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检索条件"机构=Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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Image Jacobian Matrix Estimation based on Fuzzy Adaptive Robust Kalman Filter

Image Jacobian Matrix Estimation based on Fuzzy Adaptive Rob...

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第37届中国控制会议

作者： Zhenzhu Liu Xinmei Wang Zhenjiang Feng Zhongzhao Xie Shuai Ke School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Aiming at the problem of image Jacobian matrix estimation, this paper proposes a method to get the motion state estimation of the object feature point at the current time by using the combination of robust Kalman filter and fuzzy adaptive method from the image feature space, and the estimation of the image Jacobian matrix can be obtained. Firstly, an adaptive robust decorrelation Kalman filter algorithm with colored measurement noise is proposed by reconstructing process equation and measurement equation and combining the mathematical characteristics of the standard Kalman filter noise. Secondly, by monitoring if the ratio between theoretical residual and actual residual is near 1, the fuzzy inference system constantly adjust the weighted measurement noise covariance and recursively correct the measurement noise covariance of the adaptive Kalman filter, and thus be able to estimate the position and velocity of the object feature point at the current time in the image space more accurately, then the estimation of image Jacobian matrix can be achieved accurately under unknown dynamic environment. The feasibility and superiority of the proposed method can be verified by the simulation and experimental results.

关键词： Image Jacobian Matrix Estimation Robust Kalman Filter Fuzzy Adaptive

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Research on Digital Hearing Aid Speech Enhancement Algorithm

Research on Digital Hearing Aid Speech Enhancement Algorithm

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第37届中国控制会议

作者： Junlei Song Yuan Meng Junming Cao Jin Fang Kaifeng Dong Fang Jin Wenqin Mo School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The microphone array speech enhancement algorithm（MASEA）, the minimum mean square error algorithm for short-time logarithmic spectrum estimation based on voice activity detection（VAD-LSA-MMSE）, and the Wiener filtering algorithm based on voice activity detection（VAD-Wiener） are currently the three most commonly used speech enhancement algorithms. Among them, the MASEA algorithm has some disadvantages such as poor noise reduction effect. VAD-LSA-MMSE algorithm has some disadvantages of relying on high SNR and introducing music noise, thereby reducing the intelligibility. The VAD-Wiener algorithm has some disadvantages such as higher SNR requirements. Aiming at the shortcomings of these three speech enhancement algorithms, based on the VAD algorithm and the MASEA algorithm, this paper proposed a new speech enhancement algorithm by combining the characteristics of Wiener filtering algorithm and LSA-MMSE algorithm. The new speech enhancement algorithm is a VAD-based microphone array speech enhancement algorithm（VAD-MASEA）. VAD-MASEA is better than the other three algorithms in noise reduction, speech enhancement and voice intelligibility, and has the characteristics of adapting to a lower SNR environment. This paper used MATLAB to carry out experimental research, including the new algorithm and the three existing algorithms were simulated and compared the signal waveforms of the four algorithms. Experimental results show that the proposed VAD-MASEA algorithm overcomes the high SNR requirement and can be used in low SNR environments and obtain highly intelligible enhanced signals.

关键词： Voice enhancement Wiener filtering VAD-LSA-MMSE

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A Safety Assessment Method Based on Online Sequential Extreme Learning Machine（OS-ELM） in Deep Drilling Process

A Safety Assessment Method Based on Online Sequential Extrem...

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第37届中国控制会议

作者： Yupeng Li Weihua Cao Chao Gan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Accurate and timely assessment of drilling system is key for achieving safety and efficiency in deep drilling. In this paper, an online assessment model is proposed by applying online sequential extreme learning machine（OS-ELM）. The model has been tested through the actual drilling data for drilling system safety assessment and accidents early warning. By analyzing the mechanism characteristics of accidents, well logging parameters are chosen as the input and accident types are chosen as the output. Owing to the OS-ELM is capable of updating network parameters based on new arriving data without retraining historical data, the model can be updated online for specific formation accidents information to make it more adaptable to a particular environment. The numerical test results show that, comparing with other widely used assessment techniques like support vector machines（SVM） and back propagation（BP）, the proposed model has a higher accuracy and shorter recognition time.

关键词： safety assessment extreme learning machine deep drilling online learning

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Piecewise control Strategy for a Planar Four-link Underactuated Manipulator with Friction

Piecewise Control Strategy for a Planar Four-link Underactua...

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第37届中国控制会议

作者： Haoqiang Chen Xuzhi Lai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

A piecewise control strategy is proposed to realize the position-posture control of a planar four-link Active-PassiveActive-Active（APAA） underactuated manipulator（UM）. Specially, the particle swarm optimization（PSO） algorithm is used to obtain the target angle of all links based on the constraints of control objects of the system. The overall control process of the system is divided into two stages: Firstly, we design a fuzzy-PI controller for the first link to realize the control target of the passive link, and the error between the current angle and the desired angle of the passive link is converged to zero by adjusting the velocity of the first link. Secondly, the position mode of the servo controller is adopted to control the active links move to their target angles, respectively. Finally, the experimental results of the real system verify the effectiveness of the proposed control strategy.

