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IEEE International Conference on Radar

作者： Lei Wang Huabing Wang Mingyan Chen University of Electronic Science and Technology of China Chengdu P.R. China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang P.R. China

ISBN: (数字)9781728126609

ISBN: (纸本)9781728137858

The detection mechanism of airborne radar sensor is simulated dynamically and realistically by data simulation method, including the detection function, the track function and resource scheduling function. A functional level detection model is established, which can realize the detection process of airborne radar and obtain the position and velocity parameters of aerial targets in three work modes such as single target tracking(STT), tracking while searching(TWS) and tracking add searching(TAS). The typical target scene is set up, and the detection mechanism of airborne radar sensor is analyzed and verified through three simulations for different purposes. Meanwhile, the influence of deceptive jamming such as range dragging or speed dragging on airborne radar detection ability is tested and analyzed.

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A practical approach for test equipment perception and virtualization in cloud manufacturing

A practical approach for test equipment perception and virtu...

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Chinese Intelligent systems Conference, CISC 2017

作者： Xu, Yuyan Zhang, Lin Luo, Xiao Zhang, Han School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Engineering Research Center of Complex Product Advanced Manufacturing Systems Ministry of Education Beijing100191 China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing100854 China

ISBN: (纸本)9789811064982

As an advanced manufacturing paradigm, Cloud Manufacturing provides a new way to solve the problems in the testing process. In cloud manufacturing, resource perception and resource virtualization are the foundation of building an actual service platform. According to the characteristic of test equipment, a practical approach for test equipment perception and virtualization is proposed, which is based on cloud computing, Automatic Test Markup Language (ATML) description and data mapping. A test service platform prototype is given to validate the feasibility of this approach. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Virtualization

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AutolEncideraBased for High Spectral Dimensional Data Classification and Visualization 2

AutolEncideraBased for High Spectral Dimensional Data Classi...

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2nd IEEE International Conference on Data science in Cyberspace, DSC 2017

作者： Zhu, Jiang Wu, Lingda Hao, Hongxing Song, Xiaorui Lu, Yi Science and Technology on Complex Electronic System Simulation Laboratory Equipment Academy Beijing China

ISBN: (纸本)9781538615997

The classification and visualization for surface objects receives a great deal of attention for high spectral dimensional data processing. A lot of methods were proposed and applied in this problem over the past decade. Whereas most of them still exist some challenge issues, include pre-treatment fussily, features extraction simplify, larger data processing difficultly and classification inaccurately. To solve these problems, we propose a novel method bases on deep learning which combines maximum noise fraction (MNF) and multilayers auto-encoders. Thereinto, MNF is used to reduce the inherent spectral dimensionality of high spectral dimensional data;autoencoders and softmax logistic regression function are applied for extracting high-level features and visualizing types of objects. On the experiments, we compared with traditional linear SVM method by using a 200-bands AVIRIS dataset over Indian Pines and a 103-bands ROSIS dataset over Italy Pavia. The results indicate that the algorithm in paper statistically significantly improved the classification and visualization accuracies and efficiencies. © 2017 IEEE.

关键词： Visualization

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Design of ELF/VLF Chirp-BOK communication based on modulated heating low ionosphere

Design of ELF/VLF Chirp-BOK communication based on modulated...

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作者： Zhou, Kaijie Wang, Huali Cao, Peipei Luo, Zhangkai Institute of Communications Engineering PLA Army Engineering University Nanjing210007 China College of Physics and Electrical Engineering Huaiyin Normal University Huai'an223300 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101400 China

Excitation of Extremely Low Frequency (ELF)/Very Low Frequency (VLF) from ionosphere,which is artificial modulated by High Frequency (HF) waves can provide a way of antenna generation for deep submarine communication. In this paper, based on plasma energy conservation equation, the theoretical model of amplitude modulation HF pump heating low ionosphere for ELF/VLF generation is established. The linear frequency modulation technique of up-chirp and down-chirp have good self-correlation and cross-correlation, by which information can be transmitted by up-chirp and down-chirp. Thus, the linear frequency modulation technique can be applied to the ionosphere ELF/VLF communication. Based on this, a Chirp-BOK (Binary Orthogonal Keying) communication scheme is proposed. Indeed the Chirp-BOK amplitude and power modulation function are designed by combining the linear frequency modulation technique with the square wave amplitude modulation technique. The simulation results show in the condition that the ionosphere is heated by the Chirp-BOK power modulation HF waves, the temperature of ionospheric electronic and the variations of conductivity have obvious frequency modulation characteristics which are the same as that of power modulation, so does the variation of ionospheric current. Thus, when the ionosphere is heated by Chirp-BOK power modulation HF waves, the up-chirp (symbol '0') and down-chirp (symbol '1') ELF/VLF signals can be generated. Copyright © 2018 The Institute of electronics, Information and Communication Engineers.

关键词： Ionosphere

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Research of multi-Agent cognition and decision 17

Research of multi-Agent cognition and decision

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2017 International Conference on E-Commerce, E-Business and E-Government, ICEEG 2017

作者： Liu, Dong Yan, Xuefei Li, Xinming Wang, Shoubiao Science and Technology on Complex Electronic System Simulation Laboratory Equipment Academy Beijing China

ISBN: (纸本)9781450352482

Reinforcement Learning(RL) is the main cognition and decision method of Agent since its adaptation and exploration ability to the unknown environment, but it cannot ensure the convergence for the multi-Agent situation since the transition of the state space is not only influenced by the Agent itself but also influenced by the other Agent's action, so the other Agent's action has to be taken into account. In consideration of the deep theory foundation and advantages to deal with the other player's policy, this paper think that combination of the games theory and multi-Agent is a breakthrough for the cognition and decision in the status of the multi-Agent Learning. Based on the introduction of the traditional decision technology, the learning technology and the typical algorithm framework of the multi-Agent reinforcement learning with the games theory, some related algorithms such as MinMax-Q, nash-Q, FF-Q, CE-Q and Nego-Q were presented and analyzed. © 2017 Association for Computing Machinery.

