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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chengzhi Zhu Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.

关键词： Parameter estimation Filtering Heuristic algorithms Estimation Real-time systems Manipulator dynamics Convergence

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Stiffness Estimation and Intention Detection for Human-Robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation Robot sensing systems Manipulators Robots

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Antenna Cross-correlation based Compressive Subspace Learning for Wideband Spectrum Sensing 19

Antenna Cross-correlation based Compressive Subspace Learnin...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Gong, Tierui Liu, Zhifeng Yang, Zhijia Wang, Gengshan Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Shenyang Institute of Automation 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

ISBN: (纸本)9781728105352

To address the performance degradation of wide-band spectrum sensing by sub-Nyquist sampling (SNS) in wireless fading channels, two compressive subspace learning (CSL) algorithms are proposed for signal subspace learning based on antenna cross-correlations for further improving the sensing performance. Both algorithms are developed based on different organizations of SNS samples, and both exploit space diversity and noise uncorrelations between antennas. We further establish the expressions for statistical covariance matrices (SCMs) obtained by SNS samples in the multi-antenna SNS cognitive radio system. Based on the derived SCM expressions, conditions to ensure SCMs without noise contamination are given. Simulations validate the derived conditions and show the improvement on sensing performance over related works. © 2019 IEEE.

关键词： Covariance matrix

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An Improved Upper Bound on the Maximum Eigenvalue of Exponential Model Based Spatial Correlation Matrices in Massive MIMO systems 19

An Improved Upper Bound on the Maximum Eigenvalue of Exponen...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Gong, Tierui Yang, Guang Mu, Ke Zhao, Chun Yang, Zhijia Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728105352

Massive Multiple-Input, Multiple-Output (MIMO) communications are considered as one of the most promising directions for the next generation communications and have attracted extensive research interests. The using of exponential model is recognized as an effective manner to describe the spatial correlation matrix in a spatially correlated MIMO channel for its simple form and accurate fitting on the experimental measurements. Based on this useful exponential model, an improved upper bound on the maximum eigenvalue of the spatial correlation matrix is given in this paper. Complying with the existing results, the proposed upper bound is also a function of the number of antennas and the absolute value of correlation coefficient. However, the proposed upper bound is obtained using a trace-based derivation, which is tighter than existing results in high spatial correlation massive MIMO systems. Simulations show that the proposed upper bound is closer to the true maximum eigenvalues for both uniform linear array and uniform planar array scenarios, which is expected to be widely used in massive MIMO systems. © 2019 IEEE.

关键词： Eigenvalues and eigenfunctions

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Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion during Conventional Microsurgery Using Machine Learning

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Journal of Medical robotics Research 2021年第3-4期6卷

作者： Fattahi Sani, Mohammad Ascione, Raimondo Dogramadzi, Sanja Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Bristol Heart Institute and Translational Biomedical Research Centre Faculty of Health Science University of Bristol Bristol United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffiled United Kingdom

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-Time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (M

关键词： feature extraction hand tracking machine learning minimally invasive surgery real-Time low-cost hand tracking Robot-Assisted surgery

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Arctic Respiratory Monitoring System

Arctic Respiratory Monitoring System

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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Lukyanov, G. Rassadina, A. Kossareva, V. Stepanov, A.A. Alexeev, R.Z. Itmo University Saint-Petersburg The Faculty of Control Systems and Robotics Russia Larionov Institute of Physical and Technical Problems of the North Sb Ras Department of Heat and Mass Transfer Yakutsk Russia Laboratory of Immunological Researches Yakut Science Centre of Complex Medical Problems Medical Institute M. K. Ammosov North-Eastern Federal University Yakutsk Russia

ISBN: (纸本)9781728169514

The results of heat transfer dynamic processes during the breathing are presented. The study is relevant for investigation of health in the extreme conditions of the Arctic. For estimation of this processes the measurement system of temperature, pressure and velocity pulsations in nostrils are developed. It has been shown that for respiratory system diagnostic it is necessary to analyze such dynamic characteristics as the power spectral density and entropy. © 2020 IEEE.

