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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Yang, Hong-Jun Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Institute of Automation The State Key Laboratory for Management and Control of Complex Systems 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 Institute of Systems Engineering Macau University of Science and Technology The Joint Laboratory of Intelligence Science and Technology Taipa China

ISBN: (纸本)9781665481090

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios. © 2022 IEEE.

关键词： Convolution

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Development of a Propeller with Undulating Fins and Its Characteristics

Development of a Propeller with Undulating Fins and Its Char...

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

作者： Ruichen Ma Yu Wang Rui Wang Shuo Wang 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 CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

This paper addresses some experiments about measurements of thrust and heave force generated by a modular designed biomimetic underwater propeller. The propeller has a ribbon fin that consists of twelve rays linked by a latex membrane. Different forms of waves can be generated by the propeller. A measurement platform is constructed to obtain the thrust and heave force generated by the waves along with the input power of the propeller. Three types of waves are performed on the platform, including sinusoidal waves, fusiform sinusoidal waves and inward counter-propagating sinusoidal waves. By changing the parameters of undulating waves, a total of three hundred series of experiments are implemented. The measurement results of three types of waves are analyzed and the differences among three types of waves are compared.

关键词： Force Propellers Force measurement control systems Standards Robots Current measurement

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LSMR iterative method for general coupled matrix equations

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Journal of Applied Mathematics 2015年第1期2015卷

作者： Toutounian, F. Khojasteh Salkuyeh, D. Mojarrab, M. Department of Applied Mathematics School of Mathematical Sciences Ferdowsi University of Mashhad Iran Center of Excellence on Modelling and Control Systems Ferdowsi University of Mashhad Iran Faculty of Mathematical Sciences University of Guilan Iran

By extending the idea of LSMR method, we present an iterative method to solve the general coupled matrix equations ∑ k=1qAikXkBik=Ci, i=1,2,.,p, (including the generalized (coupled) Lyapunov and Sylvester matrix equa...

By extending the idea of LSMR method, we present an iterative method to solve the general coupled matrix equations ∑ _k=1^qA_ikX_kB_ik=C_i, i=1,2,.,p, (including the generalized (coupled) Lyapunov and Sylvester matrix equations as special cases) over some constrained matrix groups (X₁,X₂,.,X_q), such as symmetric, generalized bisymmetric, and (R,S)-symmetric matrix groups. By this iterative method, for any initial matrix group (X₁⁽⁰⁾,X₂v,.,X_q ⁽⁰⁾), a solution group (X₁∗,X₂∗,.,X_q∗) can be obtained within finite iteration steps in absence of round-off errors, and the minimum Frobenius norm solution or the minimum Frobenius norm least-squares solution group can be derived when an appropriate initial iterative matrix group is chosen. In addition, the optimal approximation solution group to a given matrix group (X¯1,X¯2,.,X¯q) in the Frobenius norm can be obtained by finding the least Frobenius norm solution group of new general coupled matrix equations. Finally, numerical examples are given to illustrate the effectiveness of the presented method. © 2015 F. Toutounian et al.

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Probabilistic Graph Matching with Multiplicative Updating Algorithm for Correspondence between Remote Sensing Images

Probabilistic Graph Matching with Multiplicative Updating Al...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Jing Yang Xu Yang Zhang-Bing Zhou Zhi-Yong Liu School of Information Engineering China University of Geosciences(Beijing) State Key Laboratory of Management and Control for Complex Systems Institute of Automation 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 School of Information Engineering China University of Geosciences(Beijing) TELECOM SudParis Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781665430302

Point correspondence is an important problem in remote sensing images matching task which lays the foundation for the image registration task. Due to the existence of the rotation transformation and the large scale, and the change of scenery between the two obtained images, the point correspondence problem is still challenging. Tradition algorithms such as the SIFT and SURF aim at using the point appearance to find the point correspondence. However, when the above situations happens, the appearance based algorithms will fail to solve the problem. In this paper, the structural information between points is introduced to the correspondence problem and a spectral graph matching algorithm is proposed based on the probabilistic scheme and the multiplicative updating scheme. The proposed algorithm has three advantages compared with the traditional algorithms. First, incorporating the structural cues to the matching problem makes it more robust to the large scale and the geometric transformation. Second, the best assignment between points which is interpreted by a probabilistic manner is figured out by ranking the probabilities. Third, the optimization problem is solved by an approximate spectral method efficiently and a multiplicative updating algorithm is introduced to ensure the final discrete solution. Experiments on the real remote sensing images validate the effectiveness of the proposed algorithm.

关键词： Image registration Software algorithms Approximation algorithms Probabilistic logic Sensors Internet Task analysis

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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An Assist-as-Needed controller for Robotic Rehabilitation Therapy Based on RBF Network

An Assist-as-Needed Controller for Robotic Rehabilitation Th...

