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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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Cooperative Lane Change Motion Planning of Connected and Automated Vehicles: A Stepwise Computational Framework

Cooperative Lane Change Motion Planning of Connected and Aut...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

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

ISBN: (纸本)9781538644522

This paper focuses on the scheme of cooperative lane change motion planning of multiple connected and automated vehicles, so as to minimize the time for lane change while penalizing large steering angles subject to hard collision avoidance constraints. Nominally this scheme should be formulated in a centralized way with the constraints of all the vehicles considered simultaneously. In order to facilitate the numerical solving process of this centralized optimization problem, we propose a stepwise computation framework. Starting with a sub-problem with all of the collision avoidance constraints removed, a sequence of sub-problems are defined by adding back the removed collision avoidance constraints gradually until the original problem takes shape in the end. The optimum of one sub-problem is always used as the initial guess when solving the next sub-problem. This iterative process continues until the optimum of the original problem is obtained. In this way, the difficulties in the original centralized problem are divided into multiple parts, and every progress made to address the partial difficulties is 'solidified' by the initial guess. © 2018 IEEE.

关键词： Collision avoidance

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Convolutional ordinal regression forest for image ordinal estimation

arXiv

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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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Modeling and simulation on melt flow and thermal in crystal growth 36

Modeling and simulation on melt flow and thermal in crystal ...

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第36届中国控制会议

作者： Ni Zhang Ding Liu WeiChao Huang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781538629185

A coupled thermal flow crystal growth model based on Lattice Boltzmann Method（LBM） is constructed for the internal melt in a single crystal furnace,according to the characteristic that the internal temperature distribution of the melt is difficult to *** the melt’s characteristic on the free surface boundary,the specular reflection scheme is used on the free *** flow pattern and the temperature of the internal melt under different growth environment are simulated by the proposed *** results show that,in the pure heating environment,the fiercer melt flows,the greater the temperature gradient below the crystal growth interface,and the more uneven the temperature distribution on the free surface;crystal rotation and crucible rotation reduce the temperature gradient below the growth interface,and improve the temperature distribution uniformity of the free *** simulation result is consistent with the experiment result,which proves the correctness of the simulation model and the boundary scheme on the free surface.

关键词： Modeling computer simulation crystal growth thermal-flow Lattice Boltzmann Method

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Assessment and prediction of leakage degree of expansion joints in underground network heating pipe network based on LS-SVM and ARIMA model

Assessment and prediction of leakage degree of expansion joi...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jiao Shang-bin Fan Le Wu Peng-yue Qiao Lei Jin Yong-ze Shen Qian-kun Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Xi'an Thermal Power Research Institute Co. Ltd Xi'an China

The study shows that the leakage of the expansion joints in the underground heating primary pipe network will lead to the fluctuation of soil temperature and conductivity around the leakage point. Based on the online monitoring of changes of physical quantities around the expansion joint, this paper established the assessment model of temperature and conductivity as the input parameters and the degree of expansion joints leakage as the output parameters, which took the radial basis function as kernel function and made use of PSO algorithm to achieve the optimization of the kernel function parameters. Then, the temperature and conductivity were predicted based on the ARIMA model, and the LS-SVM assessment of expansion joint leakage was realized according to the prediction results. Both simulation and field experiments show that the prediction model based on ARIMA has higher accuracy, which has identified the foundation for on-line prediction and accurate assessment of the expansion joint leakage in the underground heating primary pipe network.

关键词： Predictive models Heating systems Monitoring Prediction algorithms Conductivity Classification algorithms Optimization

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Radio-wave communication with chaos

arXiv

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arXiv 2018年

作者： Ren, Hai-Peng Zheng, Wu-Yun Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex System and Mathematical Biology SUPA University of Aberdeen AberdeenAB243UE United Kingdom

The invariance of the Lyapunov exponent of a chaotic signal as it propagates along a wireless transmission channel provides a theoretical base for the application of chaos in wireless communication. In additive Gaussian channel, the chaotic signal is proved to be the optimal coherent communication waveform in the sense of using the very simple matched filter to obtain the maximum signal-to-noise ratio. The properties of chaos can be used to reduce simply and effectively the Inter-Symbol Interference (ISI) and to achieve low bit error rate in the wireless communication system. However, chaotic signals need very wide bandwidth to be transmitted in the practical channel, which is difficult for the practical transducer or antenna to convert such a broad band signal. To solve this problem, in this work, the chaotic signal is applied to a radio-wave communication system, and the corresponding coding and decoding algorithms are proposed. A hybrid chaotic system is used as the pulse-shaping filter to obtain the baseband signal, and the corresponding matched filter is used at the receiver, instead of the conventional low-pass filter, to maximize the signal-to-noise ratio. At the same time, the symbol judgment threshold determined by the chaos property is used to reduce the Inter-Symbol Interference (ISI) effect. Simulations and virtual channel experiments show that the radio-wave communication system using chaos obtains lower bit error rate in the multi-path transmission channel compared with the traditional radio-wave communication system using Binary Phase Shift keying (BPSK) modulation technology and channel equalization. Copyright © 2018, The Authors. All rights reserved.

关键词： Matched filters

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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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Establishment and Optimization of Gas Flow Prediction Model for Annealing Furnace Based on GA-SVM 37

Establishment and Optimization of Gas Flow Prediction Model ...

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37th Chinese control Conference, CCC 2018

作者： Qu, Qilin Yuan, Yan Yali, Jin Zhang, Yongyue China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China School of Information Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789881563941

T0616. 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. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Flow of gases

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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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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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