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

作者： Tianyu Shen Chao Gou Jiangong Wang Jun Huang Yonglan He Huadan Xue Zhengyu Jin Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the “medical small sample”, the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis.

关键词： Deep learning Visualization Image recognition Annotations Digital twin Conferences Computational modeling

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MVM3Det: A novel method for multi-view monocular 3D detection

arXiv

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arXiv 2021年

作者： Li, Haoran Duan, Zicheng Ma, Mingjun Chen, Yaran Li, Jiaqi Zhao, Dongbin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing China The College of Engineering and Computer Science Australian National University ACT2601 Australia The School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

Monocular 3D object detection encounters occlusion problems in many application scenarios, such as traffic monitoring, pedestrian monitoring, etc., which leads to serious false negatives. Multi-view object detection effectively solves this problem by combining data from different perspectives. However, due to label confusion and feature confusion, the orientation estimation of multi-view 3D object detection is intractable, which is important for object tracking and intention prediction. In this paper, we propose a novel multi-view 3D object detection method named MVM3Det which simultaneously estimates the 3D position and orientation of the object according to the multi-view monocular information. The method consists of two parts: 1) Position proposal network, which integrates the features from different perspectives into consistent global features through feature orthogonal transformation to estimate the position. 2) Multi-branch orientation estimation network, which introduces feature perspective pooling to overcome the two confusion problems during the orientation estimation. In addition, we present the first dataset for multi-view 3D object detection named MVM3D. Comparing with State-Of-The-Art (SOTA) methods on our dataset and public dataset WildTrack, our method achieves very competitive results. © 2021, CC BY.

关键词： Object recognition

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Impact of corticosteroids on initiation and half-year durability of humoral response in COVID-19 survivors

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Chinese Medical Journal Pulmonary and Critical Care Medicine 2024年第1期2卷 48-55页

作者： Yeming Wang Li Guo Guohui Fan Yang Han Qiao Zhang Lili Ren Hui Zhang Geng Wang Xueyang Zhang Tingxuan Huang Weiyang Wang Lan Chen Lixue Huang Xiaoying Gu Xinming Wang Jingchuan Zhong Ying Wang Hui Li Jiapei Yu Zhibo Liu Chaolin Huang Bin Cao Jianwei Wang National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesChina-Japan Friendship HospitalBeijing 100029China National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 102629China Key Laboratory of Respiratory Disease Pathogenomics Chinese Academy of Medical SciencesBeijing 100029China School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeState Key Laboratory of Respiratory Health and MultimorbidityKey Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College)Ministry of EducationBeijing 100730China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Clinical research and Data managementCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Jin Yin-tan Hospital WuhanHubei 430023China Department of Pulmonary and Critical Care Medicine West China HospitalSichuan UniversityChengduSichuan 610041China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences&Peking Union Medical CollegeNational Clinical Research Center for Dermatologic and Immunologic Diseases(NCRC-DID)Ministry of Science&TechnologyState Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College Hospital(PUMCH)Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijing 100730China Department of Pulmonary and Critical Care Medicine Center of Respiratory MedicineChina-Japan Friendship HospsitalInstitute of Respiratory MedicineChinese

Background:The impact of corticosteroids on humoral responses in coronavirus disease 2019(COVID-19)sur-vivors during the acute phase and subsequent 6-month period remains *** study aimed to determine how the use of corticosteroids influences the initiation and duration of humoral responses in COVID-19 survivors 6 months after infection ***:We used kinetic antibody data from the lopinavir-ritonavir trial conducted at Jin Yin-Tan Hospital in January 2020,which involved adults hospitalized with severe COVID-19(LOTUS,ChiCTR2000029308).Anti-body samples were collected from 192 patients during hospitalization,and kinetic antibodies were monitored at all available time points after ***,plasma samples were collected from 101 COVID-19 survivors for comprehensive humoral immune measurement at the half-year follow-up *** main focus was comparing the humoral responses between patients treated with systemic corticosteroid therapy and the non-corticosteroid ***:From illness onset to day 30,the median antibody titre areas under the receiver operating characteristic curve(AUCs)of nucleoprotein(N),spike protein(S),and receptor-binding domain(RBD)immunoglobulin G(IgG)were significantly lower in the corticosteroids *** AUCs of N-,S-,and RBD-IgM as well as neutralizing antibodies(NAbs)were numerically lower in the corticosteroids group compared with the non-corticosteroid ***,peak titres of N,S,RBD-IgM and-IgG and NAbs were not influenced by *** 6-month follow-up,we observed a delayed decline for most binding antibodies,except N-IgM(𝛽−0.05,95%CI[−0.10,0.00])in the corticosteroids group,though not reaching statistical *** significant difference was observed for ***,for the half-year seropositive rate,corticosteroids significantly accelerated the decay of IgA and IgM but made no difference to N-,S-,and RBD-IgG or ***,corticosteroids group showed a trend towards d

关键词： COVID-19 Corticosteroid Immunity Antibody Humoral response

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An Adaptive Federated control Strategy for Participant Selection in Multi-Client Collaboration

An Adaptive Federated Control Strategy for Participant Selec...

