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

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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...

引用

Chinese control Conference

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

This paper investigates the stability of neural networks with a time-varying delay. Based 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 candidate. Then an extended reciprocally convex matrix inequality and an auxiliary function-based inequality are utilized to estimate the derivative of the LKF. As a result, an improved stability criterion is established. Finally, the advantage of proposed method is demonstrated by a numerical example.

关键词： Linear matrix inequalities Stability criteria Symmetric matrices Biological neural networks Numerical stability Neurons

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Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Electroencephalogram Emotion Recognition Based on A Stacking...

引用

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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A Prediction Model Based on SVR for Untwist Angle in Drilling Process

A Prediction Model Based on SVR for Untwist Angle in Drillin...

引用

Chinese control Conference

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

As slide steering technology has lower maintenance costs, it is widely used in geological drilling industry. In order to adjust the hole trajectory, this technology changes the drilling direction by controlling tool face angle of downhole power drill tool. However, due to the existence of the untwist angle, it is difficult to precisely control the angle, which will directly affect the quality of hole trajectory. So untwist angle prediction is the prerequisite of hole trajectory control. This paper introduces a common method for calculating untwist angle for generating the training set. And then factors that influence untwist angle will be analyzed. Meanwhile, based on the analysis and calculation results, support vector regression is introduced in the prediction algorithm to provide a new way for untwist angle prediction.

关键词： Training Face Predictive models Tools Trajectory Azimuth Prediction algorithms

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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...

引用

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

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

引用

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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Study on Maximum Power Point Tracking Based on Parabolic Approximation Method for Photovoltaic Power Generation system

Study on Maximum Power Point Tracking Based on Parabolic App...

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Chinese control Conference

作者： Rengui Li Zhili Tan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Maximum power point tracking controller is essential to obtain the maximum power from a solar array in the photovoltaic systems as the PV power module varies with the temperature and solar irradiation. In the DC/DC circuit, the maximum power point tracking algorithm based on parabolic approximation method is used. On the basis of analyzing the principle of various tracking methods, the key technology of parabola approximation can be found to find the exact maximum power point.

关键词： Mathematical model Photovoltaic cells Photovoltaic systems Maximum power point trackers Approximation methods

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Prediction of ROP and MPV Based on Support Vector Regression Method

Prediction of ROP and MPV Based on Support Vector Regression...

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Chinese control Conference (CCC)

作者： Yang Zhou Xin Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

During the drilling process, accurate prediction of drilling efficiency and safety plays a key role in timely adjustment of drilling process state. In general, surface parameters rate of penetration(ROP) and mud pit volume(MPV) are often used as important parameters to judge drilling safety and efficiency due to the bad bottom hole environment and unreliable detection devices. However, most drilling systems are underground, the structure is complex and exists many disturbances, so the state of drilling process is difficult to accurately predict. In this paper, an online support vector regression(OSVR) model is proposed to predict the ROP and MPV. First, the parameters of the model are determined by simple drilling process analysis. Then, the fast fourier transform filtering method is used to filter the high frequency disturbances of the data. Finally, the prediction model is established by support vector regression(SVR) method and the model is continuously updated by the model update method. The simulation results of industrial data show that the proposed model has a good prediction effect.

关键词： Predictive models Safety Data models Vibrations Fast Fourier transforms Rocks Support vector machines

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CamShift Target Tracking Based on the Combination of Inter-frame Difference and Background Difference

CamShift Target Tracking Based on the Combination of Inter-f...

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Chinese control Conference

作者： Lianghua He Lai Ge Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Aiming at the detection of moving objects in video series, a moving object detection algorithm based on background difference method and inter-frame difference method is proposed. A new background update method is proposed to update the unchanged background area into the background frame. Experiments show that this method overcomes the problems of false detection and empty in the previous detection algorithms. The method can meet the need of real-time detection and tracking of moving targets with the advantages of high accuracy and fast calculation speed.

关键词： Target tracking Image color analysis Object detection Real-time systems Mathematical model Image segmentation Histograms

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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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Chinese control Conference (CCC)

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

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 AdaCost2-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： Prediction algorithms Support vector machines Kernel Radio frequency Mathematical model Drilling machines Machine learning algorithms

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Research on Handwritten Alphabet Recognition system Based on Extreme Learning Machine

Research on Handwritten Alphabet Recognition System Based on...

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Chinese control Conference

作者： Junlei Song Qi Liu Shengnan Tian Yi Wei Fang Jin Wenqin Mo Kaifeng Dong Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Aiming at the problems of slow recognition, low efficiency and degree of automation in handwritten letter recognition system at present, a handwritten letter recognition system based on extreme learning machine is designed in this paper. The system is implemented by mixed programming with MATLAB and visual studio, it can reads, normalize, binarize and extract the handwritten letter images. The real-time interactive recognition of handwritten letters can be realized on the basis of training the simple pictures by using the identification model of the extreme learning machine algorithm. The experimental results show that the handwriting recognition system based on extreme learning machine designed in this paper can recognize 98.82% of handwritten letters and greatly reduce learning and testing time. Compared with BP neural network and other recognition algorithms, its training times have been reduced by hundreds or even thousands of times. At the same time, there is no manual intervention in the entire learning and testing process, which improves the automation of handwriting recognition.

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