检索结果-内蒙古大学图书馆

The status of carbon neutrality of the world’s top 5 CO_(2) emitters as seen by carbon satellites

学校读者我要写书评

暂无评论

Fundamental Research 2022年第3期2卷 357-366页

作者： Fei Jian Wei He Weimin Ju Hengmao Wang Mousong Wu Jun Wang Shuzhuang Feng Lingyu Zhang Jing MChen Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Geography University of TorontoTorontoOntario M5S3G3Canada Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

China,the Unite States(US),the European Union(EU),India,and Russia are the world’s top 5 fossil fuel and cement CO_(2)(FFC)emitting countries or regions(CRs).It is very important to understand their status of carbon neutrality,and to monitor their future changes of net carbon fluxes(NCFs).In this study,we implemented a well-established global carbon assimilation system(GCAS,Version 2)to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO_(2)*** reductions of flux uncertainty and XCO_(2)bias,and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 *** results suggest that Russia has achieved carbon neutrality,but the other 4 are still far from being carbon neutral,especially *** mean annual NCFs in China,the US,the EU,India,and Russia are 2.33±0.29,0.82±0.20,0.42±0.16,0.50±0.12,and-0.33±0.23 PgC yr^(−1),*** 2010 to 2019,the NCFs showed an increasing trend in the US and India,a slight downward trend after 2013 in China,and were stable in the *** changes of land sinks in China and the US might be the main reason for their ***’s trend was mainly due to the increase of FFC *** relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased,while those of the US and India have increased,implying the US and India must take more active measures to control carbon emissions or increase their *** study indicates that satellite XCO_(2)could be successfully used to monitor the changes of regional NCFs,which is of great significance for major countries to achieve greenhouse gas control goals.

关键词： Net carbon flux GOSAT OCO-2 XCO_(2) Atmospheric inversion

CODC-S: A quality-controlled global ocean salinity profiles dataset

学校读者我要写书评

暂无评论

Scientific data 2025年第1期12卷 917页

作者： Zhetao Tan Yujing Zhu Lijing Cheng Viktor Gouretski Yuying Pan Huifeng Yuan Zhankun Wang Guancheng Li Xinyi Song Bin Zhang Senliang Bao Yuanlong Li Jiang Zhu State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China. Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace Ecole Normale Supérieure - Université PSL Paris 75231 France. University of Chinese Academy of Sciences Beijing 100049 China. State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China. chenglij@***. University of Chinese Academy of Sciences Beijing 100049 China. chenglij@***. Computer Network Information Center Chinese Academy of Sciences Beijing 100039 China. NOAA National Centers for Environmental Information Silver Spring MD 20910 USA. Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment Guangzhou 510611 China. Institute of Oceanology Chinese Academy of Sciences Qingdao 266071 China. Oceanographic Data Center Chinese Academy of Sciences Qingdao 266071 China. College of Meteorology and Oceanography National University of Defense Technology Changsha 410073 China.

Changes in the global ocean salinity reflect the evolution of the global hydrological cycle. These secular changes are assessed using seawater salinity profiles obtained during the past ~80 years. Here, we introduce a new global ocean salinity profiles database named CODC-S (the Chinese Academy of Science (CAS) Oceanography Data Center - Salinity component), which encompasses over 11 million in-situ salinity profiles from 1940 to 2023 obtained by means of several instrument types. These salinity profiles are quality-controlled (QC-ed) using a new automated salinity quality control system named CODC-QC-S (the CODC Quality Control system - Salinity component), consisting of 11 distinct quality checks. By applying time-varying, flow-dependent, and topographical-dependent 0.5% and 99.5% quantile thresholds, the CODC-QC-S defines local climatology salinity ranges without the assumption of Gaussian distribution. The CODC-S database, together with the newly proposed QC algorithm, has undergone extensive evaluations, including comparisons with the benchmark data and climatology, as well as analyses of global and basin-scale long-term salinity changes before and after QC. These validations demonstrate that the quality of salinity data in the CODC-S is time-, depth-, region- and instrument type-dependent. The eight-decade quality-homogeneous salinity profiles from the CODC-S database can support diverse oceanographic and climatic research such as monitoring water cycle changes and freshwater/overturning transports.

关键词：

Dynamic evolution of spring sand and dust storms and cross-border response in Mongolian plateau from 2000 to 2021

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2023年第1期16卷 2341-2355页

作者： Yu Zhang Juanle Wang Altansukh Ochir Sonomdagva Chonokhuu Chuluun Togtokh State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China College of Geoscience and Surveying Engineering China University of Mining&Technology(Beijing)BeijingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China Environmental Engineering Laboratory Department of Environment and Forest EngineeringSchool of Engineering and Applied Sciences and Institute for Sustainable DevelopmentNational University of MongoliaUlaanbaatarMongolia Department of Environment and Forest Engineering National University of MongoliaUlaanbaatar CityMongolia Institute for Sustainable Development National University of MongoliaUlaanbaatarMongolia

According to the United Nations Sustainable Development Goals(SDG 15.3),frequent sand and dust storms(SDSs)in the spring are a long-term challenge to the prevention and control of land degradation on the Mongolian *** this study,MODIS remote sensing data are used to monitor and analyse SDS events on the Mongolian *** annual distribution of spring SDSs(March to May)from 2000 to 2021 are obtained based on the dust storm detection *** overall classification accuracy is 85.24%and the kappa coefficient is *** show a decrease in the overall frequency of SDS events,where storm events in 2000–2010 are significantly higher than those in the second *** cross-border regions between China and Mongolia appear to be SDS intensity centers,particularly those in southern *** exhibits a strong negative correlation with the area affected by SDS,and the correlation coefficient is–*** increase in barren and sand contributes primarily to the increase in SDS,whereas wind prevention and sand control projects undertaken by the Mongolian and Chinese governments promote regional *** pertaining to cross-board sandstorm responses on the Mongolian Plateau are recommended.

