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IET International Radar Conference 2018, IRC 2018

作者： Gao, Jing-Peng Shen, Liang-Xi Ye, Fang Wang, Shang-Yue Zhang, Ran College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing Institute of Space Long March Vehicle Beijing China

In order to solve the problem that radar emitter signal modulation recognition under low signal-to-noise ratio (SNR) has difficulty in selecting the parameters of the classifier and the problem of low recognition rate caused by the improper selection of features, a multi-feature fusion modulation recognition algorithm based on improved particle swarm optimisation (PSO) algorithm is proposed. Firstly, the algorithm uses Choi-Williams distribution time-frequency analysis to transform the radar signals into time-frequency images, and combines transfer learning and principal component analysis (PCA) to extract the transfer features. Then it extracts the Renyi entropy and AR model coefficients of the signals and combines with the image features to realise multi-feature fusion. Finally, the improved PSO algorithm optimises support vector machine parameters so that population converge quickly. The simulation results show that the recognition rate of this algorithm is 95.3% when the SNR is 0 dB. The number of iterations of this algorithm is small, the signal recognition rate of the system is improved, and the effectiveness of the system is improved by applying PCA. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Principal component analysis

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Photoinduced anisotropic lattice dynamic response and domain formation in thermoelectric SnSe

arXiv

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arXiv 2021年

作者： Wang, Wei Wu, Lijun Li, Junjie Aryal, Niraj Jin, Xilian Liu, Yu Fedurin, Mikhail Babzien, Marcus Kupfer, Rotem Palmer, Mark Petrovic, Cedomir Yin, Weiguo Dean, Mark P.M. Robinson, Ian Tao, Jing Zhu, Yimei Condensed Matter Physics and Materials Science Division Brookhaven National Laboratory UptonNY11973 United States State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Accelerator Science & Technology Initiative Accelerator Test Facility Brookhaven National Laboratory UptonNY11973 United States London Centre for Nanotechnology University College LondonWC1E 6BT United Kingdom

Identifying and understanding the mechanisms behind strong phonon-phonon scattering in condensed matter systems is critical to maximizing the efficiency of thermoelectric devices. To date, the leading method to address this has been to meticulously survey the full phonon dispersion of the material in order to isolate modes with anomalously large linewidth and temperature-dependence. Here we combine quantitative MeV ultrafast electron diffraction (UED) analysis with Monte Carlo based dynamic diffraction simulation and first-principles calculations to directly unveil the soft, anharmonic lattice distortions of model thermoelectric material SnSe. A small single-crystal sample is photoexcited with ultrafast optical pulses and the soft, anharmonic lattice distortions are isolated using MeV-UED as those associated with long relaxation time and large displacements. We reveal that these modes have interlayer shear strain character, induced mainly by c-axis atomic displacements, resulting in domain formation in the transient state. These findings provide an innovative approach to identify mechanisms for ultralow and anisotropic thermal conductivity and a promising route to optimizing thermoelectric devices. © 2021, CC BY.

关键词： Monte Carlo methods

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Dimension degradation of fractionally spaced super-exponential algorithm for sparse channel equalisation

Dimension degradation of fractionally spaced super-exponenti...

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IET International Radar Conference 2018, IRC 2018

作者： Bai, JinLiang Jiang, ZhiYe Wang, Feng Zhou, Yi Sun, Heng National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing China Array and Information Processing Laboratory College of Computer and Information Hohai University West Focheng Road No. 8 Jiangning District Nanjing China

Fractionally spaced super-exponential (FSSE) algorithm has the disadvantage of computational complexity since it exploits high-order statistics explicitly. The authors propose a dimension degradation technique for FSSE when it is applied to the equalisation of a sparse channel in accordance with the relationship between the coefficients of the cross fourth-order cumulant (CFOC) in FSSE and the channel impulse response. They implement partial updating on the prominent coefficients of the CFOC with all the small coefficients remaining unchanged. The computational complexity of the modified FSSE reduces significantly with an acceptable performance loss, and its performance is validated via numerical simulations. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Computational complexity

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Effect of celestial body gravity on Taiji mission range and range acceleration noise

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Physical Review D 2022年第10期106卷 102005-102005页

作者： Xiaoqing Han Xiaodong Peng Wenlin Tang Zhen Yang Xiaoshan Ma Chen Gao Li-e Qiang Yuzhu Zhang Mengyuan Zhao Jiafeng Zhang Binbin Liu Key Laboratory of Electronics and Information Technology for Space System National Space Science Center Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Taiji Laboratory for Gravitational Wave Universe Hangzhou 310024 China Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province Hangzhou 310024 China

