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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Hu, Xiaofang Wang, Leimin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665412872

This article discusses the uniform stability of Caputo fractional-order memristive neural networks (FMNNs) with discrete delay and distributed delay. By virtue of fractional-order Razumikhin-type theorem, interval matrix method, differential inclusion and set-valued mapping, a LMI-based standard is acquired to certify uniform stability of the addressed FMNNs. Lastly, the validity of the constructed criterion is manifested by an example. © 2021 IEEE.

关键词： Time delay

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Survival prediction of LGG patients based on deep learning of MRI data and genomic data integration

Survival prediction of LGG patients based on deep learning o...

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2021 China Automation Congress, CAC 2021

作者： Sun, Lingzhi He, Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

Low-grade gliomas (LGG) is the most common primary intracranial tumor, with high incidence rate, high recurrence rate, high mortality rate and low cure rate. Therefore, it is necessary to predict the survival of LGG patients in diagnosis. Considering the complementarity of information, the new proposed algorithm in this study is designed to integrate Magnetic Resonance Image (MRI) data and gene expression data using deep learning method to predict the Disease Specific Survival (DSS) of LGG patients. Firstly, MRI data of 44 patients is screened from TCIA database, and then the corresponding gene expression data of 44 patients is searched from TCGA database. Then, 724 image feature data extracted from MRI data are filtered and extracted by deep learning method, and DSS tags are used to train the model;deep learning method is used to extract 20530 features of gene expression data, and DSS tags are also used for training. As a contrast, the deep learning method is used to integrate the two features to train the model. Experiments are evaluated on MRI data, gene expression data and the integration data of MRI and gene expression data, respectively. The results show that by using the integration of MRI data and gene expression data performs better than using single data in terms of the time-dependent receiver operating characteristic(ROC) and the area under the curve (AUC) of the ROC curve criteria. © 2021 IEEE

关键词： Magnetic resonance imaging

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A Research on Attitude control of Quadrotor UAV Based on Equivalent-Input-Disturbance Approach and Model Predictive control

A Research on Attitude Control of Quadrotor UAV Based on Equ...

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Intelligent systems, International Workshop on

作者： Zihao Ning Jinhua She Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper discusses the problem of attitude control of a quadrotor unmanned aerial vehicles (UAV). As external disturbances and system nonlinearities will affect the attitude control of quadcopter UAV, making it difficult for general control methods to achieve satisfactory results. To address this problem, this paper applies the equivalent-input-disturbance (EID) approach and the model predictive control (MPC) to the attitude control of a quadcopter UAV to ensure the reference tracking and suppress the influence of the total disturbances on the attitude of the quadcopter UAV, and the simulation results prove the effectiveness of the method.

关键词： Attitude control Simulation Conferences Autonomous aerial vehicles Prediction algorithms control systems Intelligent systems Quadrotors Predictive control Intelligent control

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CPO-VMD Method for Denoising sEMG Signals

CPO-VMD Method for Denoising sEMG Signals

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Jingwen Liu Juan Zhao Seiichi Kawata Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

In the field of biomedical engineering, surface electromyography (sEMG) is a key tool for monitoring muscle activity and is widely used in various fields such as human- computer interfaces, muscle fatigue assessment, and rehabilitation training. However, sEMG signals are often affected by power line interference and other noise sources during the acquisition process, which may mask useful information. In this study, a new method combining the variational mode decomposition (VMD) and the crown porcupine optimization (CPO) algorithm, named CPO-VMD, is proposed, aiming to optimize the VMD parameters to improve the denoising effect of sEMG signals. By automatically adjusting the key parameters of the VMD through the intelligent algorithm, this method solves the problem of difficult parameter selection, furthermore, enhancing the denoising efficiency. In this paper, a simulated sEMG signal is constructed and the VMD parameters are optimized by CPO. The experimental results show that the method effectively improves the quality of sEMG signals and provides a more accurate idea for signal processing in muscle fatigue assessment and rehabilitation applications.

关键词： Training Noise reduction Signal processing algorithms Interference Muscles Fatigue Optimization Surface treatment Monitoring Signal to noise ratio

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Efficient Dynamic 3d Shape measurement Technique for Resisting Motion-Induced Error

SSRN

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SSRN 2022年

作者： Yao, Pengcheng Gai, Shaoyan Da, Feipeng The School of Automation Southeast University Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China Shenzhen Research Institute of Southeast University Guangdong Shenzhen518000 China

The phase-shifting profilometry is usually used to acquire dynamic 3D scenarios owning to these merits of dense 3D points, high speed, and simple devices. However, the object in motion will introduce an unknown phase shift, inducing the motion error in 3D shape. Eliminating the motion error with high efficiency is still a significant challenge. This paper analyzes the model of motion error and proposes a novel fringe projection strategy to resist the motion error. Unlike the traditional methods, typically compensating for the motion error with supplementary computing, the proposed method can directly obtain the accurate phase since the designed fringe projection strategy possesses the function of resisting motion error. Therefore, it is more efficient without the post-processing procedure. Simulations and experiments reveal that the proposed method can improve efficiency by about 300% with reasonable accuracy. © 2022, The Authors. All rights reserved.

关键词： Profilometry

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Stabilization of discrete time stochastic linear systems with time delay via event-triggered control

Stabilization of discrete time stochastic linear systems wit...

