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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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In-Situ 3D Printing Monitoring in Dynamic Environments via Self-Supervised Deep Neural Network Adaptation 20

In-Situ 3D Printing Monitoring in Dynamic Environments via S...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Wang, Fang Xiong, Gang Fang, Qihang Shen, Zhen Wang, Di Wan, Li Dong, Xisong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China Beijing Ten Dimens Technol Co Ltd Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Self-supervised learning

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A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 843267页

作者： Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Chongqing Univ Sch Automation Chongqing Peoples R China Wuhan Second Ship Design & Res Inst Sci & Technol Thermal Energy & Power Lab Wuhan Peoples R China Beihang Univ Sch Automation Sci & Elect Engn Beijing Peoples R China

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

关键词： multifingered hand visual-motor control multi-mode fusion hand structural evolution learning-based manipulation

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Design and Optimization of a control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform

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INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2022年第3期20卷 968-982页

作者： Tamir, Tariku Sinshaw Xiong, Gang Dong, Xisong Fang, Qihang Liu, Sheng Lodhi, Ehtisham Shen, Zhen Wang, Fei-Yue Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Cloud Comp Ctr Guangdong Engn Res Ctr 3D Printing & Intelligent Dongguan 523808 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China

Additive manufacturing, also known as 3D printing, is an emerging technology. The existing additive manufacturing technologies deploy a 3-axis printing mechanism where the material accumulation grows only in the z-direction. This results in limited printing freedom. Apart from this, support structures are needed to print overhang structures. Removal of these supports ultimately reduces print quality. This paper proposes a novel robot-assisted additive manufacturing along with a control system framework, which possesses multi-directional printing without support structures. Taking the advantage of its high stiffness and high payload-to-weight ratio, a 6-degree of freedom Stewart platform manipulator is designed to substitute the printer build plate. The kinematics and dynamics of the manipulator is formulated. Then, an extended proportion-derivation sliding mode controller is designed for trajectory tracking. The modified grey wolf optimization algorithm is applied to compute the optimal controller parameters. The integral absolute error (IAE) is used as a cost function and its minimum value is reached in the iteration interval [75,100]. The analytical model simulation in MATlab is run for 10 seconds, and the results show that the desired length trajectories of the six legs of the manipulator are achieved after 3.5 seconds. The performance of the analytical model is verified on the automated dynamic analysis of mechanical systems (ADAMS).

关键词： Additive manufacturing extended proportion-derivation sliding mode controller grey wolf optimization print quality Stewart platform

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A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment

A deep learning-based computational prediction model for cha...

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Conference on Medical Imaging - Digital and Computational Pathology

作者： Peng, Zhengyao Bian, Chang Du, Yang Tian, Jie Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Sci & Engn Beijing 100191 Peoples R China Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China

ISBN: (数字)9781510649545

ISBN: (纸本)9781510649545;9781510649538

Evaluation of cancer cell and immune cell distribution in tumor microenvironment (TME) is one of the most important factors for guiding cancer immunotherapy and assessing therapeutic response. Multiplexed immunohistochemistry (mIHC) is often used to obtain the different types of cellular biomarker expression and distribution information in TME, but mIHC is limited by time-consuming and cost-intensive, and pathologists' objectives etc. In this work, we proposed a deep learning-based modified U-Net ( m-Unet), by replacing the original convolution sub-module with a modified block to predict the distribution of several typical cellular biomarkers' expression and distribution information in TME. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners. The model can extract segmentation information from Hematoxylin and Eosin (H&E) images, and predict the cellular biomarker distributions including panCK for colon cancer cells, CD3 and CD20 for tumor infiltrating lymphocytes (TILs) and DAPI for nucleus. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners and. the performance of the m-Unet is better than the U-Net in this work. The optimal prediction accuracy of m-Unet is 88.3% on the test dataset. In general, this model possesses the potential to assist the clinical TME analysis.

关键词： tumor microenvironment cellular biomarker deep learning semi-supervised training

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Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第4期18卷 667-671页

作者： Huang, Xiayuan Yang, Qiao Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 10083 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100049 Peoples R China

This letter proposes a lightweight two-stream convolutional neural network (CNN) for synthetic aperture radar (SAR) target recognition. Specifically, the two-stream CNN first extracts low-level features by three alternating convolution layers and max-pooling layers. Then two streams are followed to extract local and global features. One stream uses global maximum pooling to extract local features with the greatest response;the other uses large-stride convolution kernels to extract global features. Finally, the two streams are combined for target recognition. Therefore, the two-stream CNN can learn rich multilevel features to achieve high recognition accuracy for SAR target recognition. Moreover, compared to other popular CNNs, the two-stream CNN is very lightweight. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) data set demonstrate that the proposed method not only can improve the recognition accuracy but also reduce the number of parameters of the model dramatically.

