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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuqing Wu Jun Wang Rui Wang Jian Wang Kecheng Zhou Xiaoxin Zhang School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Emergency Zhongyi Northeast International Hospital Co. Ltd. Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computational modeling Modulation Context awareness Robustness Medical diagnosis Cardiovascular diseases Context modeling

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Research and practice of lightweight digital twin speeding up the implementation of flexible manufacturing systems 1

Research and practice of lightweight digital twin speeding u...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhang, Xiaodong Hu, Bin Xiong, Gang Liu, Xinjie Dong, Xisong Li, Dongjia School of Information Engineering Yantai Institute of Technology Yantai China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Yantai University Department of Mechanical and Electrical Engineering Yantai China School of Artificial Intelligence Yantai Institute of Technology Yantai China

ISBN: (纸本)9781665433372

Parallel manufacturing in Industry 5.0 requires digital twin to digitize physical systems, building virtual models to open up channels connecting physical systems, information systems, and social systems, and transforming the physical models of the existing production environment to achieve two-way feedback of virtual and real is the current research direction. This paper proposes the modeling idea of lightweight digital twin, extracts core dimensions and performs digital virtual simulation, so as to quickly realize the complete process of two-way feedback, and realize a set of chess flexible parallel manufacturing production lines as a practice for the design of complete lightweight digital twin. © 2021 IEEE.

关键词： Industrial research

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Line-of-Sight Strategy-Based Path-Following System for a Multi-Joint Robotic Fish

Line-of-Sight Strategy-Based Path-Following System for a Mul...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Shijie Dai Chao Zhou Zhengxing Wu Min Tan Junzhi Yu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Naval Research Academy Beijing China State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposes a real-time path-following control system for a multi-joint robotic fish. The mechanical structure and dynamic model of the robotic fish for pathfollowing are first described. Then, the framework of the path-following control algorithm is established based on the built dynamic model, including a modified line-of-sight (LOS) guidance law, an active disturbance rejection control (ADRC)based heading controller and a proportional-integral-derivative (PID)-based speed controller. Specially, the modified LOS strategy is designed to select the tracking points and also provide the desired heading angle. Afterwards, to overcome systematic uncertainties and environmental disturbances, the ADRC method is adopted to design the heading controller. Meanwhile, the PID controller is also developed to maintain an appropriate swimming speed. Finally, simulations in both linear-and circular-path following are presented to validate the effectiveness of the proposed method.

关键词： Robots Robot kinematics Task analysis Real-time systems Oscillators Mathematical model Force

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Research on Collaboration Method of Edge IoT Agent Based on Actor Model

Research on Collaboration Method of Edge IoT Agent Based on ...

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Power and Energy Engineering (ICPEE), International Conference on

作者： Bowen Zhang Weiwei Miao Lei Wei Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Information & Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9781665420471

Focusing on the resource constrained IoT agent collaboration requirements at the edge of the power Internet of things, this paper proposes an edge agent collaborative computation method based on Actor model to satisfy the rapid processing and response requirements of large-scale data in the perception layer of the power Internet of things. Through a unified programming model, this paper can divide the collection and analysis of sensing data in the sensing layer of the power Internet of things into functional components, which can be deployed in different edge IoT agents, to improve the rapid processing ability of edge-side data. At the same time, the components achieve the function cooperation through the message passing mechanism. Finally, this paper implements the proposed scheme based on Akka framework, and designs the edge IoT agent collaborative computing prototype. The prototype system experiments show that the proposed scheme can split the complex services with high resource requirements into different components and deploy them in different edge IoT agents. The proposed method can improve the resource utilization and data processing capacity of the edge IoT agent of the power Internet of things, while ensuring the real-time requirements of edge service analysis.

关键词： Computational modeling Message passing Collaboration Prototypes Programming Data models Real-time systems

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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A Robust Road Segmentation Method Based on Graph Cut with Learnable Neighboring Link Weights

A Robust Road Segmentation Method Based on Graph Cut with Le...

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17th International IEEE Conference on Intelligent Transportation systems & The Asia-Pacific Council on systems Engineering Conference 2014

作者： Jun Yuan Shuming Tang Fei Wang Hong Zhang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences High-Tech Innovation Engineering Center Institute of AutomationChinese Academy of Sciences Institute of Artificial Intelligence and Robotics of Xi'an Jiaotong University School of Information Engineering Minzu University of China

Road region detection is a crucial functionality for road following in advanced driver assistance systems(ADAS).To address the problem of environment interference in road segmentation through a monocular vision approach,a novel graph-cut based method is proposed in this *** novelty of this proposal is that weights of neighboring links(n-links) in a s-t graph are estimated by Multilayer Perceptrons(MLPs) rather than calculating by the neighboring contrast simply in previous graph-cut based *** n-link weights by MLPs reinforces the ability of graph-cut based road segmentation algorithms to tolerate the complex and changeable appearance of road ***,the Gentle Ada Boost algorithm is integrated into the graph-cut framework to estimate the terminal link(t-link) weights in the s-t *** are conducted to show the robustness and efficiency of the proposed method.

关键词： image A Robust Road Segmentation Method Based on Graph Cut with Learnable Neighboring Link Weights Graph

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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Teleoperation control for Bimanual robots based on RBFNN and Wave Variable

Teleoperation Control for Bimanual robots based on RBFNN and...

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2017 9th International Conference on Modelling, Identification and control (ICMIC 2017)

作者： Xingjian Wang Chenguang Yang Junpei Zhong Rongxin Cui Min Wang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of Technology Zienkiewicz Centre for Computational Engineering Swansea University National Institute of Advanced Industrial Science and Technology School of Marine Science and Technology Northwestern Polytechnical University

In this paper,a dual arm control method in a teleoperation system is *** a bimanual robot,moving a common object precisely requires real-time cooperation between the two *** such conditions the force interaction including the internal forces applied on the object must be taken into *** the dynamics models for master device,slave robot and the object are first *** the presence of the uncertainties in the dynamics model of the remote robot,a neural network method is used for compensation in the slave *** order to guarantee the stability of the teleoperation system,the wave variable approach is employed in the communication *** the controllers in both the master and the slave part are designed based on their dynamics *** the tracking convergence and system stability are proven by the Lyapunov *** simulation experiments are carried out to show the good performance of trajectory tracking and force reflection.

关键词： Bimanual Robot Teleoperation control Wave Variable RBF NN

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Hierarchical Task Impedance control of a Serial Manipulator for Minimally Invasive Surgery

Hierarchical Task Impedance Control of a Serial Manipulator ...

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IEEE International Conference on Human-Machine systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

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