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IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Liyong Wang Long Jin Haojie Ji Jingyan Wang Yupeng Chen Junzhe Fang State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China State Key Laboratory of Intelligent Vehicle Safety Chongqing China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

The rapid development of intelligent and connected vehicle (ICV) has brought increasing communication data and valuable data assets. However, the diversity and complexity of ICV data make it hard to carry out data security risk assessment. Aiming at the problem, this paper proposes an effective analytical method for the data security risk assessment method of ICVs. Considering the typical scenario for the generated data of ICVs, a data security classification and grading framework is constructed from the aspects of in-vehicle data, personal data, and external environment data. The data security risk assessment model is built for ICVs and includes functional modules such as asset identification, threat identification, impact assessment, vulnerability identification, and risk assessment. This work provides methods and security management and control strategy support for ICV data security risk assessment.

关键词： Industries Connected vehicles Data security Security management Transportation Data models complexity theory Risk management

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FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Wu, Jinxing Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China

Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds. However, there still present many challenges for efficient and robust feature fusion in the field of 3D object detection. In this paper, first, unstructured 3D point clouds are filled in the 2D plane and 3D point cloud features are extracted faster using projection-aware convolution layers. Further, the corresponding indexes between different sensor signals are established in advance in the data preprocessing, which enables faster cross-modal feature fusion. To address LiDAR points and image pixels misalignment problems, two new plug- and-play fusion modules, LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights with perceiving the Euclidean distance of bimodal features are proposed. In BiLiCamFuse, the fusion module with dual attention is proposed to deeply correlate the geometric and textural features of the scene. The quantitative results on the KITTI dataset demonstrate that the proposed method achieves better feature-level fusion. In addition, the proposed network shows a shorter running time compared to existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Cascade of pressure-driven phase transitions in the topological nodal-line superconductor PbTaSe2

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Physical Review B 2022年第6期106卷 L060501-L060501页

作者： Tahir Murtaza Haiyang Yang Jiajia Feng Yi Shen Yongheng Ge Yi Liu Chunqiang Xu Wenhe Jiao Yaokang Lv Christopher J. Ridley Craig L. Bull Pabitra K. Biswas Raman Sankar Wei Zhou Bin Qian Xuefan Jiang Zhenjie Feng Yonghui Zhou Ziming Zhu Zhaorong Yang Xiaofeng Xu Key Laboratory of Quantum Precision Measurement of Zhejiang Province Department of Applied Physics Zhejiang University of Technology Hangzhou 310023 China College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory Chinese Academy of Sciences Hefei 10 230031 China School of Physics and Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing 211189 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China College of Chemical Engineering Zhejiang University of Technology Hangzhou 310014 China ISIS Pulsed Neutron and Muon Source STFC Rutherford Appleton Laboratory Harwell Campus Didcot Oxfordshire OX11 0QX United Kingdom School of Chemistry University of Edinburgh David Brewster Road Edinburgh EH9 3FJ United Kingdom Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Department of Physics Changshu Institute of Technology Changshu 215500 China Materials Genome Institute Shanghai University Shanghai 200444 China

We report a succession of pressure-tuned structural transitions in the topological nodal-line superconductor PbTaSe2, evidenced from synchrotron x-ray diffraction, elastic neutron scattering, Raman spectroscopy, and electrical transport measurements up to 56 GPa, accompanied by first-principles calculations to uncover the evolution of the underlying electronic structure. In contrast to the previously proposed small shift of the Pb Wyckoff coordinate in the sub-GPa regime, our study reveals that it is rather a transition from P6¯m2 (α phase) to P63mc (β phase), subsequently followed by a transition to P6/mmm (γ phase) at ∼7.5 GPa and to Pmmm (δ phase) at ∼44 GPa. In addition, the first-principles calculations unambiguously demonstrate the multiple types of topological fermions associated with these different pressure-driven structures. Collectively, our results not only present the intriguing structural transitions in this topological PbTaSe2 superconductor, they also provide the impetus to study topological phase transitions and their physical consequences in a broader class of topological materials.

关键词： Phase transitions by order Superconducting phase transition Superconductivity Topological superconductors

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An enhanced algorithm for cell-level anomaly segmentation in photovoltaic solar panels using electroluminescence imaging

引用

Energy 2025年 331卷

作者： Ruiyao Duan Yongjian Wang Xisong Chen Shihua Li School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 PR China Institute for Automatic Control and Complex System University of Duisburg–Essen Duisburg 47057 Germany