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2021 China Automation Congress, CAC 2021

作者： Dong, Jian Zong, Xiaofeng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

This paper presents the stabilization problem of a class of discrete-time stochastic linear systems with time delay using an event-triggered control(ETC) strategy. For the general linear systems, we propose a new event triggering mechanism and deduce sufficient conditions for mean square exponentially stabilizability(MSES) and almost sure exponentially stabilizability(ASES) via ETC. The results show that the stability of the system depends on the system parameters (time delay, noise, event trigger threshold parameter, and unstable characteristic value of the original system). When the feedback control is disturbed by low-intensity noise, the unstable system can be stable in the mean square. Finally, the validity of the theoretical results is illustrated through a simulation example. © 2021 IEEE

关键词： Linear systems

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Motor Imagery EEG Recognition Based on Generative and Discriminative Adversarial Learning Framework and Hybrid Scale Convolutional Neural Network

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Dang, Xiaoyuan Liu, Guorui Tang, Xianlun Wang, Shifei Wang, Tianzhu Zou, Mi School of Intelligent Engineering Chongqing College of Mobile Communication Chongqing401520 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Motor imagery (MI) Electroencephalogram (EEG) signal recognition is an important part of brain computer interface (BCI). Nowadays, deep learning has been successfully applied to MI signal classification. However, due to the expensive equipment of EEG acquisition and the limitation of acquisition site that gives rise to the EEG data scarcity, and thus impacts deep learning model classification. Therefore, this paper proposes a novel EEG time-frequency transition method, which uses the continuous wavelet transform adaptive generate EEG time-frequency map. Generative and discriminative adversarial learning framework (GADALF) is proposed to enhance data. The correlation analysis of EEG time-frequency map is carried out by combining event related synchronization (ERS) with event related desynchronization (ERD). Based on the analysis results, the hybrid scale convolutional neural network (HSCNN) is proposed by improving the convolutional neural network. This network combines multi-dimensional convolution with channel attention mechanism to decode the hidden energy information of the α and β bands for EEG time-frequency map classification. The results showed that the proposed method can achieve higher recognition rates on the laboratory-collected dataset and the dataset BCI competition IV 2b compared with other methods. Finally, this paper designs an intelligent wheelchair control system based on a BCI that verifies the effectiveness of the system by applying the proposedmethod to the system through online experiments. © 2022,IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Convolutional neural networks

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Coverage control of Unmanned Aerial Vehicles for Periodical Monitoring of Geohazards

Coverage Control of Unmanned Aerial Vehicles for Periodical ...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Zhang, Wei Zhai, Chao School of Automation China University of Geosciences Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Ministry of Education Wuhan430074 China

ISBN: (纸本)9780738146577

Landslides have become one of the major hazards endangering the lives and property of people all over the world. In order to improve the early warning of geohazards, a cooperative coverage control algorithm of unmanned aerial vehicles (UAV) is developed in this paper. The proposed control algorithm is integrated with the airborne radar to periodically monitor the surface deformation, which is closely related to the emergence of geohazards. A Lyapunov function is constructed to ensure the stability of UAVs driven by the proposed control algorithm. Numerical simulations are conducted to substantiate the coverage control approach for UAVs in the uncertain environment. This work provides new insight into the effective monitoring and early warning of geohazards with the assistance of UAVs. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Modeling the Kinematics and Dynamics of the Pedaling Lower-Limb Rehabilitation Robot

Modeling the Kinematics and Dynamics of the Pedaling Lower-L...

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Intelligent systems, International Workshop on

作者： Hao Zhang Juan Zhao Feng Wang Seiichi Kawata Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

Lower-limb rehabilitation robots are becoming increasingly prevalent in rehabilitation training. Accurate modeling helps to develop an effective rehabilitation program. This paper analyzes the physiological structure and joint motion characteristics of the human lower limbs using a pedaling lower-limb rehabilitation robot. We establish a simplified three-link model of the human lower limbs and apply the Denavit-Hartenberg (D-H) parametric method to analyze the joint range of motion, determining geometric parameters and boundary conditions. The forward kinematics analysis derives the expression for the end position of the three-link model, and inverse kinematics calculates joint angles, velocities, and accelerations. Using Lagrange's dynamical equations, we compute the torque required by each joint for different motion states, establishing the foundation for subsequent controller design. Finally, joint angles and torques were simulated in MATLAB. The results verified the feasibility of the model.

关键词： Training Analytical models Torque Computational modeling Dynamics Kinematics Assistive robots Robot sensing systems Mathematical models Optimization

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Oscillation Suppression Method based on Power control Optimization of the Wind Farms 3

Oscillation Suppression Method based on Power Control Optimi...

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3rd IEEE International Power Electronics and Application Conference and Exposition, PEAC 2022

作者： Yang, Jiaqi Wang, Shaojun Li, Zhen Tian, Xu Liu, Bin Liu, Xiangdong Beijing Institute of Technology School of Automation Beijing China Co.Ltd Bayannur Power Supply Bureau Inner Mongolia China State Key Laboratory for Intelligent Control and Decision of Complex Systems Beijing China Beijing University of Mining and Technology Beijing Electric Engineering and Automation Beijing China Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (数字)9781665491419

ISBN: (纸本)9781665491419

Oscillation issues in the grid-connected wind farm system can severely deteriorate the power system security and lead to serious damage. Based on the impedance analysis method, many oscillation suppression methods have been proposed for reshaping the impedance characteristics of system to avoid the risk of oscillations. However, modifying converters' controllers to reshape the wind turbines (WTs) impedance and usage of additional damping devices for reshaping the grid impedance both are time-consuming and costly. Therefore, based on the sequence impedance model of the grid-connected wind farm system, this paper proposes a novel oscillation suppression method by optimizing the power control of wind farm. The system stability margin can be improved by modifying the number of online WTs and the output power of each WT to reshape the impedance of wind farm, under the precondition that the output power demand of wind farm is satisfied. Then, the intelligent particle swarm optimization (PSO) algorithm is used to solve the optimization approach to obtain the number of online WTs with optimal stability margin. Finally, the simulation confirms the effectiveness of the proposed oscillation suppression method. © 2022 IEEE.

关键词： System stability

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