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

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A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem 18

A hybrid differential evolution and estimation of distributi...

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Hao, Rong Zhang, Jia Xin, Bin Chen, Chen Dou, Lihua School of Automation Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781450357647

The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively. © 2018 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Fog Removal Method of Slope Monitoring Image Based On Vision Detection

Fog Removal Method of Slope Monitoring Image Based On Vision...

引用

Chinese control Conference

作者： Xianzhen Sang Yue Yang Xianhe Hou Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

In the slope monitoring based on image detection, the main work is to process the acquired slope image. The landslide occurred mostly in the rain, fog and other complex weather conditions. If we can process effectively and fast fog images according to the fog horizon slope vision images. It would be helpful for subsequent image segmentation, object extraction, positioning, and improving the accuracy and efficiency of detection of slope deformation. Based on the visual technology in slope monitoring, we compared two kinds of defogging algorithm of slope images. One is a kind of image enhancement method of non-physical model, mainly including: equalization algorithm and homomorphic filtering algorithm, McCann Retinex algorithm and multi-scale Retinex algorithm and a global histogram. The other is the image restoration method based on physical model, including the dark channel prior bilateral filtering algorithm, and combined with the theory of dark channel prior to fog algorithm. The experimental results show that the histogram equalization method has the advantage of fast imaging quality in slope visual image processing, and is more suitable for slope monitoring.

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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

引用

Chinese control Conference

作者： Xiangyang Dai Wei Xue Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Hyperspectral imaging Image classification Convolution Feature extraction Support vector machines

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A flexible method for time-of-flight camera calibration using random forest 1

引用

1st International Conference on Smart Multimedia, ICSM 2018

作者： Xu, Chi Li, Cheng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Bioinformatics Institute A*STAR Singapore138671 Singapore Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9783030043759

ISBN: (纸本)9783030043742

A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation. © 2018, Springer Nature Switzerland AG.

关键词： Calibration

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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

第37届中国控制会议

作者： Jun-Wei Mao Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For speech emotion recognition,emotional feature set with high dimension may produce redundant features and influence the recognition *** solve this problem and obtain the optimal emotional feature subset of speech,a feature dimension reduction based on linear discriminant analysis is *** to the confusion degree between different basic emotions,an emotion recognition method based on support vector machine decision tree is *** 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 INTERSPEECH and classic classifiers are used in comparative experiments *** results show that the proposal achieves 84.39%recognition accuracy on *** 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： Emotion Recognition Speech Linear Discriminant Analysis Support Vector Machine Decision Tree

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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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第37届中国控制会议

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

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： Rate of Penetration Mud Pit Volume Drilling Process SVR Method

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Multi-Objective Drilling Trajectory Optimization with A Modified complexity Index

Multi-Objective Drilling Trajectory Optimization with A Modi...

引用

第37届中国控制会议

作者： Wendi Huang Min Wu Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Drilling trajectory optimization is an important part before drilling process. Since decreasing the cost and increasing the safety of drilling process are contrary to each other, drilling trajectory optimization problems should be modeled as multiobjective optimization problems. For this purpose, proposing appropriate optimization index which meet the requirement of drilling process is necessary. Many researches applied drill-string torque as the safety index. However, the actual drilling trajectory may deviate from the design trajectory. Ignoring this fact may cause the torque prediction too optimistic. In this research, the drill-string torque is combined with tortuosity of drilling trajectory to reduce the optimism of the prediction of drillstring torque. A 3D drilling trajectory optimization problem is formulated as a multi-objective optimization problem, and the objective functions are drilling trajectory length and the modified drill-string torque. Non-dominated sorting genetic algorithm II is applied to solve the multi-objective optimization problem, and optimal pareto set are obtained.

关键词： Drilling trajectory multi-objective optimization drill-string torque

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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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第37届中国控制会议

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

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： Parabolic approximation method Maximum power point tracking MPPT Simulation of MATLAB

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Research and Implementation of Target Tracking Algorithm Based on Convolution Neural Network

Research and Implementation of Target Tracking Algorithm Bas...

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第37届中国控制会议

作者： LiJun Zhang Chen Wang Xing Jin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence of in-depth learning methods provide a new opportunity for the study of video target tracking. This paper first analyzes the research problems of video target tracking at present, analyzes the characteristics and trends of video target tracking in the new period, introduces the emerging recursive neural network frame structure, combined with Kalman filter And the experimental results show that the accuracy and robustness of the target tracking based on the convolution neural network algorithm are all good.

关键词： Regional convolutional neural network. Target tracking Kalman filtering

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Hyperparallelepiped Based Loop partitioning of Adaptive Dynamic Programming Structure

Hyperparallelepiped Based Loop partitioning of Adaptive Dyna...

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第37届中国控制会议

作者： Lu Li Xin Chen Hao Fu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper focuses on an accelerating method for partitioning the loops in the structure of the adaptive dynamic programming（ADP）. ADP contains critic-actor structure which involves the iterations of the value function. When the system needs to be stable, the value function generally needs to iterate thousands of times, the high computation burden which hinders the iterations will be generated. In order to reduce the computation burden, we introduce a hyperparallelepiped based loop partitioning（H-LP） method which splits the iterations of the value function and reduces the communication traffic calculated by the data footprint. The experiment results show that the computation performance will be enhanced when the H-LP method is introduced. The proposed method has an important practical significance.

关键词： Hyperparallelepiped Loop Partitioning Adaptive Dynamic Programming

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