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arXiv 2022年

作者： Zhai, Shixian Jacob, Daniel J. Shah, Viral Yang, Laura Hyesung Zhang, Qiang Sun, Yele Wang, Shuxiao Luo, Gan Yu, Fangqun Woo, Jung-Hun Kim, Younha Kim, Hwajin Dibb, Jack E. Lee, Taehyoung Han, Jin-Seok Li, Ke Liao, Hong John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Earth System Science Tsinghua University Beijing100084 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Atmospheric Sciences Research Center University at Albany AlbanyNY12226 United States Department of Civil and Environmental Engineering Konkuk University Seoul05029 Korea Republic of Laxenburg 2361 Austria Department of Environmental Health Sciences Graduate School of Public Health Seoul National University Seoul08826 Korea Republic of Institute for the Study of Earth Oceans and Space University of New Hampshire DurhamNH03824 United States Department of Environmental Science Hankuk University of Foreign Studies Yongin449791 Korea Republic of Department of Environmental and Energy Engineering Anyang University Anyang14028 Korea Republic of 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 and Technology Nanjing210044 China

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM) between 2.5 μm and 10 μm aerodynamic diameter, as inferred by the difference between PM10 and PM2.5 observations. This coarse PM averages 47 μg m-3 in the North China Plain (NCP) region in 2015-2019 and 21 μg m-3 in the Seoul Metropolitan Area (SMA). It is dominantly contributed by urban fugitive dust, rather than by natural dust as is often presumed. Concentrations decreased by 21% from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. This anthropogenic coarse PM is generally not included in air quality models but acts as a sink of nitric acid (HNO3), thus affecting fine particulate nitrate which is a major air quality concern in China and South Korea. GEOS-Chem model simulation of surface and aircraft observations from the KORUS-AQ campaign over the SMA in May-June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of PM1 nitrate. Anthropogenic coarse PM in the model increases the sensitivity of PM2.5 nitrate to ammonia (NH3) emission in winter. In summer, anthropogenic coarse PM directly affects PM2.5 nitrate by HNO3 uptake, and we find that the decrease of anthropogenic coarse PM over 2015-2019 offset the PM2.5 nitrate reductions expected from decreasing NOx and NH3 emissions. Copyright © 2022, The Authors. All rights reserved.

关键词： Nitrates

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Research on Algorithm for Model Predictive control Based on Robust Optimizition

Research on Algorithm for Model Predictive Control Based on ...

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第三十八届中国控制会议

作者： Xia Zhang Ding Liu Fei Yu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

The practicality of model predictive control(MPC) is partially limited by its ability to solve optimization problems in real time. The robust model prediction algorithm based on linear matrix inequality is divided into online and offline parts. In order to improve the response speed and control precision of the system, the upper bound of the objective function obtained by the off-line algorithm is taken as a known quantity, and a series of large asymptotically stable invariant elliptic sets are obtained by re-optimization. The state variables can be determined-lying between two ellipsoid sets within the ellipsoid set sequence, on the basis of online measured state variables. By adding an adjacent small ellipse set, the state variables are precisely located by three ellipse sets optimization, and the system control quantity is obtained. The simulation results show that this algorithm is better than the traditional algorithm. Finally, this algorithm is applied to identify parameters of silicon single crystal thermal field temperature model and the simulation results prove its excellent ability of parameters identification.

