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A Robotic End-Effector for Screwing and Unscrewing Bolts From the Side

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9793-9800页

作者： Tao, Rui Fan, Junfeng Jing, Fengshui Jun, Hou Xing, Shiyu Ma, Yunkai 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

This letter presents a novel robotic end-effector for screwing and unscrewing bolts. In many industrial scenarios, it is required to manipulate bolts from the side using robots with end-effectors. Besides, the reaction torque during tightening needs to be balanced to the stability of the robot. To address these challenges, we design a robotic end-effector that can realize side screwing and torque counteraction. Specifically, through an open gear set and a width-adjustable screwing mouth, the end-effector can approach the bolt from the side in any direction and then screw and unscrew it. A gripper with force-magnification and self-locking drive is equipped to counteract the reaction torque during screwing and unscrewing. Considering the extensions for different application scenarios, a method to extend the end-effector by switching screwing mouths is given, and the extended end-effector is able to screw a variety of objects. After further analysis and optimization of the force and size, experiments are carried out. The results show that the end-effector can screw and unscrew the target bolt robustly and counteract the reaction torque. It is also applicable in different scenarios.

关键词： Fasteners End effectors Robots Torque Mouth Gears Grippers Screwing and unscrewing grippers and other end-effectors mechanism design assembly

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Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第2期19卷 717-730页

作者： Liang, Yunji Wang, Qiushi Xiong, Kang Zheng, Xiaolong Yu, Zhiwen Zeng, Daniel Northwestern Polytech Univ Sch Comp Sci Xian 710060 Shaanxi Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. In spite of the diversity of cybercrimes, it is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they cannot provide robust performance due to the diversity of cybercrimes and cannot cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

关键词： paced learning temporal convolutional network factorization machine wide & deep malicious URL detection

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Cooperative ETM-Based Adaptive Neural Network Tracking control for Nonlinear Pure-Feedback MASs: A Special-Shaped Laplacian Matrix Method

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第3期35卷 3528-3538页

作者： Zhu, Qiangqiang Niu, Ben Wang, Ding Li, Shengtao Wang, Xiaomei Kong, Jie Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Univ Jinan Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article solves the cooperative adaptive tracking control problem for nonlinear pure-feedback multi-agent systems (MASs). Compared with the previous achievements of adaptive control of pure-feedback MASs, the partial derivative of the nonaffine function may not exist by using decoupling technology. In the controller design framework based on the backstepping technique, the additional state variables are processed using the special properties of the radial basis function neural networks (RBF NNs). A special-shaped Laplacian matrix is proposed to unify the leader gain form in the tracking error design process (the coefficient in the second term of tracking error). Furthermore, an event trigger mechanism (ETM) is introduced to save resources. The constructed controller under the ETM can not only stabilize the system states but also make the tracking error reach a small accuracy. Finally, the simulation results demonstrated the feasibility of the proposed method.

关键词： Laplace equations Nonlinear systems Multi-agent systems Switches Simulation Learning systems Information science Decoupling technology neural network (NN) pure-feedback multi-agent systems (MASs) relative threshold event trigger mechanism (ETM) special-shaped Laplacian matrix

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Unsupervised Domain Adaptation Based Automatic COVID-19 CT Segmentation 7

Unsupervised Domain Adaptation Based Automatic COVID-19 CT S...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Zhang, Zhen Guo, Dalei University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences State Key Lab. of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

The outbreak of COVID-19 has brought huge challenges to health systems around the world. Computed Tomography (CT), as an effective method to screen COVID-19, can provide explainable visual information which can be used for the diagnosis and quantification of COVID-19. It will make diagnosis easier and more effective by providing segmentation results of infections from given CT scans. Deep learning based methods can perform well in tasks that have abundant annotated data, while they may suffer from great performance drops when there is little annotated data available. To collect and annotate CT scans of COVID-19 cases is time-consuming and labor-intensive, which will demand highly-proficient radiology expertise. To tackle this bottleneck, this paper provides a novel unsupervised domain adaptation method that bridges the gap of distributions between the available annotated CT scans and private CT scans, thus reducing dependence on private annotated CT scans. Quantitative analysis demonstrates the effectiveness of our proposed method, which achieves 20 increments in Dice score compared to models without adaptation. © 2022 IEEE.

