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检索条件"机构=Hubei Key Laboratory of Advanced Control and IntelligentAutomation for Complex Systems"

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Dense Depth Estimation with Absolute Scale

Dense Depth Estimation with Absolute Scale

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第37届中国控制会议

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

Considering the difficulties in estimating depth from single image, in this paper, we propose a method to obtain the absolute scale depth map by combining the convolution neural network and depth filter. We compute relative transformation between consecutive frames by direct tracking features, which are extracted from RGB images and whose depthes are predicted by deep network, and then optimize relative motion by searching for a better feature alignment in epipolar line, and finally update every pixel depth of the reference frame by depth filter. We evaluate the proposed method on the open dataset comparison against the state of the art in depth estimation to evaluate our method.

关键词： Depth Estimation Pose Estimation Depth Filter

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Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Electroencephalogram Emotion Recognition Based on A Stacking...

引用

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...

引用

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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control strategy based on differential evolution algorithm for planar second-order nonholonomic manipulator

Control strategy based on differential evolution algorithm f...

引用

第37届中国控制会议

作者： Yawu Wang Xuzhi Lai Pan Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a two-stage position control strategy based on the differential evolution(DE) algorithm for a planar second-order nonholonomic manipulator,which has one passive joint and this passive joint is not the first joint(planar APA manipulator for short,where m≥1,n≥0).An offline DE algorithm is used to calculate all link target angles corresponding to the target *** to these target angles,a Lyapunov function is constructed to design the controllers A for the control stage 1,which are used to control all active links to their target *** to the constraint equation of the planar APA manipulator,an oscillatory trajectory is planned for the first active link based on the online DE *** the first active link tracks the oscillatory trajectory,it will back to its target angle ***,the passive link will be jointly controlled to its target angle ***,the other Lyapunov function is constructed to design the controllers B for the control stage 2,which are used to control the first active link tracks the target trajectory and control the remaining m+n-1 active links maintain in their target *** results of a planar AAPA manipulator demonstrate the effectiveness of the proposed control strategy.

关键词： Planar second-order nonholonomic manipulator Differential evolution algorithm Lyapunov function Online intelligent trajectory planning

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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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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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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

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第37届中国控制会议

作者： Xiangyang Dai Wei Xue School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote *** image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Convolutional Neural Network Hyperspectral Image Deep Learning Classification

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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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Synchronous Detector for GMI Magnetic Sensor Based on Lock-in Amplifier

Synchronous Detector for GMI Magnetic Sensor Based on Lock-i...

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第37届中国控制会议

作者： Jinchao Wang Fang Jin Lei Zhu Zhi Zhao Hengchang Rao Junlei Song Kaifeng Dong Wenqin Mo School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

A new concept of a synchronous detector for Giant Magneto-Impedance（GMI） sensors is presented. This concept combines a lock-in amplifier, with outstanding capabilities, high speed and a feedback approach that ensures the amplitude detection with easily adjustable gain. The synchronous detector is capable of measuring high-frequency and very low amplitude signals without the use of diode-based active rectifiers or analog switches. In comparison with most of the commercially available diode-based peak detectors, the linearity of the synchronous detector is generally better, especially for low level amplitudes. The synchronous detector has been used for the amplitude measurement of single frequency sine signal and for the demodulation of amplitude-modulated signal. It has also been successfully integrated in a GMI sensor prototype. Magnetic field measurements in open-and closed-loop of this sensor have been conducted. The measured sensitivity was about 1.02 V/Oe in open-loop while it was 0.12 V/Oe in close-loop. The above research provides technical accumulation for the design of sensor nodes based on GMI sensor wireless sensor networks.

关键词： magnetic sensor Giant Magneto-impedance synchronous detector amplitude demodulation

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Inverse Kinematic control Method of Redundant Manipulators with Joint Limits Constraints

Inverse Kinematic Control Method of Redundant Manipulators w...

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第37届中国控制会议

作者： Ting Fei Xin Chen Jiafeng Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For the control problem of redundant manipulators with joint limits constraints, a novel method combining the simplified clamping weighted least-norm method and the typical gradient projection method is proposed in this paper. The method solves the problem that the manipulability of the redundant manipulator is poor caused by the clamping weighted least-norm method and makes the joint velocity change more smoothly. The proposed novel method is implemented on a kinematic redundant manipulator with four degree-of-freedom(DOF) which belongs to the dulcimer music-playing robot. The numerical simulation results show that the joint positions are well-bounded within the joint limits and the manipulator has a good performance for tracking a given trajectory.

关键词： Redundant manipulators Joint limits Inverse kinematic control

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