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

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Design and Characterization of An Ultralow-Potential Drift Ag/AgCl Electric Field Sensor

Design and Characterization of An Ultralow-Potential Drift A...

引用

Chinese control Conference

作者： Wang Luo Haobin Dong Jian Ge Huan Liu Bingjie Bar Cheng Zhang Zhiwen Yuan Jun Zhu Haiyang Zhang Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex Systems Wuhan Hubei China Science and Technology on Near-Surface Detection Laboratory Wuxi Jiangsu China

This paper presents an all-solid porous Ag/AgCl electric field sensor with ultralow-potential drift for detecting the seafloor electric field signals. The superiority of porous electrode compared with flat electrode on the polarization stability is expounded, and the technological process using solid state agglomeration in developing the all-solid porous Ag/AgCl electrode core are described. Moreover, a new sensor shell is proposed. Numerous parameters, e.g., polarization resistance, self-potential, drift, etc., of the proposed electrode are tested. The experimental results show that the self-potential is less than ±0.1 mV, the source resistance is less than 0.01H, and the drift potential is less than ±5μV/24h, which indicate the superior performance of the proposed Ag/AgCl electric field sensor for marine electric field exploration.

关键词： Electrodes Electric potential Resistance Electric fields Solids Thermal stability Sea measurements

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Transfer learning from generic model to specific model for street object detection 5

Transfer learning from generic model to specific model for s...

引用

5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Liu, Wei Chen, Shu Liu, Zhen-Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Japan

High accurate rate of street objects detection is significant to realize intelligent vehicles. Algorithms based on Convolution Neural Network (CNN) have already shown their reasonable performance on general object detection. for example SSD and YOLO can detection wide variety of objects on 2D images in real time, but the performance is not good enough on street objects detection especially on complex urban street environment. In this paper, instead of proposing and training a new CNN model, we use transfer learning methods to learn from generic CNN model to our specific model to achieve good performance. The transfer learning methods include fine-tuning the pretrained CNN model with self-made dataset and adjusting CNN model structure. We analyze transfer learning results on fine-tuning Single shot multibox detector (SSD) with self-made datasets. The experimental results based on transfer learning method show that the proposed method is effective.

关键词： Object detection

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EEG emotion recognition based on LDA and PSO-SVM 5

EEG emotion recognition based on LDA and PSO-SVM

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5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Liu, Zhen-Tao Xie, Qiao Cao, Wei-Hua Li, Dan-Yun Zhang, Ri Ding, Xue-Wen School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Japan

Compared with speech, facial expression, and body languages, Electroencephalogram (EEG) can reflect the inner activity of brain, by which the emotion can be recognized objectively and naturally. In this paper, an EEG emotion recognition system is proposed in which EEG signals of 6 channels are detected from Frontal Lobe and Temporal Lobe, and then the time-domain features of statistics features and frequency-domain features of spectrum centroid (SC) are extracted. To remove the redundant feature, Linear Discriminant Analysis (LDA) is used to reduce the dimension of feature. In addition, an improved classifier based on PSO-SVM is applied to classify the emotional states in the Valance-Arousal emotion model, respectively, which are defined as High-Valance (HV) and Low-Valance (LV) on the Valance dimension and High-Arousal (HA) and Low-Arousal (LA) on the Arousal dimension. EEG emotion recognition experiment on DEAP dataset is performed, from which the results show that the proposed method obtains the accuracies of 73.33% on Valance dimension and 72.78% on Arousal dimension, which are higher than those of some state-of-the art works.

关键词： Emotion Recognition

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Speaker-independent speech emotion recognition based on genetic algorithm feature selection approach 5

Speaker-independent speech emotion recognition based on gene...

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5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Mao, Jun-Wei He, Yong Liu, Zhen-Tao Cao, Wei-Hua Xu, Jian-Ping School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems China

for speech emotion recognition (SER), emotional feature set with high dimension may produce redundant features and influence the recognition rate. To solve this problem, feature selection of speaker-independent speech based on genetic algorithm (GA) is proposed, which can obtain optimal feature subset. And a four-level emotional classification method based on support vector machine (SVM) is proposed according to the confusion degree among different emotional categories. A framework of speaker-independent SER is presented and the classification experiments based on proposed methods by using Chinese speech database from institute of automation of Chinese academy of sciences (CASIA) are performed, where the speaker-independent features selected by the proposed feature selection method and Spearman correlation analysis are used for emotion recognition, respectively. The experimental results show that the proposal achieved 77.6% recognition rate on average, which is about 1.2% higher than other recognition methods. By proposal, it would be efficient to distinguish the emotional states of different speakers from speech.

关键词： Genetic algorithms

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Design and Characterization of An Ultralow-Potential Drift Ag/AgCl Electric Field Sensor

Design and Characterization of An Ultralow-Potential Drift A...

