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Novel assisted cough system based on simulating cough airflow dynamics

Bio-Design and Manufacturing 2021年第3期4卷 479-489页

作者： Shuai Ren Jinglong Niu Maolin Cai Liming Hao Yan Shi Weiqing Xu Zujin Luo School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China North Automatic Control Technology Institute Taiyuan 030006China Department of Respiratory and Critical Care Medicine Beijing Engineering Research Center of Respiratory and Critical Care MedicineBeijing Institute of Respiratory MedicineBeijing Chao-Yang HospitalCapital Medical UniversityBeijing 100043China

Cough is a defensive behavior that protects the respiratory system from infection and clears airway *** airflow dynamics have been analyzed by a variety of mathematical and experimental *** this paper,the cough airflow dynamics of 42 subjects were obtained and *** identification model based on piecewise Gauss function for cough airflow dynamics is proposed through the dimensionless method,which could achieve over 90%identification ***,an assisted cough system based on pneumatic flow servo system is *** vacuum situation and feedback control are used to increase the simulated peak cough flow rate,which are important for airway secretion clearance and to avoid airway collapse,*** simulated cough peak flow could reach 5 L/s without the external assistance such as manual pressing,patient cooperation and other ***,the backstepping control is developed to generate a simulated cough airflow that closely mimics the natural cough airflow of *** assisted cough system opens up wide opportunities of practical application in airway secretion clearance for critically ill patients with COVID 2019 and other pulmonary diseases.

关键词： Airflow dynamics Pneumatic system Simulated cough Model identification Airway secretion clearance

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MADDPG Algorithm for Coordinated Welding of Multiple Robots

MADDPG Algorithm for Coordinated Welding of Multiple Robots

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作者： Weibin Chen Lei Hua Leixin Xu Benshun Zhang Mengmeng Li Tao Ma Yang-Yang Chen School of Automation Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper deals with the coordinated welding problem of multi-robot systems by applying a deep reinforcement learning algorithm which is called multi-agent deep deterministic policy gradient(MADDPG).It is assumed that the states and actions of robots are continuous and each robot can only get local information of its neighbors.A novel reward composed of trajectory optimization,coordinated welding and collision avoidance is designed,which yield multi-robot systems to arrive the welding targets quickly,achieve the simultaneous welding for a weld line and avoid collision among *** results are provided to demonstrate the effectiveness of the proposed control algorithm.

关键词： Deep multi-agent reinforcement learning MADDPG Coordinated welding

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Deformation Pattern Shape Sensing for Visuotactile Endoscope

Deformation Pattern Shape Sensing for Visuotactile Endoscope

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

作者： Yanzhe Yu Yuan Tian Shaowei Cui Yupeng Wang Rui Wang Shuo Wang Beijing University of Science and Technology Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The integration of visuotactile sensor technology with endoscopy enables the provision of tactile information for minimally invasive surgeries and other operations. This paper proposes a real-time sensing framework for contact deformation pattern shape, providing a foundation for precise perception in miniaturized visuotactile sensors. Specifically, we first construct a Deformation Pattern shape Descriptor (DPSD) and then track the key points in the descriptor using optical flow to obtain contact deformation shape information. Through extensive qualitative and quantitative experiments, our proposed algorithm achieves an average perception accuracy of around 83.81% for contact pattern shapes. It demonstrates consistent perception accuracy across various contact force magnitudes, directions, and scenarios, highlighting its robustness. Additionally, compared to traditional marker-level methods, it achieves a notable 46.5% improvement in displacement field perception accuracy. This research elevates tactile perception to the dimension of contact pattern shape sensing and has the potential for application in miniaturized visual tactile sensors.