The Taiji project is a space gravitational-wave detection mission consisting of three satellites that form a giant equilateral triangle with a side length of approximately 3×106 km in the heliocentric orbit. The target of the mission is to detect gravitational-wave signals in the 0.1 mHz to 1 Hz band. However, its intersatellite distances are affected by celestial bodies in the solar system, introducing changes that may reach levels of 30,000 km in magnitude. Thus, it is important to investigate whether the gravitational fields of celestial bodies in the detection frequency band will have an impact on the realization of ultrahigh-sensitivity intersatellite ranging measurements. In this study, a high-precision orbit propagator with an accuracy of 0.03 pm on the 1 AU scale is developed. Based on this orbit propagator, the impact of the major large celestial bodies in the solar system on the detection sensitivity in the intersatellite measurements are analyzed. Detailed numerical studies show that the linear spectral density of the range and range acceleration are approximately 1×10−13 m/Hz and 5×10−23 m/s2/Hz, respectively, at 0.1 mHz, indicating that the gravitational disturbance generated by the major celestial bodies in the solar system will not affect the aim of the Taiji mission to detect gravitational waves in the frequency band 0.1 mHz to 1 Hz.

关键词： Gravitational wave detection Gravitational wave detectors

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A unified back-projection correction algorithm for squint SAR based on SPECAN processing

A unified back-projection correction algorithm for squint SA...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Chao Wang Heng Sun Xin-Yu Zhang Ran Zhang National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing P.R. China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The missile-borne or airborne synthetic aperture radar (SAR) usually adopts the squint mode and subaperture to satisfy maneuvering and the real-time processing. A realtime imaging algorithm of squint SAR based on SPECAN processing is presented. It has high computational efficiency in low resolution. A unified back-projection algorithm is proposed to solve the problem of fan-shaped distortion and geometric distortion. Simulation results validate the effectiveness of the proposed method.

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Insect wing-beat frequency automatic extraction and experimental verification with a Ku-band insect radar system

Insect wing-beat frequency automatic extraction and experime...

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IET International Radar Conference 2018, IRC 2018

作者： Zhang, Tianran Liu, XiangRong Hu, Cheng Wang, Rui Liu, Changjiang Li, Wenqing Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing100076 China Key Laboratory of Electronic and Information Technology in Satellite Navigation Beijing Institute of Technology Ministry of Education Beijing100081 China

The wing-beat frequency is one of the most important behavioural parameters of migratory insects and is widely used to distinguish insect species due to the diversity of wing-beat frequency among different species. Traditional insect radar has been able to achieve automatic extraction of the wing-beat frequency. However, its low-range resolution and fixed threshold detection may cause false alarms and reduce the precision of wing-beat frequency. The authors' Ku-band insect radar system enables high-resolution, full-polarised detection of insects, but has not achieved automatic extraction of wing-beat frequency yet. The authors propose an insect wing-beat frequency automatic extraction algorithm based on a Ku-band insect radar system. The algorithm adopts the constant false alarm rate detector and tracks the association algorithm to get target echo automatically and combines the amplitude spectrum method and the phase spectrum method to achieve wing-beat frequency extraction. The authors carried out the outfield experiment using the radar system and used the collected data to test the proposed algorithm. The experiment results confirm that the algorithm can extract the wing-beat frequency of insects automatically based on the Ku-band insect radar system, which can improve system efficiency. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Errors

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Learning to Infer Weather States Using Partial Observations

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu China Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China College of Computer National University of Defense Technology Hunan China Clustertech LTD Hong Kong China National Climate Center China Meteorological Administration Beijing China

Accurate state estimation of the high-dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low-dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty-aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. Deep generative model enables accurate spatial interpolation of weather variables from sparse observations The generative model produces spatially coherent and uncertainty-aware estimates, effectively constrained by observational data The approach provides a framework to quantify observation impact on uncertainty reduction, enabling optimization of observation networks Accurate estimation of weather conditions across a large area is crucial but challenging due to the complex and chaotic nature of the atmosphere. Traditional methods rely on combining observations with forecasts, which can be comp

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Influences of the NAO on the North Atlantic CO2 Fluxes in Winter and Summer on the Interannual Scale

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Advances in Atmospheric sciences 2019年第11期36卷 1288-1298页

作者： Yujie JING Yangchun LI Yongfu XU Guangzhou FAN State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu 610225 China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Qingdao 266237 China Department of Atmospheric Chemistry and Environmental Sciences College of Earth Science University of Chinese Academy of Sciences Beijing 100049 China

