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arXiv 2019年

作者： Tchaye-Kondi, Jude Zhai, Yanlong Lin, Kwei-Jay Zhu, Liehuang Tao, Wenjun Yang, Kai School of Computer Science Beijing Institute of Technology Beijing100081 China Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

Storing and processing massive small files is one of the major challenges for the Hadoop Distributed File system (HDFS). In order to provide fast data access, the NameNode (NN) in HDFS maintains the metadata of all files in its main-memory. Hadoop performs well with a small number of large files that require relatively little metadata in the NN’s memory. But for a large number of small files, Hadoop has problems such as NN memory overload caused by the huge metadata size of these small files. We present a new type of archive file, Hadoop Perfect File (HPF), to solve HDFS’s small files problem by merging small files into a large file on HDFS. Existing archive files offer limited functionality and have poor performance when accessing a file in the merged file due to the fact that during metadata lookup it is necessary to read and process the entire index file(s). In contrast, HPF file can directly access the metadata of a particular file from its index file without having to process it entirely. The HPF index system uses two hash functions: file’s metadata are distributed through index files by using a dynamic hash function and, for each index file, we build an order preserving perfect hash function that preserves the position of each file’s metadata in the index file. The HPF design will only read the part of the index file that contains the metadata of the searched file during its access. HPF file also supports the file appending functionality after its creation. Our experiments show that HPF can be more than 40% faster file’s access from the original HDFS. If we don’t consider the caching effect, HPF’s file access is around 179% faster than MapFile and 11294% faster than HAR file. If we consider caching effect, HPF is around 35% faster than MapFile and 105% faster than HAR file. Copyright © 2019, The Authors. All rights reserved.

关键词： File organization

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Radiative forcing reduced by early twenty-first century increase in land albedo

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Nature 2025年第8065期641卷 1162-1171页

作者： Zhengyang Hou Liqiang Zhang Zhiqiang Xiao Ying Qu Duoying Ji Zidong Zhu Xin Yao Shuwen Peng Hao Geng Qihao Wang Suhong Liu Lanpu Zhao Zhaocong Wu Liangpei Zhang Jingjing Peng Giovanni Forzieri Aolin Jia Jintai Lin Wenjie Fan Chenghu Zhou State Key Laboratory of Remote Sensing and Digital Earth Faculty of Geographical Science Beijing Normal University Beijing China Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies (CISESS) University of Maryland College Park MD USA Department of Civil and Environmental Engineering University of Florence Florence Italy Remote Sensing and Natural Resource Modeling Department ERIN Luxembourg Institute of Science and Technology Belvaux Luxembourg Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic Sciences School of Physics Peking University Beijing China Institute of Carbon Neutrality Peking University Beijing China School of Earth and Space Sciences Peking University Beijing China State Key Laboratory of Resources and Environmental Information System Institute of Geographical Science and Natural Resources Chinese Academy of Sciences Beijing China

Surface albedo greatly affects how much energy the Earth absorbs. Intensive human activities and accelerated climate change have altered surface albedo across spatial and temporal scales1,2,3, yet assessments of the effects of land use or land cover (LULC) and snow variations on land surface albedo are scarce at the global scale. As a result, the global land surface albedo dynamics over recent decades and their corresponding radiative forcing to the climate system remain poorly understood4,5,6,7,8,9. Here we quantify the individual and combined effects of snow cover dynamics, LULC conversions and non-conversion regions on albedo variations during 2001–2020 and estimate their induced radiative forcing. We show that the negative radiative forcing induced by the global land surface albedo change was −0.142 (−0.158, −0.114) W m−2over the past two decades. The global snow-free land surface albedo increased by 2.2% (P< 0.001), with a negative radiative forcing of −0.164 (−0.186, −0.138) W m−2(P< 0.001). The magnitude of this negative forcing is sevenfold larger than the positive forcing induced by snow dynamics, and equivalent to 59.9% of that caused by CO2emissions from 2011 to 201910. The global radiative forcing due to albedo changes in LULC non-conversion regions is 3.9 to 8.1 times greater than that from LULC conversions. The radiative forcing induced by albedo changes highlights the important role of land surface dynamics in modulating global warming.

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The Research of Low Earth Orbit Prediction of Satellite Based on Deep Neural Network

The Research of Low Earth Orbit Prediction of Satellite Base...

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2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering(CMEE 2017)

作者： Hai-tao YANG Jun-peng ZHU Jian ZHANG Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Department of Graduate Management Space Engineering University

Satellite orbit prediction is a basic requirement in satellite applications. The current orbit prediction mainly depends on the dynamic model. Because of the limitations of the detection equipment and the satellite orbit data cannot be updated in time, which cause the dynamical model long-term orbit divergence to be serious. Using deep neural network as a method of orbit prediction which can predict the future data by training the satellite orbit data and grasp the implicit relationship between the data. The neural network model is optimized and the prediction data is compared with the actual data. The error of 20 days forecast is reduced to 2 km, which improves the accuracy of neural network forecasting satellite orbit.

关键词： Satellite orbit prediction Deep neural network

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MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entity and Relation Extraction

MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entit...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Da Peng Zhongmin Pei Delin Mo Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China Institute of War Studies Academy of Military Sciences

Joint entity and relation extraction has achieved impressive advances in NLP, such as document understanding and knowledge graph construction. The typical methods for entity and relation extraction typically break down the joint task into several smaller components or stages for ease of implementation, but this leads to a loss of the interconnected knowledge in the triple. Hence, we propose to model the triple in one module jointly. Furthermore, the labeling of a joint entity and relation extraction tasks is costly and domain-specific; therefore, it is important to improve its performance on low-resource data and domain adaption. To address this issue, we suggest using two sources that are rich in information, namely pre-trained models on large data and multi-domain text corpora. Pretraining allows us to provide the model with the fundamental ability to perform joint entity and relationship extraction. Second, through meta-learning on multi-domain text, we can improve the model's generalization capabilities, enabling it to perform well even with limited data. We present MTL-JER, a Meta-Transfer Learning method for Joint Entity and Relation Extraction in low-resource settings in this paper. Using exhaustive experiments on five datasets, we prove that our model obtains optimal results.

关键词： Adaptation models Training data Knowledge graphs Artificial neural networks Data models Data mining Labeling

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3D state decomposition modeling method for MBSE in space engineering

3D state decomposition modeling method for MBSE in space eng...

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AIAA space Conference and Exposition 2012

作者： Zhang, Hao Gerndt, Andreas Liu, Bo Simulation and Software Technology Lilienthalplatz 7 Braunschweig 38108 Germany Center for Space Science and Applied Research Chinese Academy of Sciences Beijing 100190 China Graduate School of Chinese Academy of Sciences Beijing 100190 China Center for Space Science and Applied Research Chinese Academy of Sciences NO.1 Nanertiao Zhongguancun Haidian district Beijing 100190 China

ISBN: (数字)9781600869402

ISBN: (纸本)9781600869402

The increasing number of space missions and the market environment make it mandatory that the design process and design models have to be reused in future space missions. As a solution to such a reuse problem, Model-Based systems Engineering (MBSE) is highly recommended. Since constructing a model is the first step to reach MBSE, a 3D state decomposition modeling method is proposed in this paper. Starting from Technical Requirements (TRs), the method maps them to state variables and defines states as well as relations along three red lines of space engineering: internal subsystem, inter-subsystems, and space mission lifecycle. Eventually, it yields decomposition of a space system into a hierarchical three-dimensional model of states. Relying on the Eclipse Modeling Framework (EMF), the model can be described and stored in a standardized format, which allows the reuse of once defined models in subsequent space missions. This study leads to a novel MBSE solution to implement specification, analysis, design, and operation of space mission systems in a unified framework. © 2012 by Hao Zhang, Andreas Gerndt, Bo Liu. Published by the American Institute of Aeronautics and Astronautics, Inc.

关键词： systems engineering

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Integrated Navigation Based on Pulsar in Libration Point Orbit

Integrated Navigation Based on Pulsar in Libration Point Orb...

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2014中国制导、导航与控制学术会议

作者： Chengwei Yang Jianhua Zheng Mingtao Li Dong Gao Space Information and Simulation Laboratory Center for Space Science and Applied Research of Chinese Academy of SciencesBeijing100191 China

ISBN: (纸本)9781479946983

Libration point orbit is playing more and more significant role in deep space exploration.X-ray pulsar-based navigation (XNAV) is a novel and promising autonomous navigation ***,there are only a few research papers concerning how to employ XNAV in libration orbit,especially the application of integrated navigation based on *** libration orbit is *** is a new research topic for us to delve *** improve the autonomous navigation performance in halo orbit,an integrated navigation method was *** dynamic model of halo orbit was ***-level differential correction method was *** measurement models of the XNAV and the ultraviolet sensor were *** federated unscented kalman filter based on UD factorization was adopted to estimate the state of the *** clock error correction was included in the *** simulation results show that the proposed integrated navigation is feasible in halo orbit of Earth-Moon system,and can provide better performance in comparison with XNAV or ultraviolet sensor-based *** only can the integrated navigation obtain a highly accurate spacecraft position,but also it can restrain clock drift.

关键词： aerospace instrumentation celestial mechanics space vehicle navigation Earth-Moon system UD factorization X-ray pulsar-based navigation XNAV measurement models autonomous navigation method autonomous navigation performance clock drift deep space exploration federated unscented kalman filter halo orbit halo orbit dynamic model integrated navigation integrated navigation method libration point orbit spacecraft position two-level differential correction method ultraviolet sensor-based navigation Amplitude modulation Clocks Moon Navigation Numerical models Orbits space vehicles

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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A Position Probability Prediction Method of Marine Moving Targets for Optimization Search by Satellite

A Position Probability Prediction Method of Marine Moving Ta...

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IEEE International Conference on Control science and systems Engineering (CCSSE)

作者： Xiao Zheng Xiaodong Peng Zhiyuan Guo Huimin Qiang Junbao Zhao Laboratory of System Simulation and Concurrent Design Technology (National Space Science Center Chinese Academy of Sciences) University of Chinese Academy of Science Beijing China Beijing Remote Sensing Information Institute Beijing China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

Marine moving ship target search has great application demand and development prospects in marine surveillance, marine search and rescue, customs anti-smuggling, and other aspects. Satellites are a vital means of searching for marine moving targets. Ground coverage for imaging satellites is relatively limited; the vast marine area makes searching for marine moving ships challenging. Target position prediction, used as the guidance information for satellite detection to reduce the uncertainty of the target position and improve search efficiency, is an essential approach to achieving target optimization search. This paper proposes a target position prediction method based on joint learning of multi-source factors for optimal satellite search of marine moving targets. Firstly, it analyzes the characteristics of marine moving targets and the factors affecting motion. Then, it puts forward the motion prediction model based on a deep neural network, integrating multifactor, like the state of the moving target, environmental attributes such as nautical charts, meteorology and hydrology, and kinematic constraints, to predict the target positional distribution probability. Finally, the target search strategy based on the maximum sum of the distribution probabilities of the satellite coverage area is adopted to improve target detection probability. The experiment results show that the prediction method can realize the target positional distribution probability prediction, reduce the uncertainty search when making the satellite search plan, and improve efficiency.

关键词： Satellites Uncertainty Object detection Kinematics Predictive models Search problems Satellite navigation systems Marine vehicles Modeling Optimization

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Driving factors of carbon dioxide emissions in China： an empirical study using 2006-2010 provincial data

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Frontiers of Earth science 2017年第1期11卷 156-161页

作者： Yu LIU Zhan-Ming CHEN Hongwei XIAO Wei YANG Danhe LIU Bin CHEN Institute of Policy and Management Chinese Academy of Sciences Beijing 100190 China Department of Energy Economics School of Economics Renmin University of China Beijing 100872 China National Academy of Development and Strategy Renmin University of China Beijing 100872 China Economic Forecasting Department State Information Center Beijing 100045 China School of Economics and Management Beijing University of Chemical Technology Beijing 100029 China School of Humanities and Social Science Beijing Institute of Technology Beijing 100081 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China

The rapid urbanization of China has increased pressure on its environmental and ecological well being. In this study, the temporal and spatial profiles of China＇s carbon dioxide emissions are analyzed by taking heterogeneities into account based on an integration of the extended stochastic impacts using a geographically and temporally weighted regression model on population, affluence, and technology. Population size, urbanization rate, GDP per capita, energy intensity, industrial structure, energy consumption pattern, energy prices, and economy openness are identified as the key driving factors of regional carbon dioxide emissions and examined through the empirical data for 30 provinces during 2006-2010. The results show the driving factors and their spillover effects have distinct spatial and temporal heterogeneities. Most of the estimated time and space coefficients are consistent with expectation. According to the results of this study, the heterogeneous spatial and temporal effects should be taken into account when designing policies to achieve the goals of carbon dioxide emissions reduction in different regions.

关键词： carbon dioxide emission heterogeneity space spillover

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Research on knowledge network modelling for aero-craft system design 7

Research on knowledge network modelling for aero-craft syste...

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7th International Conference on simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017

作者： Zhai, Xiang Dai, Feng Qu, Huiyang Zhong, Lingling Du, Chenyong Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Aviation Engineering Institute Civil Aviation Flight University of China Sichuan China Beijing ZhiYuanChuangTong IT Co Ltd Beijing China

ISBN: (纸本)9789897582653

A great amount of existing knowledge is required during the development of aero-craft system. At present, the existing knowledge organization model construct different knowledge model for different stages is difficult to adapt to the complicated product life cycle application. This study proposes a product development oriented knowledge network model focusing on expressing the knowledge demands and connecting the computer-aided system. Under the overall knowledge network model, this study describes the ontology description method of spacecraft product model, development tasks and aerospace terminology. Recommending missile aerodynamic knowledge within the design stage is presented as a case study, and the framework and the method is proved to be effective. © Copyright 2017 by SCITEPRESS - science and technology Publications, Lda. All rights reserved.

关键词： Ontology

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