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6th IEEE Information Technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Yang, Guang Li, Zhihang Yang, Zhijia Cui, Shuping State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781665460033

Photoresist coating technology is an important part of the surface treatment of semiconductor wafers. The presence of bubbles in photoresist can seriously affect the quality of wafers, however, the lack of sufficient bubble samples makes intelligent automatic detection techniques impossible. To solve such a problem, we propose B-GAN based on adversarial generative network, and design a mapping network for potential encoding mapping and a synthetic network for high-quality bubble image generation, so that defective bubble samples can be automatically generated. Experiments prove that our method achieves excellent results. © 2023 IEEE.

关键词： III-V semiconductors

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy 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 Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Performance Comparison of Teleoperation Interfaces for Ultra-Lightweight Anthropomorphic Arms

Performance Comparison of Teleoperation Interfaces for Ultra...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Filip Zorić Alejandro Suarez Goran Vasiljević Matko Orsag Zdenko Kovačić Anibal Ollero Laboratory for Robotics and Intelligent Control Systems (LARICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia GRVC Robotics Lab University of Seville Sevilla Spain

This paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

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Enhanced cathodic activity by tantalum inclusion at B-site of La_(0.6)Sr_(0.4)Co_(0.4)Fe_(0.6)O_(3)based on structural property tailored via camphor-assisted solid-state reaction

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Journal of Advanced Ceramics 2022年第8期11卷 1330-1342页

作者： Dingyu XIONG Sefiu Abolaji RASAKI Yangpu LI Liangdong FAN Changyong LIU Zhangwei CHEN Additive Manufacturing Institute Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics Shenzhen UniversityShenzhen 518060China Department of Chemical and Petroleum Engineering University of CalgaryCalgary T2N1N4Canada College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060China

Lanthanum strontium cobalt ferrite(LSCF)is an appreciable cathode material for solid oxide fuel cells(SOFCs),and it has been widely investigated,owing to its excellent thermal and chemical ***,its poor oxygen reduction reaction(ORR)activity,particularly at a temperature of≤800℃,causes setbacks in achieving a peak power density of>1.0 W·cm^(-2),limiting its application in the commercialization of *** improve the ORR of LSCF,doping strategies have been found ***,the porous tantalum-doped LSCF materials(La_(0.6)Sr_(0.4)Co_(0.4)Fe_(0.5)7Ta_(0.03)O_(3)(LSCFT-0),La_(0.6)Sr_(0.4)Co_(0.4)Fe_(0.5)4Ta0.06O_(3),and La_(0.6)Sr_(0.4)Co_(0.4)Fe_(0.5)Ta0.1O_(3))are prepared via camphor-assisted solid-state reaction(CSSR).The LSCFT-0 material exhibits promising ORR with area-specific resistance(ASR)of 1.260,_(0.5)80,0.260,0.100,and 0.06Ω·cm^(2)at 600,650,700,750,and 800℃,*** performance is about 2 times higher than that of undoped La_(0.6)Sr_(0.4)Co_(0.4)Fe_(0.6)O_(3)with the ASR of 2.515,1.191,_(0.5)96,0.320,and 0.181Ω·cm^(2)from the lowest to the highest *** material characterization,it was found that the incorporated Ta occupied the B-site of the material,leading to the enhancement of the ORR *** the use of LSCFT-0 as the cathode material for anode-supported single-cell,the power density of>1.0 W·cm^(-2)was obtained at a temperature<800℃.The results indicate that the CSSR-derived LSCFT is a promising cathode material for SOFCs.

关键词： tantalum-doped lanthanum strontium cobalt ferrite(LSCF) solid oxide fuel cell(SOFC) oxygen reduction reaction(ORR) solid-state synthesis reaction clean energy conversion

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Development and Optimization of a WebGL-Based Mechanical Model Simulation Platform 4th

Development and Optimization of a WebGL-Based Mechanical Mod...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wang, Xintong Tian, Hao Fang, Jingzhe Zhang, Hualiang Zhang, Tao Software College Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789819778867

The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation platforms are severely limited by the issue of sluggish rendering performance, which frequently arises when obtaining high-quality rendering. When drawing high demand mechanical models, a lot of processing power is frequently needed, which makes real-time rendering speed unsatisfactory for practical purposes. In order to achieve real-time rendering and simulation of 3D robots, this experiment built and implemented a WebGL-based 3D simulation platform. Additionally, the simulation platform can be optimized by increasing frame rate and rendering speed without sacrificing rendering effect by building a mixed bounding box. © The Author(s) 2025.

关键词： Digital elevation model

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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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第42届中国控制会议

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

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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Prior Knowledge Informed Neural Network for Subsurface Flow Prediction

Prior Knowledge Informed Neural Network for Subsurface Flow ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng 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

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Data-driven deep learning has been widely applied to solve subsurface flow problems. However, it is difficult to achieve accurate prediction with pure data-driven methods, especially when there is only a small amount of valid data. Prior knowledge is pre-existent and comes from an independent source. The neural network can be informed by prior knowledge to improve algorithm performance even with limited and sparse data. In this study, prior knowledge informed neural network (PKINN) is proposed to predict subsurface flow. The prior knowledge, on the one hand, is embedded in the neural networks as integrated loss function to inform the training process of deep learning methods. On the other hand, it is used as part of input term of neural network to improve calculation accuracy. Subsurface flow models are built to verify the proposed PKINN model. The results show that the PKINN method can solve subsurface flow problems effectively.

关键词： Knowledge engineering Deep learning Training Industrial electronics Solid modeling Accuracy Neural networks

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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Soft robotics:Morphology and Morphology-inspired Motion Strategy

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IEEE/CAA Journal of Automatica Sinica 2021年第9期8卷 1500-1522页

作者： Fan Xu Hesheng Wang Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China

robotics has aroused huge attention since the *** of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental ***,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured *** decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing *** presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific *** paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

关键词： Soft continuum manipulator soft gripper soft mobile robot soft robot control method soft robot modeling method soft robotics

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Wearable multichannel pulse condition monitoring system based on flexible pressure sensor arrays

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Microsystems & Nanoengineering 2022年第1期8卷 249-258页

作者： Jie Wang Yirun Zhu Zhiyong Wu Yunlin Zhang Jian Lin Tao Chen Huicong Liu Fengxia Wang Lining Sun Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215123China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province&College of Information EngineeringTaiyuan University of TechnologyTaiyuan 030024China Suzhou Institute of Nano-tech and Nanobionics Chinese Academy of SciencesSuzhou 215123China

Pulse diagnosis is an irreplaceable part of traditional Chinese medical ***,application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data ***,a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was *** pulse sensing platform simultaneously detected pulse conditions at three pulse positions(Chi,Cun,and Guan).These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping,which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse ***,the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse *** addition,this system could provide full information on the temporal and spatial dimensions of a person’s pulse waveform,which is similar to the true feelings of doctors’*** innovative,cost-effective,easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care.

关键词： multichannel system replace

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