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International Joint Conference on Neural Networks

作者： Lincong Luo Liang Peng Chen Wang Zengguang Hou Weiqun Wang 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 CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

The rehabilitation training with active involvement and efforts of impaired subjects can enhance the outcome of rehabilitation therapies. To promote patients' voluntary engagement, a variety of assist as needed (AAN) controllers have been proposed for robot-aided therapy. However, patients' impairment level is not taken into account in the implementation of those control schemes. In this study, a novel AAN controller is developed for upper limb rehabilitation therapy. The control paradigm uses Gaussian radial basis function (RBF) network to learn the model of subjects' motor capability in workspace. The update of weight vectors of RBF network is based on a greedy strategy, which has the potential to promote subjects' voluntary engagement by providing task challenge for them. Considering the difference in the impairment degree of patients, the assistance and impedance level of robot control is regulated based on the task performance of patients. The results of experiments at four healthy subjects verify the feasibility and adaptability of the proposed AAN controller.

关键词： Radial basis function networks Robot sensing systems Training Medical treatment Task analysis Mathematical model

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Generalized Visual-Tactile Transformer Network for Slip Detection

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IFAC-PapersOnLine 2020年第2期53卷 9529-9534页

作者： Shaowei Cui Junhang Wei Xiaocan Li Rui Wang Yu Wang Shuo Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Future Technology University of Chinese Academy of Sciences Beijing 100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 200031 China

Slip detection plays a vital role in robotic dexterous grasping and manipulation, and it has long been a challenging problem in the robotic community. Different from traditional tactile perception-based methods, we propose a Generalized Visual-Tactile Transformer (GVT-Transformer) network to detect slip based on visual and tactile spatiotemporal sequences. The main novelty of GVT-Transformer is its ability to address unaligned vision and tactile data in various formats captured by various tactile sensors. Furthermore, we train and test our proposed network on a public and our visual-tactile grasping datasets. The experimental results show that our method is more suitable for sliding detection tasks than previous visual-tactile learning methods and more versatile.

关键词： Information sensor fusion Perception sensing Intelligent robotics Deep neural networks Visual-tactile fusion perception

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Output Feedback control for MIMO systems with Disturbances Compensation

Output Feedback Control for MIMO systems with Disturbances C...

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European control Conference (ECC)

作者： D.E. Konovalov S.A. Vrazhevsky I.B. Furtat A.S. Kremlev Martin Cech Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia The Laboratory ”Control of Complex System” Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences (IPME RAS) St. Petersburg Russia Pilsen — NTIS - European Center of Excellence University of West Bohemia

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

The paper deals with the novel feedback output control law for multiple-input multiple-output (MIMO) plant with mismatched bounded disturbances. The plant model is described by linear and Lipshitz nonlinear terms. The control law is based on the auxiliary loop method for disturbance estimation and modified backstepping method for control under mismatched disturbances. Differently from existing results based on backstepping method the proposed algorithm allows to reduce the accuracy in steady state, while the existing auxiliary loop based algorithm are feasible under matched disturbances. The results in the England Power System Benchmark illustrate the efficiency of the proposed scheme.

关键词： Backstepping Steady-state control systems Mathematical model Attenuation MIMO communication Estimation

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Optimal Discrete-Time Sliding-Mode control Based on Recurrent Neural Network: A Singular Value Approach

Research Square

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Research Square 2021年

作者： Toshani, Hamid Farrokhi, Mohammad School of Electrical Engineering Iran University of Science and Technology Narmak FarjamSt. Tehran16846 Iran Center of Excellence for Modeling and Control of Complex Systems Iran University of Science and Technology Narmak Farjam St. Tehran16846 Iran

In this paper, a strategy involving the combination of optimal discrete-time sliding-mode control and recurrent neural networks is proposed for a class of uncertain discrete-time linear systems. First, a performance index based on the reaching law and the control signal is defined. Then, the constrained quadratic programming problem is formulated considering the limitations on the control signal as the static constraint. The dynamic and algebraic model of the neural network is derived based on the optimization conditions of the quadratic problem and their relationship with the projection theory. The proposed method prevents the chattering by selecting proper parameters of the twisting reaching law. The convergence of the neural network is analysed using the Lyapunov stability theory. A singular-value-based analysis is employed for robustness of the proposed method. The stability conditions of the discrete-time closed-loop system is analysed by studying eigenvalues of the closed-loop matrix using the singular value approach. The performance of the proposed algorithm is assessed in simulated example in terms of chattering elimination, solution feasibility, and encountering uncertainties and is compared with the recently proposed DSMC methods in the literature. © 2021, CC BY.

关键词： Recurrent neural networks

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RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network

arXiv

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arXiv 2019年

作者： Ni, Zhen-Liang Bian, Gui-Bin Xie, Xiao-Liang Hou, Zeng-Guang Zhou, Xiao-Hu Zhou, Yan-Jie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU. Copyright © 2019, The Authors. All rights reserved.

关键词： Surgical equipment

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