关键词： Visualization Accuracy Shape Deformation Tracking Endoscopes Shape measurement

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Fault Detection and Isolation for Multi-agent systems Under Event-Triggered Communication: A Zonotopic Joint Estimation Method

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IEEE Internet of Things Journal 2025年

作者： Tian, Mingliang Guo, Zhihua Wang, Xing Niu, Ben Wang, Xinjun Wang, Ding Wang, Huanqing Shandong Normal University School of Information Science and Engineering Jinan250014 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China Bohai University Department of Mathematical Science AndAutomatization Institute Jinzhou121000 China

This paper investigates the fault detection and isolation problem of a class of leader-follower multi-agent systems subject to unknown-but-bounded disturbances under an event-triggered communication mechanism (ETCM). Firstly, an adaptive ETCM is proposed to avoid the continuous acquisition of outputs from neighboring agents, thereby reducing the communication burden. Secondly, a joint state and fault observer is designed, and H∞ analysis is utilized to improve the robustness of the observer against external disturbances and event-triggered errors. Next, zonotopic reachability analysis is utilized to derive the estimated intervals of the system state and fault signal, based on which a fault detection and isolation method is developed to determine both when the system is faulty and which agent is at fault. Finally, a fighter formation model is employed to verify the effectiveness of the proposed approach. © 2014 IEEE.

关键词： Multi agent systems

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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An overview on GNSS carrier-phase time transfer research

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Science China(Technological Sciences) 2020年第4期63卷 589-596页

作者： ZHANG Ming Lü JinHu BAI ZhengDong JIANG ZhiQi CHEN BoBo The Key Laboratory of Systems and Control Institute o f Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Institute of Geomatics of Tsinghua University Beijing 100084China Management and Department of Energy and Resources Engineering College of EngineeringPeking UniversityBeijing 100871China

Global navigation satellite system(GNSS)carrier phase observations are two orders of higher accuracy than pseudo-range observations,and they are less affected by multipath *** a result,the time transfer accuracy can reach 0.1 ns,and the frequency transfer stability can reach 1×10^-15 with carrier phase(CP)method,therefore CP method is considered the most accurate and promising time transfer *** focus of this paper is to present a comprehensive summary of CP method,with specific attention directed toward day-boundary clock jump,ambiguity resolution(AR),multi-system time transfer and real-time time ***-boundary clock jump is essentially caused by pseudo-range *** approaches were proposed to solve the problem,such as continuously processing strategy,sliding batch and bidirectional filtering methods which were compared in this ***,researches on AR in CP method were *** scholars attempted to fix the single-difference ambiguities to improve the time transfer result,however,owing to the uncalibrated phase delay(UPD)was not considered,the current studies on AR in CP method were still ***,because four GNSS systems could be used for time-transfer currently,which was helpful to increase the accuracy and reliability,the researches on multi-system time transfer were ***’s more,real-time time transfer attracted more attention nowadays,the preliminary research results were presented.

关键词： carrier phase(CP) time transfer day-boundary clock jump ambiguity resolution(AR) multi-system time transfer real-time time transfer

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On deployment problem of multi-agent systems with delays

On deployment problem of multi-agent systems with delays

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Katarzyna Topolewicz Carlos E. T. Dórea Sorin Olaru Ewa Girejko Faculty of Computer Science Bialystok University of Technology Białystok Poland Department of Computer Engineering and Automation Universidade Federal do Rio Grande do Norte UFRN-CT-DCA Natal RN Brazil Laboratory of Signals and Systems CentraleSupelec Univesity Paris-Saclay France

ISBN: (纸本)9781665476881

The paper analyzes the decentralized deployment policies for scalar multi-agent systems subject to delays. As a main contribution we obtain explicit conditions on the local feedback gains at the level of subsystems, which ensure safe deployment independent of the delays on the measurement of the positions of the neighboring agents. From the theoretic point of view, positive invariance of sets is used as a main concept for the proofs of the results.

关键词： Sufficient conditions Decentralized control Position measurement Gain measurement Robustness Delays Safety

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent systems

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Ma, Ning Yan, Long The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing100070 China

ISBN: (纸本)9781665481090

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (612 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians. © 2022 IEEE.

关键词： Medical imaging

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