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Public Opinion Analysis on Novel Coronavirus Pneumonia and Interaction With Event Evolution in Real World

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第4期8卷 1042-1051页

作者： Liu, Xin Zheng, Liang Jia, Xiaofeng Qi, Hongwei Yu, Shaowen Wang, Xiao China Univ Petr Huadong Dept Artificial Intelligence Qingdao 266580 Peoples R China Beijing Big Data Ctr Data Management Dept Beijing 100085 Peoples R China Datatang Beijing Technol Co Ltd Beijing 100094 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

During the novel coronavirus pneumonia, the underlying comments and news of the epidemic had bombed on the Internet media and social networks, reflecting the views and attitudes of the governments, society, and individuals to the epidemic situation. Those comments are closely related to people's lives and blend with each other. Novel coronavirus pneumonia affects people's lives, and they publish comments on social networks and media, and the underlying information from social networks and Internet media in turn affects people's attitude to the epidemic and the measures, and strategies taken to fight coronavirus in life. We collect the comments from various social network platforms and network media such as Weibo, WeChat, Chinanews, Sina News, NetEase News, Sohu News, and analyze the overall situation of public opinion on the novel coronavirus pneumonia. We explore the interaction between online public opinion and real life using a variety of algorithms in different stages of the epidemic. We can obtain public attention and key nodes in the dissemination of public opinion by detecting hot events and calculating user's influence. The analysis of information dissemination path is conducive to the rapid propagation of healthy comments, curbing the spread of bad information. Event-specific details and topics are mined from large amounts of comments by thematic clustering and association relationship detecting, which helps to provide technical support for government response measures. The emotional analysis of Internet users' groups helps to guide the direction of public opinion. The situation analysis shows the deep blending and mutual influence between the online public opinion and the real world.

关键词： Social networking (online) COVID-19 Blogs Pulmonary diseases Sentiment analysis Epidemics Monitoring Novel coronavirus public opinion topic detection

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A nondestructive automatic defect detection method with pixelwise segmentation

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KNOWLEDGE-BASED systems 2022年 242卷

作者： Yang, Lei Fan, Junfeng Huo, Benyan Li, En Liu, Yanhong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Defect detection is essential for the quality control and repair decision-making of various products. Due to collisions, uneven stress, welding parameters and other factors, cracks form on the surface or inside of products, which affect the product appearance and mechanism strength and may even cause huge safety accidents. Nondestructive testing (NDT) is an effective and practical method for accurate defect detection, but it still faces various challenges against complex factors, such as complex backgrounds, poor contrast, weak texture, and class imbalance issues. Recently, deep learning has rapidly improved the performance of automatic defect detection with the strong feature expression ability of deep convolutional neural networks (DCNNs). However, various limitations remain due to the insufficient processing of local contextual features, which affects the detection precision. To address this issue, with the encoder-decoder network structure, a novel nondestructive defect detection network, namely, NDD-Net, is proposed in this paper to construct an end-to-end nondestructive defect segmentation scheme. To make the segmentation network better emphasize the defect areas, an attention fusion block (AFB) is proposed to replace the raw skip connections to acquire more discriminative features and enhance the segmentation performance on microdefects. Meanwhile, by fusing a dense connection convolution network and a residual network, a residual dense connection convolution block (RDCCB) is also proposed to be embedded into the proposed segmentation network to acquire richer information about the local feature maps. Two public datasets with severe class imbalance issues are adopted for model evaluation: the Grima X-ray (GDXray) database and the rail surface discrete defects (RSSDs) dataset. Experimental results show that the proposed segmentation network outperforms other related segmentation models.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Defect detection Deep architecture Image segmentation Attention fusion Residual dense connection convolution network

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Component calibration and configuration planning in assembly automation with a parallel manipulator

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2022年 77卷

作者： Xing, Shiyu Wang, Zhe Hou, Jun Fan, Junfeng Jing, Fengshui Tan, Min 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 100049 Peoples R China

Parallel manipulator (PM) is widely used as posture alignment mechanism in heavy component machining and assembly process. To achieve precise kinematics backward transformation, the relative spatial relationship between the component and PM's spherical joints need be calibrated, which suffers from the pain-point that the spherical joint center is inside the mechanical socket and its position can hardly be directly recovered. We refer this problem as component calibration. In this research, we present an alternate solution by formulating kinematic chain in form of AX = XB based on designedly established PM reference configuration and base coordinate system, which does not rely on the measurement of spherical joint center position. To improve the calibration accuracy, accuracy-influencing factors, PM's dexterity distribution over workspace and properties of angle-axis space are jointly analyzed then 4 centrosymmetric angle-axis vector pairs on the boundary of maximum feasible angle-axis space at zero manipulator translation are set as configuration pairs to establish the kinematic chain. Configuration planning in angle-axis space facilitates the visualization and control of the critical factors in calibration process and tends to set these factors to optimum simultaneously. Experiments are carried out finally and the results show that the calibration accuracy could be notably improved with such method compared with no configuration planning, especially for translation accuracy.

