufacture

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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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Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

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2021 China automation Congress, CAC 2021

作者： Yang, Linyao Xu, Yancai Hou, Jiachen Dai, Yuxin Lv, Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426473

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules. © 2021 IEEE

关键词： Embeddings

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RGB-D Co-attention Network for Semantic Segmentation 15th

RGB-D Co-attention Network for Semantic Segmentation

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhou, Hao Qi, Lu Wan, Zhaoliang Huang, Hai Yang, Xu National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China The Chinese University of Hong Kong Hong Kong China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030695248

Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose a co-attention Network (CANet) to capture the fine-grained interplay between RGB’ and D’ features. The key part in our CANet is co-attention fusion part. It includes three modules. At first, the position and channel co-attention fusion modules adaptively fuse color and depth features in spatial and channel dimension. Finally, a final fusion module integrates the outputs of the two co-attention fusion modules for forming a more representative feature. Our extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Modeling human travel and social contact with multi-layer networks for epidemic prediction 9

Modeling human travel and social contact with multi-layer ne...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Duan, Wei Wang, Tao Wang, Peng Ju, Rusheng Wang, Xiao Yang, Tian College of Systems Engineering National University of Defense Technology Changsha City China State Key Laboratry of Complex Systems Management and Control Institute of Automation Chinese Academy of Sciences Beijing City China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha City China

ISBN: (纸本)9780738132020

It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology. © 2021 IEEE.

关键词： Viruses

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

arXiv

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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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Design and attitude control of a novel robotic jellyfish capable of 3D motion

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Science China(Information Sciences) 2019年第9期62卷 182-184页

作者： Junzhi YU Xiangbin LI Lei PANG Zhengxing WU State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Aquatic creatures such as fish, cetaceans, and jellyfish could inspire innovative designs to improve the ways that manmade systems operate in and interact with aquatic environments [1, 2]. Jellyfish in nature use jet propulsion to move through the water, which have been proven to be one of the most energetically efficient swimmers on the planet [3]. Researchers are making an integrated effort to develop smart actuators to fabricate var-

关键词： bar Design and attitude control of a novel robotic jellyfish capable of 3D motion

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