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

作者： Yizhu Chen Xiaoming Du Xiao Wang Jun Jason Zhang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %,9 respectively.

关键词： Training Adaptation models Costs Biological system modeling Collaboration Optimization methods Ecology

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Target tracking control based on dual model fusion

Target tracking control based on dual model fusion

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Li, Kang Zha, Wenzhong Meng, Xiangrui Zhao, Xiaoguang China Electronics Technology Group Corporation Information Science Academy CETC Key Laboratory of Cognition and Intelligence Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781728163215

Recent years have witnessed rapid progress in target tracking. To track a moving target for mobile robots, however, both performance and speed of the algorithm are indispensable. This paper proposes a dual model fusion strategy to improve target tracking drift. Among them, the spatiotemporal context model in middle-level feature space (MFSTC) is utilized to ameliorate target tracking effect when illumination or appearance changes, the mean shift based on 3D back projection (MS3D) is fused to allow the algorithm to tackle occlusion and deformation. Tracking controller based on visual servo is also designed for mobile robots. We validate the efficiency of the proposed fusion model on the university of Birmingham RGB-D Tracking Benchmark (BTB) and show that our approach compares favorably with the state-of-the-arts, mobile robots using our approach can track targets robustly under various challenging scenes. © 2019 IEEE.

关键词： Target tracking

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Attention based Graph Covolution Networks for Intelligent Traffic Flow Analysis

Attention based Graph Covolution Networks for Intelligent Tr...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hongxin Zhang Jiaxin Liu Ying Tang Gang Xiong State Key Lab of CAD & CG Zhejiang University China College of Software Engineering Zhejiang University of Technology China State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Predicting traffic flow is one of the fundamental issue to smart cities. However it is still challenging in vehicular cyber-physical systems because of ever-increasing urban traffic data. Most previous models for traffic flow prediction suffers the problem with variance error depends on time and location. In this paper, a novel graph convolution network (GCN) approach is proposed for predicting citywide traffic flow. Our key idea is to introduce spatial and temporal attention to the GCN model to lessen the impact of urban data complexity. A framework of flow-based graph convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose practical strategies that efficiently learn parameters of the model. Experimental results demonstrate that the proposed approach to traffic flow prediction outperforms state-of-the-art approaches.

关键词： Predictive models Data models Analytical models Convolution Urban areas Chebyshev approximation Roads

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A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 961-968页

作者： Xiuqin Shang Dayong Shen Fei-Yue Wang Timo R. Nyberg the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences (CAS)Beijing 100190China the Qingdao Academy of Intelligent Industries Qingdao 266109China IEEE the Beijing Enineering Research Center of Intelligent Systems and Technology Institute of AutomationCASBeijing 100190China the Cloud Computing Center CASDongguan 523808China the School of Science at Aalto University AaltoFinland

We study the fabric spreading and cutting problem in apparel *** the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment *** reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be *** propose an iterated greedy algorithm for solving the fabric spreading and cutting *** algorithm contains a constructive procedure and an improving *** the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay *** improving loop will run until it cannot obtain any smaller lay set or the time limit is *** experiment results on 500 cases show that the proposed algorithm is effective and efficient.

关键词： Construction and improvement cutting and packing fabric spreading and cutting heuristic algorithm

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BiFNet: Bidirectional fusion network for road segmentation

arXiv

引用

arXiv 2020年

作者： Li, Haoran Chen, Yaran Zhang, Qichao Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion net- work(BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) Dense space transformation module, which solves the mutual conversion between camera image space and BEV space. 2) Context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features. This method has achieved competitive results on KITTI dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

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Calibration of Agent-Based Model Using Reinforcement Learning

Calibration of Agent-Based Model Using Reinforcement Learnin...

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

作者： Bing Song Gang Xiong Songmin Yu Peijun Ye Xisong Dong Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China Fraunhofer Institute for Systems and Innovation Research Breslauer Germany

In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity.

关键词： Learning systems Microscopy Digital twin Sociology Reinforcement learning Mathematical models Calibration

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