关键词： Parallel manipulator Component calibration Angle-axis space Configuration planning

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Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Cascade Network with Boundary-Adaptive Filter for Surgical G...

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2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Chen H.-B. Ni Z.-L. Li Z. Bian G.-B. Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780738133669

Automatically recognizing surgical gestures plays a key role in computer-assisted surgery and automatic skill assessment. Solutions to this task that only rely on video without additional sensor hardware have attracted great attention since they are economical and feasible. However, it is still a challenging problem due to the variable gesture durations and the oversegmentation problem that occurred in a long gesture duration. To address the above problems, we propose a cascade network with boundary-adaptive filter to dynamically capture the long-range temporal dependencies and reduce the over-segmentation errors obviously. A cascade module is designed to enhance the temporal modeling abilities in different timescales, in which multiple similar temporal convolution networks are divided into cascade stages and a fusion stage. Every cascade stage with different temporal receptive fields has a frame-specific weight to adjust the distribution of each stage and they will be aggregated into the fusion stage to refine the predictions. The frame-specific model architecture may incur a more serious over-segmentation problem. A boundary-adaptive filter is designed to leverage gesture boundary information to smooth the prediction results adaptively, which maintains consistency within a gesture duration while keeping the gesture-crossing discrepancy. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The proposed method reaches the state-of-the-art frame-wise accuracy (83.79%) and comparable segment-level performance (83.35% F1@50). © 2021 IEEE.

关键词： action segmentation surgical gesture recognition temporal convolutional network video understanding

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Visuotactile Feedback Parallel Gripper for Robotic Adaptive Grasping

Visuotactile Feedback Parallel Gripper for Robotic Adaptive ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Boyue Cui, Shaowei Zhang, Chaofan Hu, Jingyi Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

In robot grasp and dexterous manipulation tasks, tactile sensing is important for the control adjustment of the manipulator. In this paper, we present a novel low-cost parallel gripper with high-resolution tactile sensing, named the GelStereo Gripper. Furthermore, an adaptive grasp strategy is proposed to endow the gripper with tactile-feedback grasp stability-maintaining ability. We install the gripper on our robot platform and conduct various grasp experiments by utilizing proposed control methods. Experimental results verify the reliability of the GelStereo gripper and also prove the effectiveness of the proposed strategy for experimental objects with different features. © 2022 IEEE.

关键词： Grippers

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Design and Hysteresis Modeling of a Miniaturized Elastomer-Based Clutched Torque Sensor

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Sun, Ning Cheng, Long Xia, Xiuze Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Elastic torque sensors have been widely used for small-scale robots such as hand exoskeletons to achieve torque control. However, designing a miniaturized and lightweight elastic torque sensor with human-machine interaction safety is still a challenge. In this article, a novel miniaturized and lightweight elastomer-based clutched torque sensor is presented. A rubber spring is designed and used to reduce its volume and weight. A wafer disk clutch is devised to improve mechanical safety. The torque sensor is 29.5 mm x 18 mm x 24 mm in dimension and weighs 23 g. Compared with the state-of-the-art elastic torque sensors for hand exoskeletons, the volume-to-torque ratio is reduced by 15.48%, and its weight is reduced by 23.33%. Since the hysteresis characteristics of the rubber spring leads to a nonlinear deformation-torque relationship, an improved parametric Gaussian process regression (PGPR) method based on the nonlinear autoregressive moving average structure with exogenous inputs (NARMAX) is proposed. A combined kernel function for the improved PGPR is designed to improve the fitting performance. Finally, experiments have been conducted to verify the mechanical safety and torque sensing performance. The force caused by collision on the proposed torque sensor is less than that on the torque sensor without the clutch (reduced by 51.78%). The proposed hysteresis model can reduce the maximum absolute modeling error to 7% compared with those of other intelligent hysteresis models (the modeling error is 12.32%). Therefore, the experimental results indicate that the proposed torque sensor can improve the mechanical safety and achieve accurate torque sensing.