关键词： Feature extraction Synthetic aperture radar Target recognition Convolution Kernel Training Convolutional neural networks Lightweight synthetic aperture radar (SAR) target recognition two-stream convolutional neural network (CNN)

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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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Computational Modeling of Emotion-Motivated Decisions for Continuous control of Mobile Robots

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2021年第1期13卷 31-44页

作者： Huang, Xiao Wu, Wei Qiao, Hong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Beijing Key Lab Res & Applicat Robot Intelligence CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Beijing 100190 Peoples R China

Immediate rewards are usually very sparse in the real world, which brings a great challenge to plain learning methods. Inspired by the fact that emotional reactions are incorporated into the computation of subjective value during decision-making in humans, an emotion-motivated decision-making framework is proposed in this article. Specifically, we first build a brain-inspired computational model of amygdala-hippocampus interaction to generate emotional reactions. The intrinsic emotion derives from the external reward and episodic memory and represents three psychological states: 1) valence;2) novelty;and 3) motivational relevance. Then, a model-based (MB) decision-making approach with emotional intrinsic rewards is proposed to solve the continuous control problem of mobile robots. This method can execute online MB control with the constraint of the model-free policy and global value function, which is conducive to getting a better solution with a faster policy search. The simulation results demonstrate that the proposed approach has higher learning efficiency and maintains a higher level of exploration, especially, in some very sparse-reward environments.

关键词： Decision making Brain modeling Computational modeling Robots Hippocampus Neuroscience Brain-inspired computing decision making emotion– memory interactions emotion-motivated learning reinforcement learning

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Temporal-difference spatial sampling and aggregating graph neural network for crowd flow forecasting 1

Temporal-difference spatial sampling and aggregating graph n...

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

作者： Bu, Xiaojie Wei, Zebing Li, Zhishuai Wang, Xiao Chen, Yuanyuan Song, Yunquan Lv, Yisheng College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China Qingdao Academy of Intelligent Industries Qingdao China College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

With the development of traffic demand management, crowd flow forecasting arouse increasing interest. In order to tackle this spatial-temporal prediction problem, GNN (Graph Neural Networks) have been recently employed to model spatial dependencies, and usually spatial graphs are constructed based on geodetic distances or direct connections between nodes. However, it might be insufficient for spatial graphs to model real dependencies because they neglect spatial-temporal correlations. In this paper, a novel Temporal-Difference Spatial Sampling and Aggregating graph neural network (TDSSA) is proposed to model spatial-temporal dependencies. Firstly, a new sub-sparse spatial-temporal graph is constructed to represent spatial-temporal relationships among different nodes, then a TDSSA block is designed to extract features by spatial sampling and aggregating, and difference is utilized in TDSSA blocks to exploit temporal trend and increase robustness. Experiments show that the proposed method outperforms baseline methods. © 2021 IEEE.

关键词： Graph neural networks

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Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Weiwei Nie, Xiangli Zhang, Bo Sun, Xian Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci AMSS State Key Lab Sci & Engn Comp LSEC Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci AMSS Inst Appl Math Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol NIST Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Image scene classification aiming to assign specific semantic labels for each image is vitally important for the applications of remote sensing (RS) data. In real world, since the observation environment is open and dynamic, RS images are collected sequentially and the numbers of images and classes grow rapidly over time. Most existing scene classification methods are offline learning algorithms, which are inefficient and unscalable for this scenario. In this article, an incremental learning with open-set recognition (ILOSR) framework is proposed for RS image scene classification in the open and dynamic environment, which can identify the unknown classes from a stream of data and learn these new classes incrementally. Specifically, a controllable convex hull-based exemplar selection strategy is designed to address the catastrophic forgetting issue in incremental learning, which can reduce training time and memory footprint effectively. In addition, a new loss function based on prototype learning and uncertainty measurement is proposed for OSR to enhance the interclass discrimination and intraclass compactness of the learned deep features. Experimental results on real RS datasets demonstrate that the proposed method can not only outperform the state-of-the-art approaches on offline classification, incremental learning, and OSR problem separately but also achieve better and more stable performance in the experiments for ILOSR.

关键词： Task analysis Feature extraction Data models Computational modeling Learning systems Training Support vector machines Deep learning incremental learning open-set recognition (OSR) remote sensing (RS) image scene classification

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