关键词： Respiratory system

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Fault Diagnosis Based on RseNet-LSTM for Industrial Process

Fault Diagnosis Based on RseNet-LSTM for Industrial Process

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Peifu Yao Shaojie Yang Peng Li China Copper Co.LTD Kunming China 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 University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Aiming at the problems that conventional data-driven diagnosis methods are difficult to adaptively extract effective features from industrial process data, and do not make full use of the time series characteristics of process data, in this paper, a fault diagnosis method based on residual convolutional neural networks and long short-term memory networks (ResNet-LSTM) is developed. Firstly, the local spatial features of process data are captured by the deep residual convolution network. Then, the time series characteristics of process data are extracted by LSTM. Finally, the output of the fault category is performed through the softmax classifier. This method can extract features adaptively, more fully extract features in time series fault data, and effectively reduce the difficulty of deep neural network training. The benchmark Tennessee-Eastman (TE) process is used to validate performance of the proposed method. The ResNet-LSTM model is compared with the CNN, LSTM, ResNets, CNN-LSTM models, and the experiment results show that the ResNet-LSTM method achieves better performance.

关键词： Fault diagnosis Training Time series analysis Neural networks Process control Feature extraction Data mining

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Shaojie Yang Peng Li Shuai Li Xiaofeng Zhou Shanghong Jiang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Information Center of Ansteel Mining Limited Company Anshan China

ISBN: (纸本)9781665401166

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified.

关键词： Fault diagnosis Fault detection Time series analysis Benchmark testing Feature extraction Safety Data mining

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An Accelerated Iterative Learning control Approach for X-Y precision plane motion stage

An Accelerated Iterative Learning Control Approach for X-Y p...

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作者： Tao Zhang Hongyu Yan Danping Jia Hualiang Zhang Bingjie Zhao Xu Lu Yang Liu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology School of Artificial Intelligence Shenyang University of Technology School of Information Science and Engineering Shenyang University of Technology

X-Y precision plane motion stage is widely used in precision motion control fields such as lithography machine workpiece stage,CNC machine tool,Laser measuring system,etc.,which requires high accuracy of the position *** X-Y precision plane motion stage uses two permanent magnet linear synchronous motors(PMLSM) as drivers to drive the motion table to perform XY two-degree-of-freedom *** open-closedloop PD-type iterative learning controller(ILC) approach based on accelerated convergence was proposed to improve trajectory tracking performance of *** interval was changed by real-time correction of the exponential gain matrix in the interval to accelerate the convergence *** to the λ norm,the monotone convergence of the proportion differentiation(PD) was developed for the *** controller consists of two terms:a PID controller which enhances robustness to disturbances and system parameter variability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous *** experimental results show that tracking precision and convergence speed of PMLSM can be effectively improved by the scheme.

关键词： X-Y precision plane motion stage Iterative learning control tracking precision convergence rate

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Fabrication of Translucent Graded Dental Crown Using Zirconia-Yttrium Multi-Slurry Tape Casting 3d Printer

SSRN

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SSRN 2024年

作者： Romario, Yulius Shan Bhat, Chinmai Ramezani, Maziar Pasang, Tim Chen, Zhangwei Jiang, Cho-Pei Graduate Institute of Manufacturing Technology National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan High-value Biomaterials Research and Commercialization Center National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan Department of Mechanical Engineering Auckland University of Technology Auckland1010 New Zealand Department of Engineering Design Manufacturing and Management Systems Western Michigan University KalamazooMI4900-5200 United States Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Mechanical Engineering National Taipei University of Technology Taiwan 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan

This paper aims to fabricate functionally graded dental crown using a multi-slurry tape casting additive manufacturing technology. The different luminescence of the dental crown was obtained with different composition of zirconia and yttria. Zirconia with tunable mechanical properties and translucency are obtained by adding 3, 3.5, 4, 4.5, and 5 mol% of yttrium oxide to zirconia powder. After obtaining the printable slurry with maximum solid loading, the green bodies are prepared using the in-house built high speed multi-ceramic tape casting technology. They are later sintered with two-stage sintering method. After the successful fabrication, the mechanical properties and translucency of the specimens were evaluated with Vickers hardness, three-point bending and translucency parameter tests. Finally, an FGM tooth crown with five photocurable slurries is proposed to demonstrate the translucent gradient effect of sintered part. The solid loading of 80% zirconia and 20% resin delivered samples without any surface cracks. The shrinkage ratio analysis showed that the sintered sample dimension was reduced by 20%, 20%, and 23% along X, Y, and Z directions. The samples fabricated with 3% yttrium oxide to zirconia delivered excellent hardness (1687 HV) and flexural strength (650.6 MPa). However, the relative luminescence increased with increasing the yttrium oxide for 3 – 5 mol%. With the optimized process parameters, the proposed dental crown is fabricated and analyzed for their shrinkage ratio, mechanical, and translucency properties. The study proposes the potential of fabricating customized dental crown with gradient translucent appearance. © 2024, The Authors. All rights reserved.

关键词： Zirconia

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