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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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Process Monitoring Based on Temporal Feature Agglomeration and Enhancement

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 825-827页

作者： Xiao Liang Weiwu Yan Yusun Fu Huihe Shao Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

Dear Editor,This letter proposes a process-monitoring method based on temporal feature agglomeration and enhancement,in which a novel feature extractor called contrastive feature extractor(CFE)extracts the temporal and relational features among process *** the feature representations are enhanced by maximizing the separation among different classes while minimizing the scatter within each class.

关键词： among while letter

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Discrete-time inverse linear quadratic optimal control over fnite time-horizon under noisy output measurements

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Control Theory and Technology 2021年第4期19卷 563-572页

作者： Han Zhang Yibei Li Xiaoming Hu Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Mathematics KTH Royal Institute of Technology10044 StockholmSweden

In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is *** goal is to recover the corresponding quadratic objective function using noisy ***,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally ***,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are *** formulate the problem as a risk minimization problem and approximate the problem using empirical *** is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative *** then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective *** efectiveness of our results are demonstrated by numerical examples.

关键词： Inverse optimal control Linear quadratic regulator Statistical consistency EM-algorithm

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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Science China(Technological Sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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M2Trans: Multi-Modal Regularized Coarse-to-Fine Transformer for Ultrasound Image Super-Resolution

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IEEE Journal of Biomedical and Health Informatics 2024年第5期29卷 3112-3123页

作者： Ni, Zhangkai Xiao, Runyu Yang, Wenhan Wang, Hanli Wang, Zhihua Xiang, Lihua Sun, Liping Tongji University Department of Computer Science and Technology The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai200092 China Pengcheng Laboratory Shenzhen518066 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen518115 China Tongji University Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment Department of Medical Ultrasound Center of Tumor Minimally Invasive Treatment Shanghai Tenth Peoples Hospital Ultrasound Research and Education Institute Clinical Research Center for Interventional Medicine School of Medicine Shanghai200072 China

Ultrasound image super-resolution (SR) aims to transform low-resolution images into high-resolution ones, thereby restoring intricate details crucial for improved diagnostic accuracy. However, prevailing methods relying solely on image modality guidance and pixel-wise loss functions struggle to capture the distinct characteristics of medical images, such as unique texture patterns and specific colors harboring critical diagnostic information. To overcome these challenges, this paper introduces the Multi-Modal Regularized Coarse-to-fine Transformer (M2Trans) for Ultrasound Image SR. By integrating the text modality, we establish joint image-text guidance during training, leveraging the medical CLIP model to incorporate richer priors from text descriptions into the SR optimization process, enhancing detail, structure, and semantic recovery. Furthermore, we propose a novel coarse-to-fine transformer comprising multiple branches infused with self-attention and frequency transforms to efficiently capture signal dependencies across different scales. Extensive experimental results demonstrate significant improvements over state-of-the-art methods on benchmark datasets, including CCA-US, US-CASE, and our newly created dataset MMUS1K, with a minimum improvement of 0.17dB, 0.30dB, and 0.28dB in terms of PSNR. Our code and dataset will be available at: https://***/eezkni/M2Trans © 2013 IEEE.

关键词： Ultrasonic imaging

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Design and research of A Soft Hand Rehabilitation Robot with Multiple Training Modes 3

Design and Research of A Soft Hand Rehabilitation Robot with...

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3rd International Conference on computing, Networks and Internet of Things, CNIOT 2022

作者： Wang, Fuhao Wang, Hongbo Luo, Jingjing Kang, Xiaoyang Zhang, Xueze Chen, Li Pan, Qiqi Academy for Engineering & Technology Shanghai200433 China Yanshan University Parallel Robot and Mechatronic System Laboratory of Hebei Province Qinhuangdao China Intelligent Robot Engineering Research Center Ministry of Education Shanghai200433 China Shanghai Intelligent Robot Engineering Technology Research Center Shanghai200433 China Shanghai Clinical Research Center for Aging and Medicine Shanghai200040 China

ISBN: (纸本)9781665469104

There are many hemiplegic patients with hand dysfunction caused by stroke. Restoring hand function can effectively improve the quality of life of patients. Traditional rehabilitation training can improve hand strength and flexibility, but there are few medical staff and high workload, which may easily lead to unstable training effect. Therefore, this letter designed a soft hand rehabilitation robot with multiple training modes. By analyzing the natural movements of human fingers, the flexible exoskeleton finger and driving unit are designed. The kinematics of the finger are derived to achieve uniform changes in the angle of the finger joints. The hand rehabilitation robot can support multiple training modes such as individual finger training, five-finger collaborative training, and left-right hand symmetry training. Experiments show that the hand rehabilitation robot can carry out continuous and stable finger joint training, and has a good rehabilitation training effect. © 2022 IEEE.

关键词： Personnel training

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Automatic Web services composition algorithm based on optimal matching

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Journal of Central South University 2011年第4期18卷 1169-1177页

作者：王俊丽丁志军侯玉兵 Engineering Research Center for Enterprise Digital Technology Ministry of EducationTongji University Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai Branch China Information Technology Designing and Consulting Institute

A novel layered method was proposed to solve the problem of Web services *** this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal matching problem was modeled based on the hypergraph theory,and solved by computing the minimal transversals of the ***,two optimization algorithms were designed to discard some useless states at the intermediary steps of the composition *** effectiveness of the composition method was tested by a set of experiments,in addition,an example regarding the travel services composition was also *** experimental results show that this method not only can automatically generate composition tree whose leaf nodes correspond to services composition solutions,but also has better performance on execution time and solution quality by adopting two proposed optimization algorithms.

关键词： Web services services composition optimal matching hypergraph theory

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Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition 41

Multi-Branch Spatial-Temporal Attention Graph Convolution Ne...

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第41届中国控制会议

作者： Daoshuai Wang Dewei Li Yaonan Guan Gang Wang Haibin Shao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Skeleton-based human action reco gnition has attracted considerable research interest due to its robustness to dynamic environments and complex *** based on graph neural networks have achieved great success in this ***,most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input *** to the separation of space and time,many methods cannot pay attention to the critical information of human skeleton sequence,and multi-scale spatial-temporal dependent attention modeling becomes the key to *** paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network(MB-STAGCN) to recognize human actions from skeleton *** proposed model adopts a multi-branch structure,fuses three-stream features,and then inputs them into the backbone network to extract features from multiple perspectives to a greater ***,we design a new temporal convolutional block for multi-scale extraction of information in the backbone *** also propose an attention block named Spatio-temporal Concat Attention(STCatAtt) to capture critical *** on benchmark datasets show remarkable performance for human action reco gnition,demonstrating the effectiveness of our method.

关键词： Skeleton-based human action recognition MB-STAGCN STCatAtt

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Semantic segmentation of track image based on deep neural network

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The Journal of China Universities of Posts and Telecommunications 2020年第5期27卷 23-33页

作者： Wang Zhaoying Zhou Junhua Liao Zhonghua Zhai Xiang Zhang Lianping School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Complex Product Advanced Manufacturing Kngineering Research Center Beijing Simulation CenterBeijing 100854China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China Alibaba Cloud Computing Beijing 100020China

In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection *** railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level *** encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation *** images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the *** first aspect deals with over-fitting problem due to the limited amount of training *** augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training ***,different gradient descent methods are compared to obtain the optimal optimizer for training model *** third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative *** of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.

关键词： railway track recognition convolutional neural networks semantic segmentation DeepLabv3+

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Exploring implicit influence for social recommendation based on GNN

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Soft computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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