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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xiaodong Zhang Bin Hu Gang Xiong Xinjie Liu Xisong Dong Dongjia Li School of Information Engineering Yantai institute of Technology Yantai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Yantai University Yantai China School of Artificial Intelligence Yantai Institute of Technology Yantai China

Parallel manufacturing in Industry 5.0 requires digital twin to digitize physical systems, building virtual models to open up channels connecting physical systems, information systems, and social systems, and transforming the physical models of the existing production environment to achieve two-way feedback of virtual and real is the current research direction. This paper proposes the modeling idea of lightweight digital twin, extracts core dimensions and performs digital virtual simulation, so as to quickly realize the complete process of two-way feedback, and realize a set of chess flexible parallel manufacturing production lines as a practice for the design of complete lightweight digital twin.

关键词： Industries Digital twin Conferences Buildings Production Manufacturing Flexible manufacturing systems

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Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment

arXiv

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arXiv 2017年

作者： Lu, Yan-Feng Jia, Li-Hao Qaio, Hong Li, Yi Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China School of Information Engineering Nanchang University Nanchang China

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian-Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD), and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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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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Acp-Net: Asymmetric Center Positioning Network for Real-Time Text Detection

SSRN

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SSRN 2024年

作者： Zhu, Boyuan Liu, Fagui Chen, Xi Tang, Quan Chen, C.L. Philip South China University of Technology No.342 Outer Ring East Road Panyu Guangzhou510006 China Peng Cheng Laboratory No. 2 Xingke 1st Street Nanshan Shenzhen518055 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No.95 Zhongguancun East Road Haidian Beijing100190 China

Scene text detection plays a crucial role in numerous application fields. However, despite the varying focus on real-time performance, almost all existing detection models employ the Feature Pyramid Network (FPN) structure for feature extraction, overlooking its inherent limitations. The integration of high-resolution multi-channel features within FPN demands significant computational resources. Although FPN which treats local and global features as equivalent, has been proven to be stable in various fields, it remains to be reconsidered whether this stability strategy is the most suitable for text features. To this end, we propose an Asymmetric Center Positioning Network (ACP-Net), which replaces the FPN structure and achieves precise and real-time text detection in complex scenarios. Specifically, we explore an asymmetric feature structure that includes two independent branches to acquire both global and local information, as well as an adaptive weighted fusion module to effectively capture the long-range dependencies from these two branches. Furthermore, we present a text center positioning module to enhance the understanding of text features by learning the feature centers. Comprehensive assessments conducted across various terminals have demonstrated the superiority of ACP-Net in terms of both accuracy and speed. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Discrete-Time Generalized Policy Iteration ADP Algorithm With Approximation Errors

Discrete-Time Generalized Policy Iteration ADP Algorithm Wit...

引用

IEEE Symposium Series on Computational Intelligence

作者： Qinglai Wei Benkai Li Ruizhuo Song The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Generalized policy iteration Nonlinear systems Optimal control Neural networks Reinforcement learning

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Estimation of Reduced Greenhouse Gas Emission from Municipal Solid Waste Incineration with Electricity Recovery in Prefecture- and County-Level Cities of China

SSRN

引用

SSRN 2022年

作者： Zhao, Qing Tang, Weihao Han, Mengjie Cui, Wenjing Zhu, Lei Xie, Huaijun Li, Wei Wu, Fengchang National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Guangzhou510650 China Key Laboratory of Pollution Ecology and Environmental Engineering Institute of Applied Ecology Chinese Academy of Sciences Shenyang110016 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology School of Environmental Science and Technology Dalian University of Technology Linggong Road 2 Dalian116024 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China

Municipal solid waste (MSW) without proper managements could be a significant source of greenhouse gas (GHG) emissions. MSW incineration with electricity recovery (MSW-IER) is recognized as a sustainable way to utilize waste, but its effectiveness on reducing GHG emissions at the city scale in China remain unclear due to limited data of MSW compositions. In this study, MSW compositions in 297 cities of China from 2002 to 2017 were predicted using a random forest model trained by a combination of socio-economic, climate and spatiotemporal factors. Spatiotemporal and climatic factors (such as economic development level, precipitation) accounted for 6.5%-20.7% and 20.1%-37.6% to total contributions on MSW composition, respectively. The GHG emissions from MSW-IER in each Chinese city were further calculated based on the predicted MSW compositions. The plastic is the main GHG emission source, accounting for over 91% of the total emission during 2002-2017. Compared to baseline (landfill) emission, the GHG emission reduction from MSW-IER was 12.5 × 107 kg CO2-eq in 2002 and 415 × 107 kg CO2-eq in 2017, with an average annual growth rate of 26.3%. The results provide basic data for estimating GHG emission in MSW management in China. © 2022, The Authors. All rights reserved.

关键词： Municipal solid waste

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

引用

Data Driven control and Learning systems (DDCLS)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution Automation

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A joint call for actions to advance taxonomy in China

引用

Zoological systematics 2022年第3期47卷 188-197页

作者： Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu CAS Key Laboratory of Zoological Systematics and Evolution Institute of ZoologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Animal Ecology and Conservation Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Mycology(SKLM) Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Aquatic Biodiversity and Conservation Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China Wuhan Botanical Garden Chinese Academy of SciencesWuhan 430074China Department of Marine Organism Taxonomy&Phylogeny Institute of OceanologyChinese Academy of SciencesQingdao 266071China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of BiologyChinese Academy of SciencesChengdu 610041China State Key Laboratory of Genetic Resources and Evolution&Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain Kunming Institute of ZoologyChinese Academy of SciencesKunming 650221China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China CAS Key Laboratory of Plant Resource Conservation and Sustainable Utilization South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China CAS Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglunMenglaYunnan 66630

Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.

关键词： Endangered taxonomists morphology-based taxonomy integrative taxonomy capacity building systematics

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A Survey on Big Data for Human Body Shape

A Survey on Big Data for Human Body Shape

引用

2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R.Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of Io T, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： big data human body shape 3D scanning 3D modeling clothing footwear mass customization

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