关键词： COVID-19

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Pythagorean-Hodograph curves-based trajectory planning for pick-and-place operation of Delta robot with prescribed pick and place heights

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ROBOTICA 2023年第6期41卷 1651-1672页

作者： Su, Tingting Liang, Xu Zeng, Xiang Liu, Shengda Beijing Univ Technol Beijing Inst Artificial Inteligence Beijing Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing Peoples R China Chinese Acad Sci CInst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

In this paper, a Pythagorean-Hodograph (PH) curve-based pick-and-place operation trajectory planning method for Delta parallel robots is proposed, which realizes the flexible control of pick-and-place operations to meet the requirements of various practical scenarios. First, according to the geometric relationship of pick-and-place operation path, different pick-and-place operations are classified. Then trajectory planning is carried out for different situations, respectively, and in each case, the different polynomial motion laws adopted by the linear motion segment and the curved motion segment are solved. Trajectory optimization is performed with the motion period as optimization objective. The proposed method is easier to implement, and at the same time satisfies the safety, optimization, mobility, and stability of the robot;that is, the proposed method realizes obstacle avoidance, optimal time, flexible control of the robot trajectory, and stable motion. Simulations and experiments verify the effectiveness of the method proposed in this paper. The proposed method can not only realize the fast, accurate, and safe operation in intelligent manufacturing fields such as rapid classification, palletizing, grasping, warehousing, etc., but its research route can also provide a reference for trajectory planning of intelligent vehicles in logistics system.

关键词： trajectory planning trajectory generation Delta parallel robot pick-and-place operation Pythagorean-Hodograph curves

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Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2022年第10期32卷 6728-6740页

作者： Liu, Jierui Cao, Zhiqiang Tang, Yingbo Liu, Xilong Tan, Min 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

Category-level 6D object pose estimation has gained popularity and it is still challenging due to the diversity of different instances within the same category. In this paper, a novel category-level 6D object pose estimation framework with structure encoder and reasoning attention is proposed. A structure autoencoder is introduced to mine the shared structure features in the color images within the same category, via a distinct learning strategy that recovers the image of another instance but with the most similar pose to the input. On this basis, a reasoning attention decoder and full connected layers are stacked to form a rotation prediction network, where the structure features and 3D shape features are integrated and projected to a semantic space. The semantic space includes observed patterns and learnable patterns, which are better learned by adding a shortcut connection branch parallel to reasoning attention decoder with gradient decouple. Further reasoning based on these patterns endows the decoder with powerful feature representation. Without 3D object models, the proposed method models the attributes of category implicitly in the semantic space and better performance of 6D object pose estimation is guaranteed by reasoning on this space. The effectiveness of the proposed method is verified by the results on public datasets and actual experiments.

关键词： Shape Three-dimensional displays Cognition Pose estimation Feature extraction Decoding Solid modeling Category-level 6D object pose estimation structure encoder reasoning attention

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A Deep Learning Based Personal Authentication Method Using sEMG Signal 41

A Deep Learning Based Personal Authentication Method Using s...

