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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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FedSiam-DA: Dual-aggregated Federated Learning via Siamese Networks under Non-IID Data

arXiv

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arXiv 2022年

作者： Yang, Ming Wang, Yanhan Wang, Xin Zhang, Zhenyong Wu, Xiaoming Cheng, Peng Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou310027 China

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the Siamese Network, FedSiam-DA regards the local and global model as different branches of the Siamese Network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Research Status of 6LoWPAN in the Field of Internet of Things 5

Research Status of 6LoWPAN in the Field of Internet of Thing...

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5th International Conference on Automation, control and Robotics engineering, CACRE 2020

作者： Zhao, Liang Wang, Guangwen School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9781728198880

The increasing number of wireless sensor nodes brings more application markets and challenges to the Internet of things (IoT). The traditional IPv4 address resources cannot meet the needs of the development of the IoT, so we need a communication mechanism with better compatibility and scalability. With the proposal of the 6LoWPAN protocol, the application of 6LoWPAN to the communication network of the IoT has become the focus of research. This paper studies the research status of 6LoWPAN in the field of Internet of things in the past four years, and analyzes the system architecture, system composition and monitoring parameters of 6LoWPAN through different application scenarios. Experiments in related literatures show that 6LoWPAN has stable network transmission performance and can be used for precise monitoring and management in combination with web applications. In addition, RPL routing protocol security algorithm can be one of the future research directions. © 2020 IEEE.

关键词： Internet of things

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Model Predictive control with Reach-avoid Analysis

arXiv

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arXiv 2023年

作者： Ren, Dejin Lu, Wanli Lv, Jidong Zhang, Lijun Xue, Bai State Key Lab. of Computer Science Institute of Software CAS Beijing China University of Chinese Academy of Sciences Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Competition and Cooperation Mechanism Between Agency Selling and Wholesale: An Application of the Emerging E-commerce Model

Competition and Cooperation Mechanism Between Agency Selling...

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IEEE International Conference on industrial engineering and engineering Management

作者： Haonan Wang C. K. M. Lee Ping Ji Gang Li Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China Laboratory for Artificial Intelligence in Design Hong Kong Science Park New Territories Hong Kong SAR China School of Intelligent Finance and Business XJTLU Entrepreneur College (Taicang) Xi'an Jiaotong - Liverpool University China School of Management Xi'an Jiaotong University The State Key Lab for Manufacturing Systems Engineering The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering Xi'an China

Recently, the platform economy allows enterprises to collaborate with business partners and provide additional channels for various parties. E-commerce platforms have become integral channels for connecting suppliers with consumers. In the platform economy, agency selling and reselling are two predominant sales models that often coexist for the same product types, resulting in a competitive relationship. This study analyzes transaction data for 30,000 Stock Keeping Units (SKUs) provided by the Chinese e-commerce platforms over one month. We employ three models to compare the sales quantity and profits of manufacturers and online platforms and to identify the most efficient mode for stakeholders based on the model establishment and empirical evidence. Our findings suggest that apart from the existing channel to provide products to customers, manufacturers enter the platform which will increase the profit of platform operators. On the other hand, platform operators may also affect the profit of the manufacturers.

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Weighted Fusion of Multiple Classifiers for Human Activity Recognition

Weighted Fusion of Multiple Classifiers for Human Activity R...

引用

International Conference on Information Fusion

作者： Kezhu Zuo Xinde Li Jean Dezert Yilin Dong School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Nanjing China The French Aerospace Lab ONERA DTIS/MIDL Palaiseau France College of Computer Science and Engineering Shanghai Maritime University Shanghai China

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.

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Efficient Wideband Beamforming with Null Broadening Using Fusion Convolutional Block

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Li, Chenglong Feng, Liwen Gao, Yajie Shi, Baozhu Du, Ruiyan Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing 066004 China Northeastern University School of Computer Science and Engineering Shenyang 110819 China Hebei University College of Mathematics and Information Science Baoding 071002 China

To meet the development requirements of bandwidth and efficiency in future 6G wireless communications, an efficient wideband beamforming (WB) algorithm is proposed based on fusion convolutional block neural network (FCBNN) in this paper, named as WB-FCBNN algorithm. The presented algorithm can significantly enhance the real-time performance while obtaining a superior output signal to interference plus noise ratio (SINR) of WB. Firstly, the algorithm utilizes null broadening algorithm with interference plus noise covariance matrix (INCM) reconstruction to generate labels, avoiding the influence of interference direction of arrival (DOA) estimation errors. Then, a novel FCBNN framework is constructed to enhance the real-time performance and the output SINR. Specifically, a fusion convolutional block (FCB) is introduced into the FCBNN framework, which can fuse the asymmetric convolution and the standard convolution into one convolution. In this way, the network parameters will be reduced, thus the computational complexity of the network can be reduced. Furthermore, the FCBNN model can be trained via the label. Finally, the well-trained FCBNN model can quickly predict the near-optimal weight vector of WB. Simulation results demonstrate that the presented algorithm can effectively increase the real-time performance of WB, and still has a satisfactory SINR in the environment of high SNR and low snapshot. © 2017 IEEE.

关键词： Array signal processing deep learning wideband beamforming

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Adaptation Via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive 3d Object Detection

SSRN

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SSRN 2023年

作者： Li, Ziyu Yao, Yuncong Quan, Zhibin Qi, Lei Feng, Zhenhua Yang, Wankou School of Automation Southeast University Nanjing210096 China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-labels. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely Adaptation Via Proxy (AVP), that explicitly leverages cross-domain relationships to generate adequate high-quality samples without any target statistics, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on the source domain with curriculum example mining to enhance its generalization capability. Then, we fine-tune the pre-trained detector on the instance-aware proxy that is a data collection with stronger supervision. The instance-aware proxy utilizes the profitable instances from the source domain to provide more robust supervision for optimizing the detector to overcome domain gaps. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11189-11201页

作者： Zhijun Zhang Zhongwen Cao Xingru Li School of Automation Science and Engineering South China University of Technology Guangzhou China Key Library of Autonomous Systems and Network Control Ministry of Education Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China Jiangxi Thousand Talents Plan Nanchang University Nanchang China College of Computer Science and Engineering Jishou University Jishou China Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Laboratory) Guangzhou China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong China School of Information Science and Engineering Changsha Normal University Changsha China Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming China

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

关键词： Manipulators Robots Fault tolerant systems Fault tolerance End effectors Planning Kinematics Manipulator dynamics Jacobian matrices Collaboration

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

Tactile-Based Object Pose Estimation Employing Extended Kalm...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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