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检索条件"机构=Key Laboratory of Intelligent Control and Design of Complex Systems"

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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...

引用

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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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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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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Prediction of ROP and MPV Based on Support Vector Regression Method

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

引用

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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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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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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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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R-H Measurement System Based on Anomalous Hall Effect Devices

R-H Measurement System Based on Anomalous Hall Effect Device...

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Chinese control Conference (CCC)

作者： Zhenyu Yuan Kaifeng Dong Laizhe Zhu Junlei Song Fang Iin Wenqin Mo Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

With the application of magnetic thin films becoming more and more widespread, people pay more and more attention to the performance characterization. In order to obtain a magnetic film with a specific performance, it is very important to judge the quality of the magnetic film and measure the magnetic properties of the film. However, with the increase of the film preparation process, the thickness of the prepared film is getting thinner and the magnetic moment signal contained therein is also decreased. This brings a certain degree of difficulty to the traditional measurement methods. For example, the VSM system that obtains the hysteresis loop by measuring the magnetic moment signal has become somewhat inadequate for the measurement of ultra-thin films. In order to solve this issue, a new method based on anomalous Hall effect is introduced in this paper. The test system of this system adopts the four-probe measuring method, a constant current is applied across the surface of the film sample, and the abnormal Hall voltage is measured at the other two ends. The R-H curve of the sample can be obtained through calculation. As compared to VSM measurement, this method is simpler and stable, more accurate, which can greatly reduce the anomalous Hall-effect device R-H characteristic measurement cost.

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Low Resource-Consuming Cyber-physical System Sampling Method using Bernoulli Circulant Matrix

Low Resource-Consuming Cyber-physical System Sampling Method...

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Chinese control Conference

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

Cyber-physical System (CPS) have a high requirement on real-time property, and it is difficult to improve the sampling efficiency base on traditional sampling theory. In this paper, the compression sensing (CS) theory is applied to the sampling compression process of CPS system. The CS theory was used to the sampling compression method of CPS system. The Bernoulli circulant matrix, which is easy to be realized and stored, and its construction algorithm were designed to simplify the realization of CS theory in CPS. It is concluded that for random data set, the compression ratio increases from 14.06% to 42.18% and the reconstruction error decreases from 27.65 to 1.28 with increasing repetition times. Note that the sampling time are around tens of microseconds and the reconstruction time are around several milliseconds, which indicates a high real-time performance for CPS. In addition, for image data set, the compression ratios are about 42.90% which indicates a high compression ratio and huge storage resources saving. More importantly, the sampling time and reconstruction time are only several microseconds and several seconds respectively, which indicates a high real-time performance for CPS.

关键词： Sparse matrices Image reconstruction Cyber-physical systems Real-time systems Genetic algorithms Training Matching pursuit algorithms

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Research of Vehicle Detection System Based on Geomagnetic Disturbance Detection

Research of Vehicle Detection System Based on Geomagnetic Di...

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Chinese control Conference

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

To overcome the shortcomings of high cost, maintenance difficulties in traditional vehicle detect ion methods. This paper presents a novel vehicle detection method used in the military field. The anisotropic magneto resistive (AMR) sensor is used to detect geomagnetic disturbances in real time, meanwhile, the ultrasonic distance-measuring sensor is adopted to determine the position of the target vehicle and avoid miscalculation. To reduce detection delay, a scheme containing dual AMR sensors and wireless communication module for information transferring is used on this detection system. In order to verify the effectiveness of the detection system, a mathematical model of vehicle detection based on geomagnetic disturbance detection is established. According to the test results, the influence cased by different content of ferromagnetic material and detection distance is analyzed. Finally, the related experiments on the effects of different ferromagnetic contents on the geomagnetic disturbance at different detection distances are carried out. The experimental results verify the correctness of the mathematical model proposed in this paper, and further verify the effectiveness of the detection system. This detection system possesses many prominent advantages such as low-cost, long-life, good performance of real time, convenience.

关键词： Magnetic fields Acoustics Vehicle detection Coatings Real-time systems Bridge circuits

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