关键词： underactuated manipulator planar four-link fuzzy-PI control position-posture control

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A control Strategy with Zero Residual Vibration for A Planar Single-Link Flexible Manipulator

A Control Strategy with Zero Residual Vibration for A Planar...

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第37届中国控制会议

作者： Qingxin Meng Xuzhi Lai Ze Yan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The residual vibration（RV） problem of the flexible link manipulators（FLMs） is very difficult to solve due to the low stiffness and the underactuated feature of these *** paper presents a control strategy with zero RV for a planar singlelink flexible manipulator（PSLFM）.The stable control objective of the system is to stabilize the PSLFM at a target equilibrium point with zero ***,the dynamic model of the PSLFM is built by using the assumed mode method（AMM）.Then,we transform the control to the trajectory tracking control.A forward trajectory and a reverse trajectory are obtained by using a bidirectional trajectories planning ***,these two trajectories are connected by using the genetic algorithm（GA）.After doing this,we get a trajectory of the system from the initial equilibrium point to the target equilibrium ***,we design a trajectory tracking controller based on the sliding mode variable structure control method to control the PSLFM track this *** simulation results show that the PSLFM arrives the target equilibrium point with zero RV,which demonstrates the effectiveness of this control strategy.

关键词： Planar single-link flexible manipulator Underactuated systems Zero residual vibration Trajectory tracking control

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Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Electroencephalogram Emotion Recognition Based on A Stacking...

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第37届中国控制会议

作者： Qiao Xie Zhen-Tao Liu Xue-Wen Ding School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

To improve the accuracy of Electroencephalogram(EEG) emotion recognition,a stacking emotion classification model is proposed,in which different classification models such as XGBoost,LightGBM and Random Forest are integrated to learn the *** addition,the Renyi entropy of 32 channels' EEG signals are extracted as the feature and Linear discriminant analysis(LDA) is employed to reduce the dimension of the feature *** proposal is tested on the DEAP dataset,and the EEG emotional states are accessed in Arousal-Valence emotion space,in which HA/LA and HV/LV are classified,*** result shows that the average recognition accuracies of 77.19%for HA/LA and 79.06%for HV/LV are obtained,which demonstrates that the proposal is feasible in EEG emotion recognition.

关键词： Emotion recognition Electroencephalogram Stacking model Renyi entropy

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Hierarchical Prediction Model Based on BP Neural Network for Predicting CO/CO2 in Iron Ore Sintering Process

Hierarchical Prediction Model Based on BP Neural Network for...

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第37届中国控制会议

作者： Ben Xu Xin Chen Yang Zhou School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production *** find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.

关键词： carbon efficiency sintering process back-propagation neural network（BPNN）

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Finite-time H∞ state estimation for discrete time-delayed stochastic switched genetic regulatory networks with uncertain sojourn probabilities

Finite-time H∞ state estimation for discrete time-delayed s...

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第37届中国控制会议

作者： REN Chuanyu WAN Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper investigates the problem of finite-time H∞ state estimation for discrete-time stochastic switched genetic regulatory networks（GRNs） with time-varying delays and exogenous disturbances. A new discrete time-delayed stochastic switched GRN model with uncertain sojourn probabilities is devised, which is more general than the switched GRNs model with completely known sojourn probabilities. The sufficient conditions which guarantee the stochastic finite-time boundedness of the estimation error dynamics with a prescribed H∞ disturbance attenuation level are derived. By solving several matrix inequalities,the state estimator parameters can be obtained. A numerical example is given to illustrate the effectiveness of our results.

关键词： Genetic regulatory networks（GRNs） Finite-time H∞ state estimation Uncertain sojourn probabilities

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Improved Kriging for Drilling Visualization Based on Quantum Genetic Algorithm

Improved Kriging for Drilling Visualization Based on Quantum...

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第37届中国控制会议

作者： Zheng Zhang Xuzhi Lai Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

To realize drilling visualization, an effective interpolation method is essential to construct three-dimensional *** is an effective interpolation method commonly used by geologists. However, the variogram model parameters in traditional Kriging have a certain subjectivity, which will influence the accuracy of interpolation. Quantum Genetic Algorithm（QGA）is introduced to optimize the variogram model parameters selection in this paper. Elevation values of boreholes are used as data sets for simulation. The results show that the proposed improved Kriging has a better prediction accuracy.

关键词： Kriging Quantum Genetic Algorithm Optimization Interpolation

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Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples

Convolutional Neural Networks for Facial Expression Recognit...

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第37届中国控制会议

作者： Zhongzhao Xie Yongbo Li Xinmei Wang Wendi Cai Jing Rao Zhenzhu Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Facial expression recognition(FER) plays an important role in human-machine interaction. An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Hence,facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing *** experiments employed to evaluate our technique were carried out using two largely used public databases(CK+, JAFFE).A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods-97.85% of accuracy in the CK+ database-it is fast to train,and it allows for real time facial expression recognition with standard computers.

关键词： Facial Expression Recognition Human-machine Interaction Convolutional Neural Network

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