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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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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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Predefined-Time Synchronization of Inertial Memristive Neural Networks

Predefined-Time Synchronization of Inertial Memristive Neura...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

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

ISBN: (纸本)9781665412872

This article investigates the predefined-time synchronization (PTS) of inertial memristive neural networks (IMNNs). First, we reduce the order of the system and design an effective controller for the error system. Furthermore, several new criteria for PTS of IMNNs are obtained and a user-defined settling time is provided. Lastly, the effectiveness of the results is proved by simulation experiment. © 2021 IEEE.

关键词： Synchronization

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Signal discrimination with wavelet algorithm and Pearson correlation-based algorithm in f-OTDR systems 2

Signal discrimination with wavelet algorithm and Pearson cor...

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2020 2nd International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2020

作者： Mo, Dai Li Yu, Zhi Hua Cai, Yun Fei Liu, Rui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of f-OTDR systems. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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Radar Vital Signs Detection Method Based on Variational Mode Decomposition and Wavelet Transform

Radar Vital Signs Detection Method Based on Variational Mode...

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2021 China automation Congress, CAC 2021

作者： Ma, Shaopeng Xue, Wei Chen, Kehui Wang, Zexi 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

To deal with noise interference in frequency modulated continuous wave (FMCW) radar vital signs and the interference of breathing harmonics on the heartbeat signal, a vital signs detection method based on variational mode decomposition (VMD) and wavelet transform is proposed. First, VMD is applied to decompose the vital signs into a series of intrinsic mode function (IMF) components. Then, the IMF components with spectral peaks in the frequency range of breathing and heartbeat are selected to reconstruct the breathing and heartbeat signals respectively. Finally, the wavelet transform threshold method is used to remove the noise in the breathing and heartbeat signals. The experimental results show the proposed method can overcome the influence of breathing harmonics, accurately extract the breathing and heartbeat signals, and effectively improve the signal-to-noise ratio and detection accuracy of heart rate. © 2021 IEEE

关键词： Variational mode decomposition

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Uniform Stability of Fractional-order Memristive Neural Networks with Hybrid Time-Varying Delays

Uniform Stability of Fractional-order Memristive Neural Netw...

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

Stabilization of discrete time stochastic linear systems wit...

引用

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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Short-term power load forecasting during the COVID-19 pandemic using XGBoost and Copula theory

Short-term power load forecasting during the COVID-19 pandem...

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2021 China automation Congress, CAC 2021

作者： Liu, Bo-Wen Xu, Da 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

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19. © 2021 IEEE

关键词： COVID-19

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Stability and Bifurcation control of Fractional BAM Neural Networks with Diffusion and Time Delays

Stability and Bifurcation control of Fractional BAM Neural N...

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第42届中国控制会议

作者： Feng Liu Wei Wang Gang Cai Feifan Zhou Hua O Wang Xiuqin Yang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Mechanical Engineering Boston University

Based on fractional calculus theory and reaction-diffusion equation theory,a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is *** constructing the phase space basis based on the Laplace operator eigenvector,the system equation is linearized to obtain the characteristic ***,the characteristic equation is analyzed,and the local stability of the system at the equilibrium point is *** taking the time delay as the bifurcation parameter,the stability changes of the system at the equilibrium point and the generation conditions of the Hopf bifurcation are studied when the time delay ***,a state feedback controller is designed to control the bifurcation of the ***,the theoretical derivation is verified by numerical simulation.

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Dynamic analysis and control of fractional order memristor Hopfield neural network

Dynamic analysis and control of fractional order memristor H...

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第42届中国控制会议

作者： Feng Liu Gang Cai Feifan Zhou Wei Wang Hua Wang Xiuqin Yang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Mechanical Engineering Boston University

In this paper, a novel smooth magnetron is introduced to construct a fractional memristor Hopfield neural network(fractional order M-HNN). The local stability of equilibrium point are analyzed theoretically. Taking the memristor coupling strength coefficient and the fractional order as bifurcation parameters, the phase trajectory diagram, the bifurcation diagram of the system are drawn to analyze the influence on the dynamic behavior of the neural network. When the system parameters are fixed, the hyperchaos phenomenon of the fractional order M-HNN model is revealed. Finally, the PD controller is applied to the model to enhance the stability of the system.

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