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Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

Frontiers of information Technology & Electronic Engineering 2017年第12期18卷 1991-2000页

作者： Zong-feng QI Qiao-qiao LIU Jun WANG Jian-xun LI State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003 China MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China Luoyang Electronic Equipment Test Center of China Luoyang 471000 China

The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is proposed in this paper, which can be applied to battle damage assessment （BDA）. This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine （UCI） data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.

关键词： Battle damage assessment Improved Kullback-Leibler divergence sparse autoencoder Structural optimization Feature selection

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A Feature Matching Method For Simultaneous Localization And Mapping 2

A Feature Matching Method For Simultaneous Localization And ...

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2017 IEEE 2nd information Technology, Networking, Electronic and Automation control Conference (ITNEC 2017)

作者： Chen Xiangkui Jiang Min Zuo Liangyu Jiang jian College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

ISBN: (纸本)9781509064151;9781509064144

Simultaneous Localization and Mapping is an important technology which help a mobile robot to determine its location and build the environment map. Recently, the RGBD sensor is widely used in the robot, research on RGBD-SLAM becomes a hot topic. In order to calculate the movement parameters of robot, feature matching is adopted to register the two adjacent RGBD images in the video stream. This paper proposed an improved feature matching method for RGBDSLAM. The experiment results show that, compared with the traditional SIFT feature matching methods for RGBD-SLAM,the performance of the proposed method is improved significantly.

关键词： Simultaneous Localization and Mapping Feature Matching SIFT FREAK

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Toward stronger robustness of network controllability: A snapback network model

arXiv

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arXiv 2018年

作者： Lou, Yang Wang, Lin Chen, Guanrong Department of Electronic Engineering City University of Hong Kong Hong Kong SAR Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

A new complex network model, called q-snapback network, is introduced. Basic topological characteristics of the network, such as degree distribution, average path length, clustering coefficient and Pearson correlation coefficient, are evaluated. The typical 4-motifs of the network are simulated. The robustness of both state and structural controllabilities of the network against targeted and random node- and edge-removal attacks, with comparisons to the multiplex congruence network and the generic scalefree network, are presented. It is shown that the q-snapback network has the strongest robustness of controllabilities due to its advantageous inherent structure with many chain- and loop-motifs. Copyright © 2018, The Authors. All rights reserved.

关键词： controllability

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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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ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIMETRIC CIRCULAR SAR MULTI-ASPECT SIGNATURES

ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIME...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yang Li Yun Lin Wen Hong Ran Xu Zhimin Zhuo Qiang Yin Beijing Institute of Electronic System Engineering Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Science Beijing China Beijing University of Chemical Technology Beijing China

Circular synthetic aperture radar (CSAR) can provide distinctive multi-aspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in a SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multi-aspect and fully polarimetric SAR signatures of point-like and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by use of constant false alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science (IECAS). The results indicate the framework can retain anisotropic scattering and extract a series of new multi-aspect polarimetric SAR signatures for terrain classification.

关键词： Scattering Apertures Synthetic aperture radar Covariance matrices Radar polarimetry Image color analysis Optical imaging

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Reliability analysis and matpower simulation of IEEE14 node based on mixed entropy measure

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European Journal of Electrical Engineering 2018年第5-6期20卷 573-588页

作者： Shao, Zhenhua Zhong, Zhixiong Lin, Wenzhong China Digital Fujian IoT Laboratory of Intelligent Production Minjiang University Fujian350108 China Fujian Province Key Laboratory of Information Processing and Intelligent Control Minjiang University Fujian350108 China College of Computer and Control Engineering Minjiang University Fujian350108 China

With the increasingly large-scale interconnection of power system, the object of this paper was to analyze the fragility of the fault of IEEE14 nodes based on the mixed entropy *** mixed entropy approach was adopted to quantify the fragile links in the system, which is made up by the flow entropy and the risk entropy. The simulation experiments involved in the study were implemented in the MATPOWER toolbox of the MATLAB platform. The results obtained in this study include that the flow entropy is the key factor on unbalanced distribution of power grid, furthermore the safety of the whole power grid can be achieved a quantitative assessment by the risk entropy. The simulation experiments of IEEE-14 node involved in the study were implemented in the MATPOWER toolbox of the MATLAB platform. The results were presented in the form of data and histograms. The impacts of the obtained results are that the transfer entropy is modified by distribution entropy of power flow. the findings of this study may do good to the power network vulnerability analysis with large nodes. © 2018 Lavoisier.

