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

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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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Low Resource-Consuming Cyber-physical System Sampling Method using Bernoulli Circulant Matrix

Low Resource-Consuming Cyber-physical System Sampling Method...

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第37届中国控制会议

作者： Xing Jin Xiang Yang Dian Zhang Jingjing Zhang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Cyber-physical System(CPS) have a high requirement on real-time property, and it is difficult to improve the sampling efficiency base on traditional sampling theory. In this paper, the compression sensing(CS) theory is applied to the sampling compression process of CPS system. The CS theory was used to the sampling compression method of CPS system. The Bernoulli circulant matrix, which is easy to be realized and stored, and its construction algorithm were designed to simplify the realization of CS theory in CPS. It is concluded that for random data set, the compression ratio increases from 14.06 % to 42.18 % and the reconstruction error decreases from 27.65 to 1.28 with increasing repetition times. Note that the sampling time are around tens of microseconds and the reconstruction time are around several milliseconds, which indicates a high real-time performance for CPS. In addition, for image data set, the compression ratios are about 42.90 % which indicates a high compression ratio and huge storage resources saving. More importantly, the sampling time and reconstruction time are only several microseconds and several seconds respectively, which indicates a high real-time performance for CPS.

关键词： Cyber-physical System Compressive sensing Sampling Bernoulli circulant matrix

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A novel augmented Lyapunov functional for the stability analysis of delayed neural networks

A novel augmented Lyapunov functional for the stability anal...

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第37届中国控制会议

作者： Fei Long Leichao Pang Chuan-Ke Zhang Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper investigates the stability of neural networks with a time-varying *** on the good effectiveness of the augmented Lyapunov-Krasovskii functional(LKF),some useful integral vectors are summarized and used to construct single integral terms with augmented quadratic integrand so as to develop a novel augmented LKF *** an extended reciprocally convex matrix inequality and an auxiliary function-based inequality are utilized to estimate the derivative of the *** a result,an improved stability criterion is ***,the advantage of proposed method is demonstrated by a numerical example.

关键词： Augmented Lyapunov-Krasovskii functional stability neural networks time-varying delay

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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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Lithologic identification for imbalanced logging data based on AdaC2-SVM in drilling process

Lithologic identification for imbalanced logging data based ...

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第37届中国控制会议

作者： Xi Chen Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

To identify some special formation lithology with imbalanced logging data, a framework of Multi-layer lithology identification method is proposed. In this framewoke, some special lithology is divided into one class in the first layer, and each lithology is separated in the second layer. A novel algorithm of Ada Cost2-support vector machine（AdaC2-SVM） is put forward using logging data of actual well located in Karamay for training, and the support vector machine-recursive feature elimination（SVM-RFE） is adopted to select attribute, and logging data from another well nearby is used for testing. Experiment result shows the G-mean and accuracy of our method is up to 95.3% and 94.4%, which has better performance than random forest（RF）algorithm, particle swarm optimization-support vector machine（PSO-SVM） algorithm and improved PSO-SVM（IPSO-SVM）algorithm. In the future, the proposed method have a good prospect and give a valuable result for geology research.

关键词： Lithologic identification Ada C2 Support vector machine Imbalanced data Drilling process

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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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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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RBF Neural Network Based-PID control for Weight on Bit During Drilling Operation

RBF Neural Network Based-PID Control for Weight on Bit Durin...

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第37届中国控制会议

作者： Sike Ma Min Wu Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Deep drilling is a costly project and efficiency is of paramount importance. The weight on bit is one of the main operating parameters that influences the drilling efficiency and it was controlled by manual before. But after people saw the giant potential of an auto-drilling system in increasing the drilling efficiency, more and more studies on the feed back control of weight on bit have emerged. This paper mainly studied weight on bit dynamic under the variational formation based on a lumped parameter model and a self-tuning PID controller for weight on bit control. The parameters of the PID controller are tuned by using gradient descent method and RBF neural network identification.

关键词： Weight on bit auto-drilling system RBF neural network PID

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R-H Measurement System Based on Anomalous Hall Effect Devices

R-H Measurement System Based on Anomalous Hall Effect Device...

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第37届中国控制会议

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

With the application of magnetic thin films becoming more and more widespread,people pay more and more attention to the performance *** order to obtain a magnetic film with a specific performance,it is very important to judge the quality of the magnetic film and measure the magnetic properties of the ***,with the increase of the film preparation process,the thickness of the prepared film is getting thinner and the magnetic moment signal contained therein is also *** brings a certain degree of difficulty to the traditional measurement *** example,the VSM system that obtains the hysteresis loop by measuring the magnetic moment signal has become somewhat inadequate for the measurement of ultra-thin *** order to solve this issue,a new method based on anomalous Hall effect is introduced in this *** test system of this system adopts the four-probe measuring method,a constant current is applied across the surface of the film sample,and the abnormal Hall voltage is measured at the other two *** R-H curve of the sample can be obtained through *** compared to VSM measurement,this method is simpler and stable,more accurate,which can greatly reduce the anomalous Hall-effect device R-H characteristic measurement cost.

关键词： anomalous Hall effect R-H measurement measurement system

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