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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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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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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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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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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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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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Standby and Shutdown Cycles Modeling of SOFC Lifetime Prediction

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Energy Procedia 2019年 158卷 1573-1578页

作者： Wu Xiao-long Xu Yuanwu Xue Tao Zhao Dongqi Jiang Jianhua Deng Zhonghua Fu Xiaowei Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057 China

In order to more accurately predict the impact of solid oxide fuel cells to stack life on standby and shutdown, this paper proposes a modeling method based on experimental data. Which is based on Elman neural network(NN). At the same time, a main factor is considered in the modeling process which is the effect of cooling rate on the stack. In the process of modeling, the most obvious cooling rate is used to modeling. There are two different influencing factors on classify, the training set and the verification set respectively. After the reliability of the model, the Solid Oxide Fuel cell (SOFC) stack life prediction is carried out. From the predict results and the experimental results, it is found that the prediction results are good and the high precision.

关键词： Solid Oxide Fuel Cell(SOFC) Stack useful lifetime predict Cooling speed Gas composition Cycle standby shutdown

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A Demand Analysis Method Based on TAKAGI-SUGENO Fuzzy Model for Drinking Service

A Demand Analysis Method Based on TAKAGI-SUGENO Fuzzy Model ...

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Chinese control Conference

作者： Man Hao Weihua Cao Zhentao Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

A demand analysis method based on TAKAGI-SUGENO (T-S) fuzzy model for drinking service is proposed to provide corresponding services according to users' emotions and intentions in human-robot interaction, in which T-S fuzzy model is used to establish the relationship among human intention and human demands. First, the transformation of input and output is discussed. Secondly, fuzzy rules are formulated, and then fuzzy inference is applied to get user's demand corresponding to emotion and intention. The proposal considers peoples fuzziness in inferring humans intention, which could help the robots to provide satisfied drinking service to users. To validate the proposal, drinking service experiments are performed in a laboratory scenario using a humans-robots interaction system, from which the experimental results demonstrate the feasibility of the proposal.

关键词： Analytical models Human-robot interaction Takagi-Sugeno model Proposals Service robots

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Dense Depth Estimation with Absolute Scale

Dense Depth Estimation with Absolute Scale

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Chinese control Conference (CCC)

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

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： Cameras Feature extraction Simultaneous localization and mapping Image reconstruction Pose estimation Neural networks

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Modeling and simulation of temperature distribution for planar cross-flow solid oxide fuel cell

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Energy Procedia 2019年 158卷 1585-1590页

作者： Yuan-wu Xu Xiao-long Wu Hang You Tao Xue Dong-qi Zhao Jian-hua Jiang Zhong-hua Deng Xiao-wei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Research Institute of Huazhong University of Science & Technology in Shenzhen Shenzhen 518057 China

Solid oxide fuel cell is a device that can convert chemical energy directly into electricity. Its advantages, such as high efficiency, low emission, quiet operation, fuel flexibility, bring about the broad application prospect. However, it is difficult to obtain the temperature distribution in the existing planar cross flow solid oxide fuel cell through the experiment. In this paper, a control orient two-dimensional differential equation model is established for a planar cross flow solid oxide fuel cell based on the finite node method, and an iterative algorithm for calculating the real time voltage of the fuel cell is proposed for the model. Based on the model, the temperature distribution of the fuel cell in the test and system configuration is simulated. The simulation results show that the model can reflect the thermoelectric characteristics of the planar cross flow solid oxide fuel cell, especially the temperature distribution of the fuel cell. The SOFC temperature distribution modeling in this paper is helpful for the development of temperature distribution observers and design related control methods in later studies.

关键词： modeling simulation temperature distribution planar cross flow solid oxide fuel cell

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