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International Workshop on Robot Sensing (ROSE)

作者： Jayanto Halim Mohamad Bdiwi Steffen Ihlenfeldt Group Collaborative Robot Systems Fraunhofer Institute for Machine Tools and Forming Technology Chemnitz Germany Department of Production System and Factory Automation Fraunhofer Institute for Machine Tools and Forming Technology Chemnitz Germany

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

Agile manufacturing paradigms have revolutionized a new era of production processes, offering unparalleled flexibility and adaptability to the ever-changing landscape of product variations and smaller batch sizes. In response to this dynamic environment, the robotic industry has undergone a significant transformation, presenting a range of versatile robotic systems capable of swiftly deploying diverse applications. This transformative shift accelerates manufacturing processes and showcases the ability to accommodate product specifications and batch size variations. Welding is a notable example of robotics playing a crucial role in agile production. This work presents a novel approach combining no-code robot programming and an intelligent vision-based welding system. By leveraging this framework, even non-expert users can seamlessly instruct welding trajectories through intuitive interactions. These trajectories are meticulously crafted using advanced welding seam extraction algorithms, considering various welding joint types and inputs from heterogeneous sensors. Consequently, robot-specific parameters can be parametrized, and the generated robot code can seamlessly translate into robotic movements in the real world. Validation of this approach through real-world experiments underscores its potential for deployment in agile welding applications. The promising results affirm this novel methodology's efficacy, paving the way for its practical implementation in real-world scenarios. By effectively bridging the divide between advanced robotics and agile manufacturing, this approach can potentially elevate industrial robotic applications to new levels of efficiency and adaptability.

关键词： Agile manufacturing Service robots Welding Semantics Production Robot sensing systems Mathematical models

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning

A Cross Branch Fusion-Based Contrastive Learning Framework f...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Chengzhi Wu Qianliang Huang Kun Jin Julius Pfrommer Jürgen Beyerer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Department of Automation Tsinghua University China System Technologies and Image Exploitation IOSB Fraunhofer Institute of Optronics Germany

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Contrastive learning is an essential method in self-supervised learning. It primarily employs a multi-branch strategy to compare latent representations obtained from different branches and train the encoder. In the case of multi-modal input, diverse modalities of the same object are fed into distinct branches. When using single-modal data, the same input undergoes various augmentations before being fed into different branches. However, all existing contrastive learning frameworks have so far only performed contrastive operations on the learned features at the final loss end, with no information exchange between different branches prior to this stage. In this paper, for point cloud unsupervised learning without the use of extra training data, we propose a Contrastive Cross-branch Attention-based framework for Point cloud data (termed PoCCA), to learn rich $3 D$ point cloud representations. By introducing sub-branches, PoCCA allows information exchange between different branches before the loss end. Experimental results demonstrate that in the case of using no extra training data, the representations learned with our self-supervised model achieve state-of-the-art performances when used for downstream tasks on point clouds.

关键词： Point cloud compression Representation learning Three-dimensional displays Training data Benchmark testing Data models Task analysis

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Small Sample Learning Mechanism Based on Knowledge-transferring for Fault Diagnosis

Small Sample Learning Mechanism Based on Knowledge-transferr...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xue Ma Xinyu Wu Chenglin Wen Xiaohui Sun Department of Automation Tsinghua University Beijing China Shenzhen Institutes of Advanced Technology Guangdong Provincial Key Lab of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Department of Automation Guangdong University of Petrochemical Technology Maoming China

Deep learning-based methods have been applied in many fields which has received increased attention. It requires large annotated data to train a deep model with good generalization ability. However, it may be very difficult, time-consuming, and even expensive to collect and annotate practical industrial data, for example, for fault diagnosis in industrial systems. To address this issue, a novel small sample learning (SSL) mechanism based on Knowledge-transferring is proposed in this manuscript, which leverages the expert system and deep neural network simultaneously. Firstly, an annotator is constructed based on a belief rule base (BRB) expert system only with available small labeled samples. Then, the left unlabeled samples will be labeled through the annotator to form an augmented training dataset (ATD) with the available small labeled samples, which can realize the data representation of expert knowledge. Besides, a united optimization model based on CNN that combines data and knowledge is built, which has realized the transfer of expert knowledge. Finally, we demonstrate the capability of the proposed method on the dataset collected from the real experimental platform for rotating machinery fault diagnosis.

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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The Strength of Structural Diversity in Online Social Networks

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Research 2021年第1期2021卷 1203-1212页

作者： Yafei Zhang Lin Wang Jonathan JHZhu Xiaofan Wang Alex‘Sandy’Pentland Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China Department of Media and Communication and School of Data Science City University of Hong KongHong Kong S.A.R.China Department of Automation Shanghai UniversityShanghai 200444China Media Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective *** a large-scale dataset from a knowledge-sharing website,this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social *** capture the structural diversity of an individual,we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into *** show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation,and the inclusion of a coexposure network provides an additional ingredient to achieve that *** synthetically controlling several possible confounding factors through matching experiments,structural diversity still plays a nonnegligible role in the prediction of personal online social *** work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains,such as social influence and collective intelligence studies.

关键词： collective networks founding

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Research On Optimization Of Electric Vehicle Charging Scheduling Based On Blockchain 6

Research On Optimization Of Electric Vehicle Charging Schedu...

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6th IEEE International Conference on Energy Internet, ICEI 2022

作者： Di, Yuhong Huo, Ru Sun, Chuang Zeng, Shiqin Sha, Zongxuan Huang, Tao Beijing University of Technology Faculty of Information Technology Beijing China Tsinghua University Department of Automation Beijing China Shenzhen Power Supply Bureau Co. Ltd System Operation Department Shenzhen China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9781665493277

With the popularization and application of new energy, the vigorous development of electric vehicles is facilitated. While the number of electric vehicles has increased significantly, a large number of electric vehicles are charged in disorder, resulting in the problems of low charging efficiency and serious grid load. This paper proposes a blockchain-based charging scheduling strategy for electric vehicles. Firstly, a trusted information platform based on blockchain is built, and a secure scheduling architecture for electric vehicle charging based on blockchain is designed. Furthermore, considering the factors of user reputation, time-of-use electricity price, and distance consumption, a user behavior-based calculation method of the cost is proposed. Finally, an electric vehicle charging scheduling strategy based on Particle Swarm Optimization is constructed to achieve the goal of orderly charging scheduling and peak shaving and valley filling of a large number of electric vehicles. The theoretical analysis and simulation experiments show that the proposed optimization scheme could effectively reduce user overhead and grid pressure. © 2022 IEEE.

关键词： Blockchain

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping 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 Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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