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第37届中国控制会议

作者： Wang Luo Haobin Dong Jian Ge Huan Liu Bingjie Bai Cheng Zhang Zhiwen Yuan Jun Zhu Haiyang Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex Systems Institute of Geophysics & Geomatics China University of Geosciences

This paper presents an all-solid porous Ag/Ag Cl electric field sensor with ultralow-potential drift for detecting the seafloor electric field signals. The superiority of porous electrode compared with flat electrode on the polarization stability is expounded, and the technological process using solid state agglomeration in developing the all-solid porous Ag/Ag Cl electrode core are described. Moreover, a new sensor shell is proposed. Numerous parameters, e.g., polarization resistance, self-potential, drift, etc., of the proposed electrode are tested. The experimental results show that the self-potential is less than ± 0.1 m V, the source resistance is less than 0.01Ω, and the drift potential is less than ±5μV/24 h, which indicate the superior performance of the proposed Ag/Ag Cl electric field sensor for marine electric field exploration.

关键词： Ag/AgCl electrode pair ultralow-potential drift electric filed sensor stability

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Current controller Based on Recurrent Neural Network for Grid-Connected Converter

Current Controller Based on Recurrent Neural Network for Gri...

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Chinese control Conference (CCC)

作者： Xuefen Wang Hongbin Wang Qingyi Wang Min Ding Quan Yin Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China State Grid Corporation of China Wuhan China School of Automation Huazhong University of science and technology Wuhan China

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q vector control mechanism. To mitigate these limitations, a RNN vector controller trained with Levenberg-Marquardt and FATT(forward accumulation through time) algorithm is designed. The simulation is researched by using MATLAB software, and the results show that training neural-network algorithm is effective and the system using RNN vector control method outperforms the system using conventional PI control method under low sampling rate conditions.

关键词： Mathematical model Training Artificial neural networks Recurrent neural networks Voltage control Convergence

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Analysis and Maximum Efficiency Design for Wireless Power Transfer systems

Analysis and Maximum Efficiency Design for Wireless Power Tr...

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Chinese control Conference

作者： Bing Cao Hongbin Wang Qingyi Wang Min Ding Quan Yin Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China State Grid Corporation of China Wuhan China School of Automation Huazhong University of science and technology Wuhan China

How to stabilize the output voltage while ensuring the maximum charging efficiency of the system is a problem faced by the development of wireless power transfer(WPT). Especially in the dynamic environment, the change of system coupling coefficient or load resistance will have a huge impact on the system. In this paper, a two-stage DC-DC converter control mode is used to control the system with the goal of dynamic charging demand. Among them, the system achieves impedance matching through the receiver DC-DC converter to ensure the transmission efficiency of the system, and in order to match the impedance, the voltage and current information in the coil is used to calculate the coupling coefficient, then, the coupling coefficient can be used to achieve maximum efficiency. The system output voltage is stabilized by using the transmitter DC-DC converter. The simulation experiment shows that the control mode can stabilize the output voltage well, and is more efficient than the single receiver closed loop control(SRCLP) system.

关键词： Resistance Couplings DC-DC power converters control systems Voltage control Receivers Frequency control

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Analysis of Influencing Factors on Carbon Monoxide Utilization Rate of Blast Furnace Based on Multi-Timescale Characteristics 36

Analysis of Influencing Factors on Carbon Monoxide Utilizati...

引用

第36届中国控制会议

作者： Jianqi An Yuwan Yang Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

In the blast furnace,due to the different changing frequency of different operations,and the different reaction time of gas,liquid and solid materials,there exists multi-timescale characteristics in the iron-making ***,not enough attention has been paid to this characteristic,most of the analyses on the blast furnace state before are under a fixed time ***,this paper makes a analysis of influencing factors on carbon monoxide utilization rate of blast furnace based on multi-timescale ***,the factors that affect the carbon monoxide utilization rate are analyzed from different time ***,in the short time scale,the individual influencing time scale of the permeability index,total pressure difference and top temperature is found by the support vector machine（SVM） *** in the long time scale,the influencing time scale of burdening is *** the validity of each model is verified by the field data of the blast furnace.

关键词： Multi-time scales Carbon monoxide utilization Support vector machine Blast furnace

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Consensus of discrete-time second-order multi-agent systems with delayed and noisy measurements under balanced digraphs 29

Consensus of discrete-time second-order multi-agent systems ...

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第29届中国控制与决策会议

作者： Xiaofeng Zong Tao Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Mathematics East China Normal University Shanghai Key Laboratory of Power Station Automation Technology

ISBN: (纸本)9781509046584

This paper studies mean square and almost sure consensus of discrete-time second-order multi-agent systems with time-delays and multiplicative noises in the information exchange with *** using the stochastic stability theorem of discrete-time stochastic delay systems,we find sufficient conditions for mean square and almost sure consensus explicitly related to the network and control *** is shown that if the network graph is balanced and strongly connected,then the weighted-average type control protocol can be properly designed to ensure mean square and almost sure consensus for any given time-delays and noise intensity coefficients.

关键词： Second-order multi-agent system Multiplicative noise Time-delay Consensus

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Robust Iterative Learning control for Alloy Batch Weighing Process in Refining Furnace 36

Robust Iterative Learning Control for Alloy Batch Weighing P...

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第36届中国控制会议

作者： Yibo Huang Jianqi An Yong He Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional（2D） weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality（LMI） obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.

关键词： Iterative learning control 2D weighing model Batch process Alloy weighing process Robust control

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