Electroluminescence imaging is a crucial diagnostic tool for assessing the quality and performance of photovoltaic (PV) modules. However, current research often focuses on defect detection in PV modules, neglecting the need for detailed segmentation at the individual cell level. To address the limitations of existing methods in recognizing complex defects and suppressing background noise, this paper proposes a novel semantic segmentation algorithm (CAAK-Net), capable of identifying anomalies at the cell level of photovoltaic panels. To enhance network performance, CAAK-Net uses K-Net as a baseline and incorporates the Convolutional Block Attention Module (CBAM), Attention Refinement Module (ARM), and Atrous Spatial Pyramid Pooling (ASPP) modules. Experimental results comparing CAAK-Net with mainstream segmentation networks demonstrate its superior segmentation performance, particularly in recognizing defect edges and small-area attached defects. Additionally, we establish a quantitative correlation model between detection errors and PV system efficiency losses, pioneering the translation of pixel-level segmentation accuracy into measurable operational costs, thereby providing an economic assessment framework for industrial PV inspection. Furthermore, the network exhibits a certain degree of robustness in noisy environments, showcasing its segmentation advantages in diverse defect scenarios.

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Efficient Dynamic 3d Shape measurement Technique for Resisting Motion-Induced Error

SSRN

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SSRN 2022年

作者： Yao, Pengcheng Gai, Shaoyan Da, Feipeng The School of Automation Southeast University Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China Shenzhen Research Institute of Southeast University Guangdong Shenzhen518000 China

The phase-shifting profilometry is usually used to acquire dynamic 3D scenarios owning to these merits of dense 3D points, high speed, and simple devices. However, the object in motion will introduce an unknown phase shift, inducing the motion error in 3D shape. Eliminating the motion error with high efficiency is still a significant challenge. This paper analyzes the model of motion error and proposes a novel fringe projection strategy to resist the motion error. Unlike the traditional methods, typically compensating for the motion error with supplementary computing, the proposed method can directly obtain the accurate phase since the designed fringe projection strategy possesses the function of resisting motion error. Therefore, it is more efficient without the post-processing procedure. Simulations and experiments reveal that the proposed method can improve efficiency by about 300% with reasonable accuracy. © 2022, The Authors. All rights reserved.

关键词： Profilometry

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

引用

International Conference on control and Automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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P2P transaction method for distributed energy prosumers based on reputation value

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Global Energy Interconnection 2023年第3期6卷 308-323页

作者： Tao Jiang Ting Hua Hao Xiao Linbo Fu Wei Pei Tengfei Ma Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012P.R.China Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190P.R.China School of Electronic Electrical and Communication Engineering(EECE)University of Chinese Academy of SciencesBeijing 100049P.R.China

Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P *** study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value ***,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was ***,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was ***,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation ***,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation *** results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P *** can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.

关键词： P2P Prosumer Non-cooperative game Reputation value Multi-agent of interest Operation optimization

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Multi view 3D reconstruction method for weak texture objects based on "eye-in-hand" model

Multi view 3D reconstruction method for weak texture objects...

引用

2021 China Automation Congress, CAC 2021

作者： Gong, Xuan Chen, Xin Jian, Xu He, Wenpeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665426473

Size measurement and defect detection of ceramics are required in the production process of ceramic products. Therefore, it is necessary to perform high-precision and fast three-dimensional reconstruction of such targets with smooth surfaces and weak textures. However, the reconstruction of a large number of point clouds from multiple perspectives is time-consuming due to the matching of adjacent feature points. Based on this, a fast point cloud registration strategy based on the "eye-in-hand" model is proposed, which can obtain RGB-D images at multi-view around weak-textured objects with RGB-D camera for rapid point cloud registration and reconstruction. Meanwhile, a 3D point cloud segmentation method based on voxel-grid and euclidean distance clustering is applied to divide the scene point and obtain an accurate 3D model. The experimental results indicate that the multi view 3D reconstruction speed of this method is faster and the accuracy of objects point cloud is within 2 mm. Hence, this method can be well applied in reconstructing the 3D point clouds model for ceramic products. © 2021 IEEE

关键词： Textures

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Prediction model for mechanical properties of hot-rolled strips by deep learning

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Journal of Iron and Steel Research International 2020年第9期27卷 1045-1053页

作者： Wei-gang Li Lu Xie Yun-tao Zhao Zi-xiang Li Wen-bo Wang Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and TechnologyWuhan 430081HubeiChina

The prediction of the mechanical properties of hot-rolled strips is a very complex,highly dimensional and nonlinear problem,and the published models might lack reliability,practicability and ***,a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep ***,the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing ***,a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips,and an improved inception module was introduced into this network to abstract features from different *** comparative experiments were carried out to find the optimal network structure and its ***,the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network,which includes the stepwise regression,ridge regression,support vector machine,random forest,shallow neural network,Bayesian neural network,deep feed-forward network and improved LeNet-5 convolutional neural *** results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

关键词： Deep learning Convolutional neural network Mechanical property Hot-rolled strip

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