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检索条件"机构=Key Laboratory of Advanced Transducer and Intelligent Control Systems"

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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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Identification of Essential Proteins Using Improved Node and Edge Clustering Coefficient

Identification of Essential Proteins Using Improved Node and...

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第37届中国控制会议

作者： Zhu Yuan Wu Chong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Computational methods are often applied to identify essential proteins from protein-protein interaction networks. In this paper, inspected by node and edge clustering coefficient（NEC） and Pe C, we propose an improved version of node and edge clustering coefficient（INEC） which both considers dual topological characteristics of the network and high false positives of the protein-protein interaction data. We apply it for the identification of essential proteins. And we implement three versions of INEC which combine different biological information. In order not to be confused, we call the first one INEC0 which dosen’t integrate biological information, the second one INEC1 which integrates gene expression similarity, and the third one INEC2 which integrates gene expression similarity, functional similarity, and protein-protein sequence similarity. We apply three implemented INEC methods to protein-protein interaction data of Saccharomyces cerevisiae（Yeast） and compare them with some state-of-theart methods（DC, NC, Pe C, and NEC）. The experimental results show that our proposed methods achieve better results in terms of prediction accuracy, area under the curve of PR-curve, and Jackknife methodology.

关键词： Protein-Protein Interaction Essential Protein Protein-Protein Interaction Network Biological Information

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The transient electromagnetic inversion based on the simplex-simulated annealing algorithm

The transient electromagnetic inversion based on the simplex...

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第37届中国控制会议

作者： Guangjun Wang Caifeng Xu Gang Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In order to avoid the linear inversion method falling into local minima and slow convergence speed of the global optimization inversion method, the article proposed the simplex-simulated annealing algorithm for transient electromagnetic inversion research which combines advantages of the simplex method and the simulated annealing algorithm. The simplex method is used to obtain local minimum value which is relatively close to the actual value, then the simulated annealing algorithm is used to obtain the global optimal solution which can better reflect structural characteristics of the real stratigraphic *** the comparison of the noise inversion results and noise free inversion results about K-type, H-type, KH-type and HKH-type stratigraphic models, it can be proved that the simplex-simulated annealing algorithm can suppress some noise. The comparison of the simulated annealing method and the simplex-simulated annealing algorithm shows that the simplex-simulated annealing algorithm has the characteristics of global searching ability and fast convergence speed.

关键词： Simplex method Simulated annealing The central loop Transient electromagnetic inversion

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Fast Automatic Localization of Epileptic Seizure Onset Zones Using Complex Morlet Wavelet Transform-based Singular Value Decomposition

Fast Automatic Localization of Epileptic Seizure Onset Zones...

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第37届中国控制会议

作者： Zelin Fang Xiongbo Wan Ting Wan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

A new method for localization of epileptic seizure onset zones（SOZs） is proposed, which uses the Shannon-entropybased complex Morlet wavelet transform to extract a satisfactory time-frequency feature of high-frequency oscillations（HFOs）.The singular value decomposition and the K-medoids clustering algorithm are employed to extract effective features from the redundant matrix of wavelet coefficients. A distinctive feature is to use the singular values to detect HFOs with the consideration that the singular values of HFOs are generally significantly higher than those of normal case. Based on the half-maximum method,the localization of SOZs are achieved by using the characteristics of HFOs. Comparisons show that our method provides a higher sensitivity and specificity than two existing methods do.

关键词： Epilepsy Seizure onset zone（SOZ） High-frequency oscillation（HFO） Singular value decomposition K-mediods clustering

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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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Multi-Objective Drilling Trajectory Optimization with A Modified Complexity Index

Multi-Objective Drilling Trajectory Optimization with A Modi...

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第37届中国控制会议

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

Drilling trajectory optimization is an important part before drilling process. Since decreasing the cost and increasing the safety of drilling process are contrary to each other, drilling trajectory optimization problems should be modeled as multiobjective optimization problems. For this purpose, proposing appropriate optimization index which meet the requirement of drilling process is necessary. Many researches applied drill-string torque as the safety index. However, the actual drilling trajectory may deviate from the design trajectory. Ignoring this fact may cause the torque prediction too optimistic. In this research, the drill-string torque is combined with tortuosity of drilling trajectory to reduce the optimism of the prediction of drillstring torque. A 3D drilling trajectory optimization problem is formulated as a multi-objective optimization problem, and the objective functions are drilling trajectory length and the modified drill-string torque. Non-dominated sorting genetic algorithm II is applied to solve the multi-objective optimization problem, and optimal pareto set are obtained.

关键词： Drilling trajectory multi-objective optimization drill-string torque

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Carbon Efficiency Performance Assessment Method for Sintering Process Based on Fuzzy Synthetic Evaluation

Carbon Efficiency Performance Assessment Method for Sinterin...

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第37届中国控制会议

作者： Wei Jiao Xin Chen Kailong Zhou School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Based on the hierarchical control system of sintering process, a carbon efficiency performance assessment method is proposed. Firstly, performance evaluation model of sintering process is designed according to the characteristics hierarchical control structure of sintering process. Then, the performance index of the comprehensive coke ratio（CCR） in the carbon efficiency optimization layer is designed to achieve the carbon efficiency performance assessment. Next, the performance indexes of burning through point（BTP） control system of the optimization control layer and the fuzzy synthetic evaluation method based on information entropy are designed. The proposed carbon efficiency performance assessment method is implemented on the simulation platform of the sintering process in the laboratory, which effectively realized the performance assessment for sintering process plant level control system, and then provides effective information for the staff to maintain the control system.

关键词： Hierarchical control system Performance assessment method Fuzzy synthetic evaluation Comprehensive coke ratio Burning through point

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GMI Effect Automatic Test System Based on Network Analyzer

GMI Effect Automatic Test System Based on Network Analyzer

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Chinese control Conference

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

The measuring instrument selects the network analyzer, combines the direct current source and the Helmholtz coil to form the GMI effect test system. PC program written in LabVIEW, network analyzer and DC current source is controlled by the host computer. The host computer can process and plot the impedance data collected by the network analyzer, and save the data and images locally. The test system by measuring the commercial Vitrovac6025 strip material, found that the material all showed a strong GMI effect at different frequencies. By comparing the measured data of the network analyzer and the impedance analyzer measured in the same frequency band, we found that the two are consistent, which verifies the accuracy and reliability of the test system.

关键词： Frequency measurement Impedance measurement Magnetic field measurement Impedance Magnetic fields Current measurement

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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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