关键词： Failure (mechanical)

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Chain Recurrent Point and Devaney Chaos of Topological Groups

Chain Recurrent Point and Devaney Chaos of Topological Group...

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International Conference on Smart City and systems Engineering

作者： JI Zhanjiang QIN Guijiang ZHAI Cong Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University China School of Transportation and Civil Engineering and Architecture Fo Shan University China

ISBN: (纸本)9781728113678;9781728113661

Chain recurrent point, strong chain recurrent point and Devaney chaos have an important significance in terms of theory and application. In this paper, we will introduce the concept of chain recurrent point, strong chain recurrent point and Devaney chaos. By means of properties of the product space and topological conjugation, We study the dynamical properties of chain recurrent point, strong chain recurrent point and Devaney chaos. It is gived that the following conclusions. (1) The set CR(G) and SCR(G) are a closed set. (2) The set CR(G) and SCR(G) are strongly invariable for G. (3) If G_1 and G_2 are to be topologically conjugate, then the group G_1 is Devaney chaotic if and only if the group G_2 is Devaney chaotic;(4) The group G_1 × G_2 is Devaney chaotic if and only if the group G_1 and G_2 are Devaney chaotic. These results enrich the theory of chain recurrent point, strong chain recurrent point and Devaney chaos under the action of topological group

关键词： Chaos Extraterrestrial measurements Smart cities systems engineering and theory Data science Software engineering

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Multiple Kernel Learning for Representation-based Classification of Hyperspectral Images

Multiple Kernel Learning for Representation-based Classifica...

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第30届中国控制与决策会议

作者： Yu Qin Xiaoyong Bian Yuxia Sheng School of Computer Science Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Information Science and Engineenring Wuhan University of Science and Technology

In the paper we propose a multiple kernel learning framework for representation-based classification（MKLC） of hyperspectral images. Unlike the existing methods that often exploit the single feature extraction method or the single kernel method;moreover, the single feature representation and kernelized RC is biased and less stable due to the high coherence of the training samples. The proposed approach is different from traditional kernelized methods and characterized by multiple features and multiple kernel learning in a representation-based classification manner. Experimental results on several real HSI datasets demonstrate that the proposed method can achieve superior performance than the state-of-the-art classification methods.

关键词： Hyperspectral image (HSI) multiple kernels learning (MKL) multiple features representation-based classification

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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic (vol 11, 5172, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 5172页

作者： Liu, Zhu Ciais, Philippe Deng, Zhu Lei, Ruixue Davis, Steven J. Feng, Sha Zheng, Bo Cui, Duo Dou, Xinyu Zhu, Biqing Guo, Rui Ke, Piyu Sun, Taochun Lu, Chenxi He, Pan Wang, Yuan Yue, Xu Wang, Yilong Lei, Yadong Zhou, Hao Cai, Zhaonan Wu, Yuhui Guo, Runtao Han, Tingxuan Xue, Jinjun Boucher, Olivier Boucher, Eulalie Chevallier, Frederic Tanaka, Katsumasa Wei, Yiming Zhong, Haiwang Kang, Chongqing Zhang, Ning Chen, Bin Xi, Fengming Liu, Miaomiao Breon, Francois-Marie Lu, Yonglong Zhang, Qiang Guan, Dabo Gong, Peng Kammen, Daniel M. He, Kebin Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232 Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Institut Pierre-Simon Laplace Sorbonne Université / CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France. Center for Energy and Environmental Policy Research Beijing Institute of Technology Beijing China. Department of Electrical Engineering the State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and G

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5

关键词： Climate sciences Atmospheric science Environmental sciences Environmental social sciences

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Preventive Soot Blowing Strategy based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy based on State of Health Pr...

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第37届中国控制会议

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering North University of China School of Computer Science and Technology North University of ChinaSchool of Computer Science and Technology Shanghai Jiao Tong University Department of Automationand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal-fired power plant boiler.A preventive soot blowing strategy based on state of health prediction for heat transfer surface was *** average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing *** to the theory of renewal process,an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was *** using Particle Swarm Optimization（PSO）,the optimal preventive cycle and soot-blowing threshold were obtained,and the long-run average cost rate was the *** model is validated with experiment data of a 300 MW coal-fired power plant boiler and the parameters of prediction model are *** results can verify the feasibility of proposed *** can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Coal-fired Boiler Heat Transfer Surface Soot-blowing State of Health（SOH） Prediction

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