关键词： Robust optimization Model predictive control Uncertainty Online optimization

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Multi-RIS Aided 3D Secure Precise Wireless Transmission

arXiv

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arXiv 2020年

作者： Shen, Tong Cai, Wenlong Lin, Yan Zhang, Shuo Lin, Jinyong Shu, Feng Wang, Jiangzhou The School of Information and Computer Anhui Agriculture University 230036 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology 210094 China The National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing100854 China School of Information and Communication Engineering HaiNan University Hainan 570228 China The School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom

In this paper, multiple reconfigurable intelligent surfaces (RIS) aided secure precise wireless transmission (SPWT) schemes are proposed in the three-dimensional (3D) wireless communication scenario. Unavailable direct path channels from transmitter to receivers are considered when the direct paths are obstructed by obstacles. Then, multiple RISs are utilized to achieve SPWT through the reflection path among transmitter, RISs and receivers in order to enhance the communication performance and energy efficiency simultaneously. First, a maximum-signal-to-interference-and-noise ratio (MSINR) scheme is proposed in a single user scenario. Then, the multi-user scenario is considered where the illegitimate users are regarded as eavesdroppers. A maximum-secrecy-rate (MSR) scheme and a maximum-signal-to-leakage-and-noise ratio (MSLNR) are proposed. The former achieves a better secrecy rate (SR) performance but incurs a higher complexity. The latter has a lower complexity than the MSR scheme with an SR performance loss. Simulation results show that both single-user scheme and multi-user scheme can achieve SPWT which transmits confidential message precisely to location of desired users. Moreover, MSLNR scheme has a lower complexity than the MSR scheme, while the SR performance is close to that of the MSR scheme. © 2020, CC0.

关键词： Transmitters

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The Method to Recognize Linear Block Code Based on the Distribution of Code Weight 10

The Method to Recognize Linear Block Code Based on the Distr...

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10th International Conference on communication Software and Networks, ICCSN 2018

作者： Lei, Wang Yizhou, Ge Fucai, Zhao Yong, Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China Key Laboratory of Electronic Restriction Anhui Province National University of Defense Technology Hefei China

ISBN: (纸本)9781538672235

The definition and matrix description of linear cyclic code is introduced in this paper first. Then the distribution of code weight is analyzed. The distance of code weight distribution is defined according to distance of vectors. The theoretic probability of code weight of random sequence is ratiocinated. The method to estimate code weight distribution of actual sequence is established. The code length and start are estimated by the distance between the code weight distribution and the check matrix and generation matrix are estimated by Gauss Elimination. The results of simulating experiment to different length code show that this method can recognize code with middle and short code length effectively when BER is 10-3. © 2018 IEEE.

关键词： Matrix algebra

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing

Application of EMD and 1.5-dimensional spectrum in fault fea...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Jiang, Zhanglei Wu, Yapeng Li, Jun Liu, Yaru Wang, Jifang Xu, Xiaoli Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System Inner Monolia First machinery Group Co. LTD Baotou China National Secondary Occupation Education Reform and Development Demonstration School Baotou Service Management Vocational School Baotou China

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Roller bearings

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Fabry-perot lasers as enablers for parallel reservoir computing

arXiv

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arXiv 2020年

作者： Bogris, Adonis Mesaritakis, Charis Deligiannidis, Stavros Li, Pu Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo Athens12243 Greece Department of Information & Communication Systems Engineering University of the Aegean 2 Palama & Gorgyras St. Karlovassi Samos83200 Greece Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of nodes increases and the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as to enhance classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Publisher Correction: Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China

引用

Nature communications 2022年第1期13卷 1403页

作者： Yang Yang Lili Ren Mingxuan Wu Hailong Wang Fengfei Song L Ruby Leung Xin Hao Jiandong Li Lei Chen Huimin Li Liangying Zeng Yang Zhou Pinya Wang Hong Liao Jing Wang Zhen-Qiang Zhou Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing Jiangsu China. yang.yang@***. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing Jiangsu China. Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USA. Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USA. hailong.wang@pnnl.gov. Frontier Science Centre for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory Ocean University of China Qingdao China. Qingdao National Laboratory for Marine Science and Technology (QNLM) Qingdao China. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University for Information Science and Technology Nanjing Jiangsu China. Tianjin Key Laboratory for Oceanic Meteorology Tianjin Institute of Meteorological Science Tianjin China. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan University Shanghai China.

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