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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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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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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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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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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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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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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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A novel augmented Lyapunov functional for the stability analysis of delayed neural networks

A novel augmented Lyapunov functional for the stability anal...

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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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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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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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