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第30届中国控制与决策会议

作者： Huan Lou Zongfeng Qi Jianxun Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 Key Laboratory of System Control and Information ProcessingMinistry of Education of China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System

In recent years, Generative Adversarial Network(GAN) is widely applied in many domains;however, there is still some difficulties in training the network, which are mainly caused by mode collapse, vanishing gradient of generator and indirect assessment criteria of generated samples. In this paper, the supervised signal is introduced into Wasserstein Generative Adversarial Network(WGAN) on the application of one-dimensional data augmentation to alleviate this difficulty. In the proposed method, besides generating fake samples, a well trained generative model is implemented to reconstruct the real samples, whose input data are latent space samples obtained from autoencoder(AE). In addition,the mode collapse can be prevented by the new model through ensuring that the supervised signal grounded in all the available training data. The performance of our method is verified based on parameters of electronic equipment and stock index systematically and quantitatively, and the superiority of the algorithm is demonstrated by the experiment results both in convergence rate and the quality of samples compared with WGAN and VAEGAN.

关键词： supervised signal one-dimensional WGAN VAEGAN AE

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Brain Storming Optimization Algorithm for Heating Dispatch Scheduling of Thermal Power Plant 29

Brain Storming Optimization Algorithm for Heating Dispatch S...

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第29届中国控制与决策会议

作者： Lu Kang Yali Wu Xinrui Wang Xiaohua Feng School of Automation and Information Engineering Xi'an University of Technology Shannxi Province Key Laboratory of Complex System Control and Intelligent Information Processing School of Marine Science and Technology Northwestern Polytechnical University

ISBN: (纸本)9781509046584

In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving and emission-reduction *** this paper,an optimal heat load dispatching model is set up under the premise of power load distribution of thermal power *** kinds of heat loads including to high-pressure,medium-pressure and low-pressure are taken into account in the scheduling *** to the characteristics of the problem,Brain Storming Optimization(BSO) algorithm is used to get the optimal distribution *** comparison with the original scheme shows the correctness of the model and the optimization ***,the optimization ability and effectiveness of brain storming optimization algorithm in solving complex optimization problems are verified by comparing the proposed algorithm with the other different optimization algorithms,such as Particle Swarm Optimization(PSO) and Differential Evolution(DE) algorithm.

关键词： Heating Dispatching Brain Storming Optimization Algorithm Load Distribution

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Future Development Trend of Multimedia Network 2

Future Development Trend of Multimedia Network

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2018 2nd Annual International Conference on Cloud Technology and Communication Engineering, CTCE 2018

作者： Huang, Li Zhang, Kai Hu, Wei Li, Chengcheng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the rapid development of computer network, multimedia technology has gradually spread throughout all aspects of people's life. This article, based on the concept and characteristics of multimedia, expounds the application of multimedia technology and the trend of future development. It makes a heavy analysis of multimedia teaching and analyses the meaning and advantage of multimedia teaching. © Published under licence by IOP Publishing Ltd.

关键词： Multimedia systems

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Sparse Least Square Support Vector Machines based on Random Entropy 36

Sparse Least Square Support Vector Machines based on Random ...

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第36届中国控制会议

作者： Wenlu Ma Han Liu Faculty of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing Faculty of Automation and Information EngineeringXi’an University of Technology

ISBN: (纸本)9781538629185

Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

关键词： Sparsity LSSVM Large-scale data Random Entropy

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Adaptive Backstepping Slide Mode control of Pneumatic Position Servo system

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Chinese Journal of Mechanical Engineering 2016年第5期29卷 1003-1009页

作者： REN Haipeng FAN Juntao Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi'an University of Technology

With the price decreasing of the pneumatic proportional valve and the high performance micro controller, the simple structure and high tracking performance pneumatic servo system demonstrates more application potential in many fields. However, most existing control methods with high tracking performance need to know the model information and to use pressure sensor. This limits the application of the pneumatic servo system. An adaptive backstepping slide mode control method is proposed for pneumatic position servo system. The proposed method designs adaptive slide mode controller using backstepping design technique. The controller parameter adaptive law is derived from Lyapunov analysis to guarantee the stability of the system. A theorem is testified to show that the state of closed-loop system is uniformly bounded, and the closed-loop system is stable. The advantages of the proposed method include that system dynamic model parameters are not required for the controller design, uncertain parameters bounds are not need, and the bulk and expensive pressure sensor is not needed as well. Experimental performance, as compared with some existing methods. results show that the designed controller can achieve better tracking

关键词： pneumatic position servo system adaptive backstepping design slide mode control uncertain parameter tracking accuracy

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Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 177-185页

作者： Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects *** the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor *** research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral *** involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition *** maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects *** performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关键词： distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification

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Motion Blurred Star Image Centroid Optimized Extraction Based on Prior Gaussian Distribution 29

Motion Blurred Star Image Centroid Optimized Extraction Base...

