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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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Chinese control Conference

作者： Xiangyang Dai Wei Xue Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

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 sensing. Hyperspectral 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： Hyperspectral imaging Image classification Convolution Feature extraction Support vector machines

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An optimization method for coupling matrix elements of microwave filter

An optimization method for coupling matrix elements of micro...

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Chinese control Conference

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： Couplings Tuning Transmission line matrix methods Integrated circuit modeling Optimization methods Cats

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Bifurcation Analysis and control of Fractional-Order Quorum Sensing Network 37

Bifurcation Analysis and Control of Fractional-Order Quorum ...

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37th Chinese control Conference, CCC 2018

作者： Feng, Liu Xiangjuan, Dai Qicheng, Mei Guang, Ling Xinmei, Wang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789881563941

In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by sRNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Stability criteria

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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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Chinese control Conference

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

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： Neural networks DC motors Feeds Suspensions Lifting equipment Torque Angular velocity

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Real-time optical music recognition for dulcimer robot 8

Real-time optical music recognition for dulcimer robot

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Xiao, Zhe Chen, Xin Zhou, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Arts and Communication China University of Geosciences Wuhan430074 China

Optical music recognition (OMR) is an important technology to recognize paper music sheet automatically, which has been applied to preserve music scores. In this paper, we propose a real-time OMR system to recognize scores, and the recognized information is converted into a standard electronic music file to dulcimer robot, achieving real-time performance. During the recognition steps, we treat note group and isolated notes separately. The note group is decomposed into three fundamental elements: note stem, note head and note beams. And the isolated music symbol is recognized based on shape model descriptor. We conduct tests on five pages of music sheets, which are real images taken live by the camera. The tests show that the proposed method constitutes an interesting contribution to OMR. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Computer music

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EEG emotion recognition based on ensemble learning 8

EEG emotion recognition based on ensemble learning

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作者： Ding, Xue-Wen Liu, Zhen-Tao Feng, Zhi-Yong Xie, Qiao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Automotive Engineering Wuhan University of Technology Wuhan430070 China

EEG emotion recognition based on ensemble learning is proposed, in which three classification models including k-Nearest Neighbor, Decision Tree and Logistic Regression are integrated. In feature extraction, four kinds of features including Mean, Skewness, Sample Entropy and Lempel-Ziv complexity (LZC) are extracted from eight channels’ EEG signals, and Kernel Principal Component Analysis (KPCA) is employed to reduce the dimension of the feature set. The proposal is tested on the DEAP dataset in which the EEG emotional states are accessed in Arousal-Valence emotion space. The result shows that the average emotion recognition accuracies of 77.73% in Arousal dimension and 75.34% in Valence dimension are obtained, which demonstrates its feasibility. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Emotion Recognition

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Chaotic Oscillator Based on a Modified Voltage-controlled HP Memristor Model

Chaotic Oscillator Based on a Modified Voltage-Controlled HP...

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IEEE advanced Information Management,Communicates,Electronic and automation control Conference (IMCEC)

作者： Zhen Wang Chao Sun Fang Jin Wenqin Mo Junlei Song Kaifeng Dong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Different from previous studies on memristive chaotic oscillators which tended to adopt common mathematical models but lacked practical consideration, in this paper, a novel memristive chaotic oscillator based on a modified voltage-controlled HP memristor model was proposed for the first time. By replacing Chua's diode with this model in a canonical Chua's circuit, we derived an oscillator characterized by rich dynamics such as special-shaped attractors, a wide range of chaos and insensitivity to the initial value of the memristor. These features were systematically investigated in terms of bifurcation diagrams, Poincaré map, time series, Lyapunov exponents, etc.

关键词： automation

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Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Electroencephalogram Emotion Recognition Based on A Stacking...

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Chinese control Conference (CCC)

作者： Oiao Xie Zhen-Tao Liu Xue-Wen Ding Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

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 features. In 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 set. The 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, respectively. The 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： Electroencephalography Emotion recognition Brain modeling Feature extraction Stacking Entropy Predictive models

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An Improved Version of DMOEA-εC for Many-objective Optimization Problems: IDMOEA-εC

An Improved Version of DMOEA-εC for Many-objective Optimiza...

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第三十八届中国控制会议

作者： Juan Li Bin Xin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

The decomposition strategy and the ε-constraint method are two important strategies in the field of multi-objective optimization. DMOEA-εC first attempts to incorporate the ε-constraint method into the decomposition strategy and solve a multi-objective optimization problem(MOP) via optimizing a series of scalar constrained subproblems collaboratively with the help of information from neighboring subproblems. However, given the inefficiency of applying DMOEA-εC to deal with many-objective optimization problems(MaOPs), a two-stage upper bound vectors generation procedure is proposed to generate widely spread upper bound vectors in a high-dimensional space. Besides, a boundary points maintenance mechanism is put forward to remedy the diversity loss of a population in DMOEA-εC. Based on the above, DMOEA-εC with the two-stage upper bound vectors generation procedure and the boundary points maintenance mechanism, named as IDMOEA-εC, is presented for MaOPs. IDMOEA-εC is compared with four state-of-the-art many-objective evolutionary algorithms, including HypE, NSGA-Ⅲ, MOEADD, and Two Arch2. Experimental studies demonstrate that IDMOEA-εC outperforms or performs competitively against these algorithms on the majority of sixteen DTLZ test instances with up to 10 objectives.

关键词： Many-objective optimization decomposition ε-constraint method two-stage upper bound vectors generation procedure boundary points maintenance mechanism

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Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

Speech Emotion Recognition Based on Linear Discriminant Anal...

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Chinese control Conference (CCC)

作者： Jun-Wei Mao Yong He Zhen-Tao Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

For speech emotion recognition, emotional feature set with high dimension may produce redundant features and influence the recognition accuracy. To solve this problem and obtain the optimal emotional feature subset of speech, a feature dimension reduction based on linear discriminant analysis is proposed. According to the confusion degree between different basic emotions, an emotion recognition method based on support vector machine decision tree is proposed. Experiment on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented, where standard feature sets of the INTER-SPEECH and classic classifiers are used in comparative experiments respectively. Experimental results show that the proposal achieves 84.39% recognition accuracy on average. By proposal, it would be fast and efficient to discriminate emotional states of diverse speakers from speech, and it would make it possible to realize the interaction between speaker and computer/robot in the future.

关键词： Feature extraction Support vector machines Speech recognition Emotion recognition Decision trees Dimensionality reduction Linear discriminant analysis

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