关键词： Mongolian plateau dust storm dust index land degradation MODIS

Azimuth Ambiguity Suppression for Sparse SAR Imaging Based on Unfolded Deep Network 15

学校读者我要写书评

暂无评论

Azimuth Ambiguity Suppression for Sparse SAR Imaging Based o...

15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Wu, Yuwei Zhang, Zhe Song, Ruizhen Qiu, Xiaolan Yu, Weidong Department of Space Microwave Remote Sensing System Beijing100090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783800762873

Sparse synthetic aperture radar (SAR) imaging allows for a reduction in the azimuth sampling rate based on compressive sensing theory. However, it relies on the sparsity of scenes, which can lead to significant azimuth ambiguity. To address this issue, we introduce a novel network designed to suppress azimuth ambiguity for sparse SAR imaging. Our proposed deep unfolded network is based on the Iterative Thresholding Algorithm Learned (LISTA). We replace the observation matrix with Matched Filter (MF) operators to reduce computational complexity. Experiments with real SAR scene data demonstrate the effectiveness and superiority of our approach in suppressing azimuth ambiguity. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Matched filters

Towards Hybrid-Optimization Video Coding

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Huo, Shuai Liu, Dong Li, Li Ma, Siwei Wu, Feng Gao, Wen The Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Institute of Digital Media Peking University Beijing100871 China

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem in practice, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid video coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. The discrete solution provides multiple starting points (i.e. modes) in global optimization space and then searches for the best one among them. However, the search-based optimization algorithm is not efficient enough. On the other hand, end-to-end learned video coding represents the continuous optimization solution because the optimization algorithm of deep learning, gradient descent, is based on a continuous function. The continuous solution optimizes a group of model parameters efficiently by such a numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose a hybrid of discrete and continuous optimization video coding. We regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments to verify the efficiency of our hybrid optimization. Compared to the continuous opti

关键词： Deep neural networks

A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Li, Xin Jin, Xin Fu, Jun Yu, Xiaoyuan Tong, Bei Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Eastern Institute for Advanced Study China Huawei Cloud China

Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on real-world image super-resolution (RealSR). Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples. However, these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images. To overcome this problem, we are the first to have a close look at the under-explored direction for RealSR, i.e., few-shot real-world image super-resolution, which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs. Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i.e., synthetic distortions) to the target RealSR under the guidance of distortion relation. Concretely, DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion (i.e., real distortions in RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions. In this way, DRTL could quickly learn the most relevant knowledge from the synthetic distortions for the target distortion. We instantiate DRTL with two commonly-used transfer learning paradigms, including pre-training and meta-learning pipelines, to realize a distortion relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

学校读者我要写书评

暂无评论

Phase Transition Analysis for Compressive Sensing Based DoA ...

International Radar Symposium (IRS)

作者： Yan Zhang Silin Gao Mingqian Liu Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Processing Application System Beijing China

ISBN: (数字)9788394942151

ISBN: (纸本)9781728157863

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.

关键词： Direction-of-arrival estimation Estimation Radar applications Compressed sensing Signal resolution Signal to noise ratio Automotive engineering

One-shot Generative Domain Adaptation in 3D GANs

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Ziqiang Wu, Yi Wang, Chaoyue Rui, Xue Li, Bin Nanjing University of Information Science and Technology Nanjing China University of Science and Technology of China Hefei China University of Sydney Australia CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://***/iceli1007/3D-Adapter. © 2024, CC BY.

关键词： Generative adversarial networks

DeepEraser: Deep Iterative Context Mining for Generic Text Eraser

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Feng, Hao Wang, Wendi Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China The University of Adelaide Australian Institute for Machine Learning Australia

In this work, we present DeepEraser, an effective deep network for generic text removal. DeepEraser utilizes a recurrent architecture that erases the text in an image via iterative operations. Our idea comes from the process of erasing pencil script, where the text area designated for removal is subject to continuous monitoring and the text is attenuated progressively, ensuring a thorough and clean erasure. Technically, at each iteration, an innovative erasing module is deployed, which not only explicitly aggregates the previous erasing progress but also mines additional semantic context to erase the target text. Through iterative refinements, the text regions are progressively replaced with more appropriate content and finally converge to a relatively accurate status. Furthermore, a custom mask generation strategy is introduced to improve the capability of DeepEraser for adaptive text removal, as opposed to indiscriminately removing all the text in an image. Our DeepEraser is notably compact with only 1.4M parameters and trained in an end-to-end manner. To verify its effectiveness, extensive experiments are conducted on several prevalent benchmarks, including SCUT-Syn, SCUT-EnsText, and Oxford Synthetic text dataset. The quantitative and qualitative results demonstrate the effectiveness of our DeepEraser over the state-of-the-art methods, as well as its strong generalization ability in custom mask text removal. The codes and pre-trained models are available at https://***/fh2019ustc/DeepEraser Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods