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arXiv 2022年

作者： Zhu, Yao Chen, Yuefeng Li, Xiaodan Chen, Kejiang He, Yuan Tian, Xiang Zheng, Bolun Chen, Yaowu Huang, Qingming The Zhejiang University Hangzhou310013 China The Security Department of Alibaba Group China The CAS Key Laboratory of Electro-Magnetic Space Information University of Science and Technology of China China The Hangzhou Dianzi University China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies China The Zhejiang University Zhejiang Provincial Key Laboratory for Network Multimedia Technologies China The Zhejiang University Zhejiang University Embedded System Engineering Research Center Ministry of Education of China China The University of Chinese Academy of Sciences China The Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases. In summary, our work provides new in

关键词： Deep neural networks

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

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arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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A multi-layer network topology visualization layout based on Louvain community detection 3

A multi-layer network topology visualization layout based on...

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3rd IEEE International Conference on Data science in Cyberspace, DSC 2018

作者： Zhang, Xitao Wu, Lingda Yao, Zhonghua Yu, Shaobo Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781538642108

Community is one of the most important structure features of complex networks. It has important research significance for topological structure analysis, behavior prediction, cascading failure, percolation and spreading of multi-layer networks. Aiming at the problem that the existing multi-layer network visualization can't clearly show the community structure, a multi-layer network topology visualization layout based on Louvain community detection is proposed. Firstly, a complex network community discovery algorithm (Louvain) is used to perform the multi-layer network nodes at each layer and the community affiliation of the nodes is obtained at each layer;then, based on the traditional force-directed layout, the community force is added to the internal nodes of the community, so that all the nodes that belong to the same community are gathered to the community center, so as to achieve the effect of displaying the network community structure. The simulation results show that the algorithm has the characteristics of faster convergence and clearer community structure than the traditional visual layout algorithm. © 2018 IEEE.

关键词： Network topology

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Reasearch on dynamic evolution model visualization of satellite communication network based on complex network 3

Reasearch on dynamic evolution model visualization of satell...

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3rd IEEE International Conference on Data science in Cyberspace, DSC 2018

作者： Liu, Chengxiang Xiong, Wei Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781538642108

In order to explore the dynamic evolution of satellite communication network, this paper carried out a visualization study on the dynamic evolution model of satellite communication network based on complex network theory. By constructing reasonable visual mission view and status view, the paper analyzes the properties of satellite communication network and also uses complex network theory to improve the modeling. Through the rule design and algorithm derivation of dynamic evolution model, it analyzes the detailed steps of the dynamic evolution of satellite communication network. By the experiments, it is proved that the dynamic evolution visualization model of satellite communication network conforms to the power law distribution of complex network, which lays a solid foundation for the follow-up study of satellite communication network. © 2018 IEEE.

关键词： Complex networks

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Convolutional neural networks based for high spectral dimensional data classification 3

Convolutional neural networks based for high spectral dimens...

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3rd IEEE International Conference on Data science in Cyberspace, DSC 2018

作者： Zhu, Jiang Wu, Lingda Lu, Yi Jiao, Jiao Song, Xiaorui Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781538642108

Recent research has shown that deep learning is widely used to classify the high spectral dimensional data. Under limited samples, a novel method of high spectral dimensional data classification based on convolutional neural networks (CNNs) is proposed. By standardization and adding virtual samples, the sensitivity of models on the classification of objects and quantity of classification objects is reduced. The computational complexity and computation time are reduced by optimizing the gradient descent method and the learning rate calculation method. By setting up a suitable network structure, the generalization is improved. The experimental results show that compared with the traditional classification methods such as k-Nearest Neighbor (KNN), Logistic Regression (LR) and Support Vector Machine of Radial Basis Function Kernel (RBF-SVM), the method using convolutional neural network has more competitive results in computational efficiency and classification accuracy. © 2018 IEEE.

关键词： Gradient methods

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Automatic detection of Cyanobacterial blooms using multi-source optical satellite imagery: method development and application

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Ecological Indicators 2025年 176卷

作者： Hailong Zhang Chen Lu Deyong Sun Xiaomin Ye Shengqiang Wang Quan Qin State Key Laboratory of Climate System Prediction and Risk Management Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Coastal Zone Exploitation and Protection Ministry of Natural Resources Nanjing 210017 China School of Marine Sciences Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources Beijing 100081 China Shandong Provincial Climate Center Jinan 250031 China

Cyanobacterial blooms (CyanoBloom) are a widespread environmental concern in lakes, and optical satellite imagery has been widely used to monitor their spatiotemporal dynamics. Many spectral-based CyanoBloom detection methods rely on Rayleigh- or atmosphere-corrected surface reflectance. However, these methods require ancillary atmospheric data and preprocessing procedures, thereby limiting their operational efficiency and scalability. The potential of multi-source satellite imagery remains underexploited due to the lake of robust methods for automatic CyanoBloom detection across various satellite platforms. To address this challenge, we proposed a novel automatic CyanoBloom detection (ACD) method that directly utilizes satellite top-of-atmosphere reflectance ( R TOA ) data. Cross-index and cross-sensor analyses confirmed the high detection accuracy and robustness of the ACD method against atmospheric and observational variations. The method was successfully applied to various inland lakes, including Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun in China, Lake Okeechobee in the United States, and Lough Neagh in Northern Ireland. Requiring only for four spectral bands (blue, green, red, and near-infrared), the ACD method was compatible with various optical sensors, including HY1C/D-CZI, GF-WFV, GF4-PMS, HJ-CCD, Sentinel2-MSI, Landsat9-OLI, and Terra-MODIS. This study presents a novel automatic and rapid approach for detecting CyanoBloom in lakes, providing valuable technical support for water quality monitoring and bloom management.

关键词： Cyanobacterial bloom Automatic detecting Satellite R TOA Optical satellite imagery

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Performance analysis of moving ship target signal integration and detection in geosynchronous synthetic aperture radar

Performance analysis of moving ship target signal integratio...

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IET International Radar Conference 2018, IRC 2018

作者： Zhang, Ying Dong, Xichao Xiong, Wei Hu, Cheng Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Ministry of Education Beijing100081 China

Geosynchronous synthetic aperture radar (GEO SAR) has the characteristics of wide coverage and short revisit time, which makes GEO SAR moving target indication more valuable than other platforms. This article analyses the influence of moving target on 'Stop-and-Go' error in GEO SAR, and then the accurate slant range model of moving target is constructed. The signal-to-noise ratio of the raw data echoes for moving ship target is very low because of the high satellite orbit altitude, so it cannot be detected by constant false alarm detection. Based on the accurate slant range model of moving target in GEO SAR, three kinds of integration methods are reconstructed, including moving target detection, Radon transform and generalised Radon Fourier transform (GRFT). The performance of these methods is compared through numerical simulation. GRFT can provide the best detection performance in a reasonable integration time. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Numerical methods

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Research on obstacle detection and path planning based on visual navigation for mobile robot

Research on obstacle detection and path planning based on vi...

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作者： Yixiao Zheng Bixi Yan Chao Ma Xinyu Wang Haoyang Xue School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering

Both obstacle and path planning are of significance for autonomous visual navigation of a mobile robot which is able to realize free movement and obstacle *** this paper,a scheme of obstacle detection which is based on binocular vision to identify obstacles is constructed and it is combined with improved A* algorithm for obstacle avoidance motion for mobile robot ***,a binocular vision system to detect the robot's perception of the environment is used and the visual information is *** a Gaussian filter is used to reduce the noise in the image,and the grayscale image is obtained through the binocular vision *** gray scale map is matched with SAD(Sum of Absolute Differences) in order to obtain the disparity image with ***,the location information of the obstacle is extracted through the environmental depth information contained in the disparity image,and the improved A* algorithm is used as the path planning algorithm in this paper,which can modify the path of the planning ***,the detection experiment of the robot visual obstacle avoidance system was established and the optimal path was *** examples show that,the obstacle avoidance system can complete the task of obstacle detection and give a better path for the mobile robot.

关键词： image filter Research on obstacle detection and path planning based on visual navigation for mobile robot

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Status and Prospects of the LHCf and RHICf experiments 37

Status and Prospects of the LHCf and RHICf experiments

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37th International Cosmic Ray Conference, ICRC 2021

作者： Menjo, Hiroaki Adriani, O. Berti, E. Bonechi, L. Bongi, M. D’Alessandro, R. Castellini, G. Haguenauer, M. Itow, Y. Kasahara, K. Kondo, M. Matsubara, Y. Menjo, H. Muraki, Y. Ohashi, K. Papini, P. Ricciarini, S. Sako, T. Sakurai, N. Sato, K. Shimizu, Y. Tamura, T. Tiberio, A. Torii, S. Tricomi, A. Turner, W.C. Ueno, M. Yoshida, K. Adriani, O. Berti, E. Goto, Y. Hong, B. Itow, Y. Kasahara, K. Kim, M.H. Menjo, H. Nakagawa, I. Sako, T. Sakurai, N. Sato, K. Seidl, R. Tanida, K. Torii, S. Tricomi, A. Institute for Space-Earth Environmental Research Nagoya University Aichi Nagoya464-8601 Japan INFN Section of Florence Florence Italy University of Florence Florence Italy IFAC-CNR Florence Italy Ecole-Polytechnique Palaiseau France Institute for Space-Earth Environmental Research Nagoya University Nagoya Japan Kobayashi-Maskawa Institute for the Origin of Particles the Universe Nagoya University Nagoya Japan Faculty of System Engineering Shibaura Institute of Technology Saitama Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa Japan Tokushima University Tokushima Japan Kanagawa University Kanagawa Japan RISE Waseda University Shinjuku Tokyo Japan INFN Section of Catania Catania Italy University of Catania Catania Italy CSFNSM Catania Italy LBNL BerkeleyCA United States RIKEN Nishina Center for Accelerator-Based Science Wako Japan RIKEN BNL Research Center Brookhaven National Laboratory Upton United States Korea University Seoul Korea Republic of Shibaura Institue of Technology Saitama Japan Advanced Science Research Center Japan Atomic Energy Agency Tokai-mura Japan

Precise understanding of hadronic interactions at high energies is a key to improve mass composition measurements of very high energy cosmic-rays and to solve the muon excess issue observed in high energy cosmic-ray experiments using an air-shower technique. The LHCf and RHICf experiments measures the differential production cross sections of very forward neutral particle as photons, neutral pions and neutrons at LHC and RHIC, respectively. These data are critically important to test and tune hadronic interaction models used for air-shower simulations. In this presentation, we introduce the recent results of both the experiments as well as our future operation plans. LHCf published an updated result of forward neutron measurement at pp, √s = 13 TeV. From the observed neutron energy spectra, we also obtained the average inelasticity, which is one of the key parameters for air shower development, as 0.536 +0.031-0.037. In addition, several analysis are on-going;neutral pion measurement at pp, √s = 13 TeV, central- forward correlation analysis with LHCf+ATLAS, photon measurement by RHICf. LHCf plans to have operations at pp and pO during the LHC-Run3 period. At pp collisions, new silicon readout system will be introduced to improve the read-out speed, and 10 times more statistics of the previous operation in 2015 will be obtained. Thanks to high statistics, rare particles such as η, Ks0 and Λ will be addressed also. We also plan another operation at RHIC in 2024 with a new detector. The detector, a calorimeter composed of tungsten, Si pad and pixel layers, will have a much wider acceptance and higher sensitivity of Ks0 measurement than the current detector. © Copyright owned by the author(s).

关键词： Cosmology

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THE TROPICS

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Bulletin of the American Meteorological Society 2024年第8期105卷 S214-S276页

作者： Diamond, H.J. Schreck, C.J. NOAA/OAR Air Resources Laboratory College Park Maryland Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville North Carolina NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Rosenstiel School of Marine and Atmospheric Science University of Miami Miami Florida NOAA/NWS National Hurricane Center Miami Florida NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory Miami Florida Lamont-Doherty Earth Observatory Columbia University Palisades New York Department of Geography Arizona State University Tempe Arizona Institute for Climate and Application Research (ICAR)/KLME/ILCEC/CIC-FEMD Nanjing University of Information Science and Technology Nanjing China Centro de Previsão do Tempo e Estudos Climáticos/National Institute for Space Research Center for Weather Forecasts and Climate Studies Cachoeira Paulista Brazil Bureau of Meteorology Perth Australia National Institute of Water and Atmospheric Research Ltd. Auckland New Zealand Canadian Hurricane Centre Dartmouth Canada Earth System Modeling Center Nanjing University of Information Science and Technology Nanjing China Department of Atmospheric Science Colorado State University Fort Collins Colorado NOAA/NESDIS Center for Satellite Applications and Research Fort Collins Colorado National Taiwan University Taipei Taiwan Institute for Climate and Application Research Nanjing University of Information Science and Technology Nanjing China Centre for Water Climate and Land School of Environmental and Life Sciences University of Newcastle Callaghan Australia Greater Wellington Regional Council Wellington New Zealand Bureau of Meteorology Melbourne Australia WeatherTiger Tallahassee Florida School of Ocean and Earth Science and Technology Department of Meteorology University of Hawaii at Manoa Honolulu Hawaii International Pacific Research Center Honolulu Hawaii Department of Hydrology and Atmospheric Sciences

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