关键词： Clutch elastic torque sensor hysteresis modeling mechanical design rubber spring

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PROTOTYPICAL CONTEXT-AWARE DYNAMICS GENERALIZATION FOR HIGH-DIMENSIONAL MODEL-BASED REINFORCEMENT LEARNING

arXiv

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arXiv 2022年

作者： Wang, Junjie Mu, Yao Li, Dong Zhang, Qichao Zhao, Dongbin Zhuang, Yuzheng Luo, Ping Wang, Bin Hao, Jianye The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Hong Kong Hong Kong Huawei Noah's Ark Lab

The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging. Although the recurrent structure utilized in current advances helps to capture local dynamics, modeling only state transitions without an explicit understanding of environmental context limits the generalization ability of the dynamics model. To address this issue, we propose a Prototypical Context-Aware Dynamics (ProtoCAD) model, which captures the local dynamics by time consistent latent context and enables dynamics generalization in high-dimensional control tasks. ProtoCAD extracts useful contextual information with the help of the prototypes clustered over batch and benefits model-based RL in two folds: 1) It utilizes a temporally consistent prototypical regularizer that encourages the prototype assignments produced for different time parts of the same latent trajectory to be temporally consistent instead of comparing the features;2) A context representation is designed which combines both the projection embedding of latent states and aggregated prototypes and can significantly improve the dynamics generalization ability. Extensive experiments show that ProtoCAD surpasses existing methods in terms of dynamics generalization. Compared with the recurrent-based model RSSM, ProtoCAD delivers 13.2% and 26.7% better mean and median performance across all dynamics generalization tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Fluorescence image-guided tumour surgery

NATURE REVIEWS BIOENGINEERING

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NATURE REVIEWS BIOENGINEERING 2023年第3期1卷 161-179页

作者： Wang, Kun Du, Yang Zhang, Zeyu He, Kunshan Cheng, Zhongquan Yin, Lin Dong, Di Li, Changjian Li, Wei Hu, Zhenhua Zhang, Chong Hui, Hui Chi, Chongwei Tian, Jie Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Key Lab Mol Imaging Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Engn Med Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Key Lab Human Comp Interact Beijing Peoples R China Capital Med Univ Beijing Friendship Hosp Dept Gen Surg Beijing Peoples R China Jinan Univ Affiliated Hosp 1 Med Imaging Ctr Guangzhou Peoples R China Beijing Technol & Business Univ Sch Int Econ & Management Dept Big Data Management & Applicat Beijing Peoples R China

Intraoperative fluorescent imaging can provide real-time identification of tumours, lymph nodes, nerves and other healthy and malignant tissues during oncological surgery, contributing to better surgical outcomes. Various fluorescent probes have been clinically approved for surgical applications, improving tumour resection precision and preventing iatrogenic injury. In this Review, we discuss the development and application of fluorescent probes for image-guided surgery, including systemically and locally applied probes that are either non-targeted or targeted to specific tumours and tissues. We discuss the optimization and clinical potential of these probes, and highlight their current and future applications in oncological surgery. In addition, we examine the hardware of fluorescence imaging equipment, and discuss how artificial intelligence can enable real-time quantification to guide surgical decision-making. Finally, we highlight the remaining challenges in the field of image-guided surgery, including the need for standardization to achieve regulatory approval of new probes, and the required team effort for new probe development.

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Neurons Perception Dataset for RoboMaster AI Challenge

Neurons Perception Dataset for RoboMaster AI Challenge

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Haoran Duan, Zicheng Li, Jiaqi Ma, Mingjun Chen, Yaran Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 2601 Australia Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

From virtual game to physical robot, games have witnessed the development of artificial intelligence (AI) technology, especially the data-driven technology represented by deep learning. Compared with virtual games, a physical robot game such as RoboMaster AI challenge needs to build a complete closed-loop architecture composed of perception, planning, control, and decision-making to support autonomous confrontation. Perception, as the eye of the robot, its performance in the complex environment depends on a massive dataset. Although there are many open perception datasets, these datasets are difficult to meet the needs of RoboMaster AI challenge due to the high dynamics of the task, the distinctiveness of the objects, and limited computing resources. In this paper, we release a dataset named Neurons 1 perception dataset for RoboMaster AI challenge, which covers 3 tasks including monocular depth estimation, lightweight object detection, and multi-view 3D object detection, and makes up the data blank in this field. In addition, we also evaluate state-Of-The-Art (SOTA) methods on each task, hoping to provide an impartial benchmark for the development of perception algorithm.

关键词： Deep learning Three-dimensional displays Estimation Object detection Games Benchmark testing Prediction algorithms

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