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第29届中国控制与决策会议

作者： Peng Dingbang Chen Hao Sang Xiaochong Li Jianxun School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Institute of Spaceflight Control Technology Shanghai Key Laboratory of Aerospace Intelligent Control Technology

ISBN: (纸本)9781509046584

Star centroid extraction is the precondition of star recognition in star navigation *** image will be motion blurred under high dynamic *** methods such as Wiener filter method,restore the blurred star image with an estimated PSF to calculate the star *** unique characteristic of star images different from general images is ignored in conventional *** addition,the error of motion blur parameters estimation and the approximate linear motion model can decrease the accuracy of star centroids *** paper proposes a method utilizing the prior Gaussian distribution information of star energy to deal with the motion blurred star *** experimental results demonstrate that the proposed method gets smaller error in star centroids calculation compared with the conventional estimation method.

关键词： Motion blur Star image restoration Centroid extraction Gaussian distribution Optimize

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Establishment and Optimization of Gas Flow Prediction Model for Annealing Furnace Based on GA-SVM

Establishment and Optimization of Gas Flow Prediction Model ...

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第37届中国控制会议

作者： Qilin Qu Yan Yuan Jin Yali Yongyue Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System School of Information Science and Engineering Central South University

The annealing furnace is an important equipment for annealing in the production process of cold rolled strip steel. The temperature accuracy of the steel coil is directly affected the production quality of the steel plate. In the annealing furnace temperature control system, strip temperature control is mainly decided by various furnace annealing furnace gas flow in the area, so setting up a precise model between gas flow and temperature has become an important problem of the control system of annealing furnace. In this paper, SVM is applied to the prediction model of the gas flow in the annealing furnace since it’s suitable for nonlinear and high dimensional problems as a machine learning method. In view of that the choice of C, G and the model parameters of the SVM directly affect the prediction precision of the model, this paper uses GA to optimize these two parameters. Finally, we use the data of field production as training and testing samples to establish and verify the model. The experimental results showed that the established model well achieved the requirements of the accuracy of the target.

关键词： Annealing furnace Gas flow Model Support Vector Machine Genetic Algorithm

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Near-Optimal Online Motion Planning of Connected and Automated Vehicles at a Signal-Free and Lane-Free Intersection

Near-Optimal Online Motion Planning of Connected and Automat...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Li, Bai Zhang, Youmin Zhang, Yue Jia, Ning Ge, Yuming Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Mechanical Industrial and Aerospace Engineering Concordia Institute of Aerospace Design and Innovation Concordia University Montreal Canada Division of Systems Engineering Center for Information and Systems Engineering Boston University BostonMA02215 United States Institute of Systems Engineering Tianjin University Tianjin300072 China China Academy of Information and Communications Technology Beijing100191 China

ISBN: (纸本)9781538644522

In this paper, we propose a cooperative motion planning method for a group of connected and automated vehicles (CAVs) crossing a lane-free intersection without using explicit traffic signaling. This multi-vehicle motion planning task is formulated as a centralized optimal control problem. However, the solution to this optimal control problem is numerically intractable due to the high dimensionality of the collision-avoidance constraints and the nonlinearity of the vehicle kinematics. A two-stage strategy is proposed for generating online solutions: at Stage 1, the CAVs are requested to reach a standard formation before entering the intersection;at Stage 2, the vehicles cross the intersection. As the motion planning sub-problem at Stage 2 begins with a standard configuration, the optimal solution to this standard sub-problem can be computed offline in advance and applied online directly. On the other hand, the formation reconfiguration sub-problem at Stage 1 is easy to solve online. Through dividing the entire dynamic process into two periods, the difficulties in the original optimal control problem are significantly reduced so that the real-time performance is achieved. © 2018 IEEE.

关键词： Motion planning

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A high-precision vision-based mobile robot slope detection method in unknown environment

A high-precision vision-based mobile robot slope detection m...

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Chinese Automation Congress

作者： Kenan Du Jing Xin Yaqian Shi Ding Liu Youmin Zhang CETC 20 Shaanxi key Laboratory of Integrated and Intelligent Navigation Xi'an P.R. China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi‘an University of Technology Xi'an P.R. China Department of Mechanical and Industrial Engineering Concordia University Quebec Canada

Aiming at satisfying the demand of slope perception accuracy for mobile robots in the unknown environment, a high-precision vision-based mobile robot slope detection method in unknown environment is proposed in this paper. Firstly, the RGB images of slopesin the unknown environment arecaptured by the Kinect sensor installed on the mobile robot, and edge sharpening is performed on the images of slopes. Then, the coordinates of the slope edge pixel in the image coordinate system are transformed into the camera coordinate system to obtain the real length of the slope. Finally, the single image depth estimation method based on convolution neural network (CNN) is used to estimate the depth of theslope image toobtain the projection length of the slope only using a single slope image, and the slope angle is obtained by the inverse cosine function. The experimental results show that the slope detection method proposed in this paper has higher detection accuracy and can satisfy the demand of slope sensing accuracy of mobile robot in unknown environment.

关键词： Slope detection Coordinate transformation Single image Depth estimation Convolutional neural network (CNN)

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