关键词： Reinforcement learning

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Publisher Correction: Ozone pollution mitigation strategy informed by long-term trends of atmospheric oxidation capacity

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Nature Geoscience 2024年 172页

作者： Wenjie Wang Xin Li Ying Liu Sihua Lu Yusheng Wu Shiyi Chen Keding Lu Min Hu Limin Zeng Min Shao Yuanhang Zhang Hang Su Yafang Cheng Ruijing Ni Ulrich Pöschl Zhaofeng Tan Franz Rohrer Andreas Wahner David D. Parrish Cheng Huang Xudong Tian K. M. Leung Liangfu Chen Meng Fan Qiang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing China State Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Max Planck Institute for Chemistry Mainz Germany International Joint Laboratory for Regional Pollution Control Ministry of Education Beijing China Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing China Institute of Carbon Neutrality Peking University Beijing China Institute for Environmental and Climate Research Jinan University Guangzhou China State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex Shanghai Academy of Environmental Sciences Shanghai China Institut für Energie-und Klimaforschung: Troposphäre (IEK-8) Forschungszentrum Jülich Jülich Germany Key Laboratory of Ecological and Environmental Monitoring Forewarning and Quality Control of Zhejiang Zhejiang Ecological and Environmental Monitoring Center Hangzhou China Environmental Protection Department The Government of the Hong Kong Special Administrative Region Hong Kong China State Key Laboratory of Remote Sensing Science The Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Earth System Science Tsinghua University Beijing China

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An evaluation method for network communication syst em efficiency based on multi-source information fusion

An evaluation method for network communication syst em effic...

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第37届中国控制会议

作者： Cao Yuan Wang Yue-ping Tang Xue-mei Zhao Xiao-fang Science and Technology on Complex Land Systems Simulation Laboratory Beijing Institute of Spacecraft System Engineering

The number of experimental data obtained under different conditions is large, and there are some differences in the distribution characteristics of samples. If a large number of different types of data are mixed and the network communication Effectiveness evaluation is carried out, the characteristics of some samples may be drowned out, on the other hand, the information of many experimental samples is not fully utilized. In order to solve this problem, this paper proposes a method of network communication effectiveness evaluation based on multi-source information fusion. This method firstly analyzes the communication success rate of the cell communication node by longitudinal fusion of experimental information. Based on this, the confidence degree is used to construct the weighting coefficient, and the whole communication success rate of the network information system is evaluated by the experiment information of multiple nodes. Thus, the comprehensive evaluation of the communication efficiency of the unit node and the network Information system is realized.

关键词： multi-source information network communication effectiveness evaluation communication success rate

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Characteristic Analysis of Monobit Signal

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Pengfei Ji Xiao Yang Chenglong Xia Huan Lv State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Technology National University of Defense Technology Changsha Hunan 410073 China. School of Electronic Science National University of Defense Technology Changsha Hunan 410073 China. Military Representative Office of Rocket Force in Changsha Changsha Hunan 4100205 China.

This paper analyzes the characters of monobit signals and gives the reason why the spurious free dynamic ranges (SFDR) of monobit signals is much lower than those of multi-bit signals. According to our analysis and simulation, if the third harmonic of a monobit signal can be filtered, the SFDR of it can be improved well. Therefore, we give the way to obtain the desired band, which do not contain any third harmonics, and the simulation results are in agreement with our design. Such way can be used to improve the performance of monobit digital receivers in engineering.

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Multi-criteria collaborative filtering based on matrix filling 4

Multi-criteria collaborative filtering based on matrix filli...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Ye, Li Wu, Chunming Qiang, Baohua Xie, Wu College of Computer Information Science Southwest University Chongqing China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781538683392

In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strategy and proposes to use the matrix filling method to handle the missing data. Firstly, an improved similarity algorithm combined with the Jaccard Similarity Coefficient is used to calculate the predicted rating. Secondly, the Reliable Factor is introduced to prevent the error of filling data. Finally, multiple linear regression is employed for aggregating the ratings of the criteria to eventually yield preferred predicted ratings. A series of experiments show that the proposed method can precisely reflect the user's similarity and achieve better results in predicted ratings, in contrast to traditional methods. © 2018 IEEE.

关键词： Collaborative filtering

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Publisher's Note: “Coupling analysis of transient aerodynamic and dynamic response of articulated heavy vehicles under crosswinds” [Phys. Fluids 34, 017106 (2022)]

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Physics of Fluids 2022年第2期34卷

作者： Qianwen Zhang (张倩文) Chuqi Su (苏楚奇) Makoto Tsubokura (坪倉誠) Zhen Hu (胡振) Yiping Wang (汪怡平) 1Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan 430070 China 2Hubei Collaborative Innovation Center for Automotive Components Technology Wuhan University of Technology Wuhan 430070 China 3Hubei Research Center for New Energy and Intelligent Connected Vehicle Wuhan University of Technology Wuhan 430070 China 4Graduate School of System Informatics Kobe University Kobe Hyogo 657-8501 Japan 5Complex Phenomena Unified Simulation Research Team RIKEN Advanced Institute for Computational Science Kobe Hyogo 650-0047 Japan 6Shanghai Huawei Technology Co. Shanghai 201206 China

This article was originally published online on 7 January 2022 with an incorrect page number. The correct page number is 017106. All online versions of this art

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