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第41届中国控制会议

作者： Lina Tong Mingjia Zhang Daisong Liu Hanghang Ma Yuxiang Zhang Liang Peng School of Mechanical Electronic & Information Engineering Chinese University of Mining and Technology-Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChina Academy of Science

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Personal authentication means that the relevant system confirms whether the identity of the user is real,legal and unique.A safe and convenient personal authentication method is a core for guarantee the security of our information and *** Electromyography(sEMG),which exists on the surface of the skin,is a good signal to achieve personal authentication,for it is difficult to be extracted and *** this paper,a new personal authentication method using forearm sEMG is ***,8-channel sEMG signals are recorded by Myo armband that is placed on right forearm of 14 subjects who perform the same hand open ***,two different deep learning models are presented to classify the *** multi-layers Convolution Neural Network(ML-CNN) and two stages Long Short Term Memory(2 s-LSTM) Network can achieve accuracy of 97.50% and 93.60% respectively,shows it is feasible to achieve the goal of personal *** the time cost of ML-CNN is higher than the 2 s-LSTM,both are lower than human reaction *** the architecture of ML-CNN is more suitable to achieving the goal of personal authentication.

关键词： Personal Authentication Surface Electromyography(sEMG) Myo multi-layers Convolution Neural Network(CNN) LSTM Network

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A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第1期25卷 532-544页

作者： Du, Wenbo Chen, Shenwen Li, Zhishuai Cao, Xianbin Lv, Yisheng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Adv Technol Near Space Informat Syst Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Shandong Jiaotong Univ Shandong Key Lab Smart Transportat Preparat Jinan 250353 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden among airports and show limited performances on the traffic flow prediction task for the aviation system which is composed of several airports. In this paper, we consider the multi-airport scenario and propose a novel spatio-temporal hybrid deep learning model to efficiently capture spatial correlations as well as temporal dependencies in a parallelized way. Specifically, we introduce the causal inference among airports to model their interactions and thus construct adaptive causality graphs in a data-driven manner to address the heterogeneity of airports. Furthermore, given that multi-source features are not applicable for all airports, a feature mask module is designated to adaptively select the features in spatial information mining. Extensive experiments are conducted on the real data of top-30 busiest airports in China. The results show that our spatio-temporal deep learning approach is superior to state-of-the-art methodologies and the improvement ratio is up to 4.7% against benchmarks. Ablation studies emphasize the power of the proposed adaptive causality graph and the feature mask module. All of these prove the effectiveness of the proposed methodology.

关键词： Airport traffic flow predictive models deep learning causality graph spatiotemporal analysis

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Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2071-2078页

作者： Yutong Wang Yonglin Tian Jiangong Wang Yansong Cao Shixing Li Bin Tian IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China North Automatic Control Technology Institute in Taiyuan ShanxiChina Qingdao Academy of Intelligent Industries Shandong 266109China

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data *** summary, we point out the development directions of quality inspection under metaverse tide.

关键词： Automated Optical Inspection Metaverses Quality Inspection Transformers

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Parallel Tourism System With Foundation Models and Decentralized Autonomous Organizations

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INTERNATIONAL JOURNAL OF TOURISM RESEARCH 2025年第2期27卷

作者： Huang, Jun Tian, Yonglin Zhang, Tengchao Ni, Qinghua Lin, Fei Lee, Timothy J. Wu, Naiqi Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Macau Univ Sci & Technol Fac Hospitality & Tourism Management Macau Peoples R China

Tourism is a critical driver of economic growth and cultural exchange. However, traditional tourism models often struggle to address challenges such as insufficient understanding of diverse travel preferences, limited capacity for personalized services, and a lack of intelligent, convenient service solutions. In this paper, we propose a virtual-real interactive Parallel Tourism System (PTS) and a Smart Tourism and Lifestyle Service Large Model (STLS-LM) to handle these challenges. By integrating Decentralized Autonomous Organizations (DAOs) and Foundation Models (FMs) supported by Retrieval-Augmented Generation, PTS elevates tourism services by incorporating agentic intelligence and autonomous intelligence, advancing beyond traditional AI-powered applications. The proposed framework enables a deep understanding of task requirements, precise multi-source information sensing, personalized functionality customization, and timely public feedback adoption, forming a comprehensive intelligent travel service system that caters to the evolving needs of modern tourism.

关键词： agentic intelligence autonomous intelligence creative tourism decentralized autonomous organizations large language models parallel intelligence retrieval-augmented generation

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