The differences in the influences of the North Atlantic Oscillation (NAO) on the air–sea CO2 fluxes (fCO2) in the North Atlantic (NA) between different seasons and between different regions are rarely fully investigated. We used observation-based data of fCO2, surface-ocean CO2 partial pressure (pCO2sea), wind speed and sea surface temperature (SST) to analyze the relationship between the NAO and fCO2 of the subtropical and subpolar NA in winter and summer on the interannual time scale. Based on power spectrum estimation, there are significant interannual signs with a 2–6 year cycle in the NAO indexes and area-averaged fCO2 anomalies in winter and summer from 1980 to 2015. Regression analysis with the 2–6 year filtered data shows that on the interannual scale the response of the fCO2 anomalies to the NAO has an obvious meridional wave-train-like pattern in winter, but a zonal distribution in summer. This seasonal difference is because in winter the fCO2 anomalies are mainly controlled by the NAO-driven wind speed anomalies, which have a meridional distribution pattern, while in summer they are dominated by the NAO-driven SST anomalies, which show distinct zonal difference in the subtropical NA. In addition, in the same season, there are different factors controlling the variation of pCO2sea in different regions. In summer, SST is important to the interannual variation of pCO2sea in the subtropical NA, while some biogeochemical variables probably control the pCO2sea variation in the subpolar NA.

关键词： air-sea CO2 flux North Atlantic Oscillation interannual time scale wind speed surface-ocean CO2 partial pressure

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science and Prediction of Heavy Rainfall over China:Research Progress since the Reform and Opening-Up of New China

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Journal of Meteorological Research 2020年第3期34卷 427-459页

作者： Yali LUO Jisong SUN Ying LI Rudi XIA Yu DU Shuai YANG Yuanchun ZHANG Jing CHEN Kan DAI Xueshun SHEN Haoming CHEN Feifan ZHOU Yimin LIU Shenming FU Mengwen WU Tiangui XIAO Yangruixue CHEN Huiqi LI Mingxin LI State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044 School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesSun Yat-sen UniversityGuangzhou 519082 Key Laboratory of Cloud–Precipitation Physics and Severe Storms Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 National Meteorological Center China Meteorological AdministrationBeijing 100081 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 Zhejiang Institute of Meteorological Sciences Zhejiang Meteorological BureauHangzhou 310008 School of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225 Institute of Heavy Rain China Meteorological AdministrationWuhan 43020510 Institute of Tropical and Marine MeteorologyChina Meteorological Administration Guangzhou 510640 Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082 University of Chinese Academy of Sciences Beijing 100049 CAS Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of Sciences(CAS)Beijing 100101

This paper reviews the major progress on development of the science and prediction of heavy rainfall over China since the beginning of the reform and opening-up of new China(roughly between 1980 and 2019).The progress of research on the physical mechanisms of heavy rainfall over China is summarized from three perspectives:1)the relevant synoptic weather systems,2)heavy rainfall in major sub-regions of China,and 3)heavy rainfall induced by *** development and application of forecasting techniques for heavy rainfall are summarized in terms of numerical weather prediction techniques and objective forecasting *** aided by the rapid progress in meteorological observing technology and substantial improvement in electronic computing,studies of heavy rainfall in China have advanced to investigating the evolution of heavy-rain-producing storms and observational analysis of the cloud microphysical features.A deeper and more systematic understanding of the synoptic systems of importance to the production of heavy rainfall has also been *** forecast of heavy rainfall in China has changed from subjective weather event forecasts to a combination of both subjective and objective quantitative precipitation forecasts,and is now advancing toward probabilistic quantitative precipitation forecasts with the provision of forecast uncertainty information.

关键词： heavy rainfall reform and opening-up of new China physical mechanisms forecasting techniques

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General-purpose machine-learned potential for 16 elemental metals and their alloys

arXiv

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arXiv 2023年

作者： Song, Keke Zhao, Rui Liu, Jiahui Wang, Yanzhou Lindgren, Eric Wang, Yong Chen, Shunda Xu, Ke Liang, Ting Ying, Penghua Xu, Nan Zhao, Zhiqiang Shi, Jiuyang Wang, Junjie Lyu, Shuang Zeng, Zezhu Liang, Shirong Dong, Haikuan Sun, Ligang Chen, Yue Zhang, Zhuhua Guo, Wanlin Qian, Ping Sun, Jian Erhart, Paul Ala-Nissila, Tapio Su, Yanjing Fan, Zheyong Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Hunan University Changsha410082 China MSP group QTF Centre of Excellence Department of Applied Physics Aalto University P.O. Box 15600 Aalto EspooFI-00076 Finland Chalmers University of Technology Department of Physics Gothenburg41926 Sweden National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Civil and Environmental Engineering George Washington University WashingtonDC20052 United States Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong N.T. Shatin999077 Hong Kong Department of Physical Chemistry School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Institute of Zhejiang University-Quzhou Quzhou324000 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong School of Science Harbin Institute of Technology Shenzhen518055 China College of Physical Science and Technology Bohai University Jinzhou121013 China Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Leicestershire LoughboroughLE11 3TU United Kingdom

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiation damage

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