检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Shen, Huanfeng Jiang, Yun Li, Tongwen Cheng, Qing Zeng, Chao Zhang, Liangpei School of Resource and Environmental Sciences Wuhan University Wuhan430079 China School of Urban Design Wuhan University Wuhan430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan430079 China

Air temperature(Ta)isanessential climatologicalcomponentthatcontrolsand influences variousearth surface *** this study, we makethe first attempttoemploy deep learning for Ta mapping mainly based on space remote sensing and ground station *** that Ta varies greatly in space and time and is sensitive tomany factors, assimilation dataand socioeconomicdata are also included for a multi-source data fusion based ***, a5-layers structured deep belief network(DBN) isemployed to better capturethe complicatedand non-linear relationships between Taand different ***-wise pre-trainingprocess foressentialfeaturesextraction and fine-tuningprocess forweight parametersoptimization ensure the robust prediction of Ta spatio-temporal distribution. The DBN model was implemented for 0.01°daily maximum Ta mapping across China. The ten-fold cross-validation resultsindicate that the DBN model achieves promisingresults with the RMSEof1.996°C, MAEof1.539°C, and Rof 0.986at the national *** with multiple linear regression (MLR), back-propagation neural network (BPNN) and random forest (RF)method, the DBN modelreduces the MAE values by 1.340°C, 0.387°Cand0.222°C,*** analysis on spatialdistribution and temporal tendency of predictionerrors both validate the great potentials of DBN inTa estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Atmospheric temperature

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy systematics Costs Scalability Blockchains Security

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HDEC-TFA: An unsupervised learning approach for discovering physical scattering properties of single-polarized SAR image

TechRxiv

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TechRxiv 2020年

作者： Huang, Zhongling Datcu, Mihai Pan, Zongxu Qiu, Xiaolan Lei, Bin Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling82234 Germany

Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications. © 2020, CC BY.

关键词： Synthetic aperture radar

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General Structure Physics of an Aerial Remote Sensing Platform and Its systemic Accuracy Criterion

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Journal of Geodesy and Geoinformation Science 2019年第2期2卷 2-16页

作者： Lei YAN Zhengkang ZUO Yingcheng LI Xiuxiao YUAN Yan SONG Qingsheng XUE Shihu ZHAO Institute of Remote Sensing and Geographic Information System Beijing Key Laboratory of Spatial Information Integration and Its Application Peking University Beijing 100871 China Chinese Academy of Surveying and Mapping Beijing 100039 China China TOPRS Technology Co.Ltd. Beijing 100039 China School of Remote Sensing and Information Engineering Wuhan University Wuhan 430079 China Faculty of Information Engineering China University of Geosciences Wuhan 430079 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China Satellite Surveying and Mapping Application Center NASG Beijing 101300 China

Accuracy is a key factor in high-resolution remote sensing and photogrammetry. The factors that affect accuracy are imaging system errors and data processing errors. Due to the complexity of aerial camera errors, this paper focuses on the design of digital aerial camera systems and the means to reduce system error and data processing inefficiencies. There are many kinds of digital aerial camera systems at present;however, these systems lack a unified physical model, which ultimately leads to more complicated designs and multi-camera modes. Such a system is complex and costly, as it is easily affected by factors such as vibration and temperature. Thus, the installed accuracy can only reach the millimeter level. Here, we describe a unified physical structure for a digital aerial camera that imitates an out-of-field multi-charge-coupled device (CCD), an in-field multi-CCD, and once-imaging and twice-imaging digital camera systems. This model is referred to as the variable baseline-height ratio spatiotemporal model. The variable ratio allows the opto-mechanical spatial parameters to be linked with height accuracy, thus providing a connection to the surface elevation. The twice-imaging digital camera prototype system and the wideband limb imaging spectrometer provide a transformation prototype from the current multi-rigid once-imaging aerial camera to a single rigid structure. Thus, our research lays a theoretical foundation and prototype references for the construction and industrialization of digital aerial systems.

关键词： catadioptric optical mirror digital aerial camera normal physical model of variable baseline-height ratio spatial temporal model single rigid structure systemic accuracy

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Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

TechRxiv

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TechRxiv 2019年

作者： Huang, Zhongling Dumitru, Corneliu Octavian Pan, Zongxu Lei, Bin Datcu, Mihai The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling 82234 Germany

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. © 2019, CC BY.

关键词： Synthetic aperture radar

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A convergence proof of projected fast iterative soft-thresholding algorithm for parallel magnetic resonance imaging

arXiv

引用

arXiv 2019年

作者： Zhang, Xinlin Lu, Hengfa Guo, Di Bao, Lijun Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Neusoft Medical System Shanghai200241 China

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Classification of large-scale high-resolution SAR images with deep transfer learning

arXiv

引用

arXiv 2020年

作者： Huang, Zhongling Dumitru, Corneliu Octavian Pan, Zongxu Lei, Bin Datcu, Mihai Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling82234 Germany

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes' problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What's more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. Copyright © 2020, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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全球城市热辐射方向性制图揭示了城市气候遥感的潜在偏差

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Science Bulletin 2023年第16期68卷 1809-1818,M0004页

作者：杜惠琳占文凤刘紫涵 E.Scott Krayenhoff TC Chakraborty 赵磊姜璐董攀李龙黄帆王莎莎徐于月 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 School of Environmental Sciences University of GuelphGuelph N1G 2W1Canada Atmospheric Sciences and Global Change Division Pacific Northwest National LaboratoryRichland 99352USA Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbana 61820USA School of Geography and Ocean Science Nanjing UniversityNanjing 210023China

城市热辐射方向性限制了遥感地表温度反演精度,阻碍了对全球城市气候(如城市地表能量通量估算和城市热环境评估)的准确理解.然而,全球不同气候背景城市热辐射方向性时空特征及其造成的城市地表能量通量和热岛强度遥感估算偏差仍不清楚,... 详细信息

城市热辐射方向性限制了遥感地表温度反演精度,阻碍了对全球城市气候(如城市地表能量通量估算和城市热环境评估)的准确理解.然而,全球不同气候背景城市热辐射方向性时空特征及其造成的城市地表能量通量和热岛强度遥感估算偏差仍不清楚,在全球尺度是否存在与城市热辐射方向性密切相关且易获取的地表和气候参量尚不明晰.本研究结合多角度卫星观测地表温度和遥感云平台,分析了全球5500多个城市的热辐射方向性特征,量化了其造成的全球城市地表能量通量和热岛强度遥感估算偏差,厘清了全球尺度城市热辐射方向性与关键地表和气候参量的关系.研究发现全球城市平均热辐射方向性强度在夏季白天下午高达5.4 K.在极端情况下,当地表温度观测天顶角位于±60°时,热辐射方向性使得全球城市地表显热通量和热岛强度分别低估45.4%和43.0%.研究进一步发现全球城市热辐射方向性与地表不透水面百分比和空气温度具有良好的统计关系,这为卫星遥感城市地表温度角度纠正提供了新思路.

关键词：热岛强度城市气候地表能量通量地表温度显热通量卫星观测空气温度卫星遥感

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Randomized consensus based distributed kalman filtering over wireless sensor networks

arXiv

引用

arXiv 2018年

作者： Qin, Jiahu Wang, Jie Shi, Ling Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Automation State Key Laboratory of Fire Science Institute of Advanced Technology University of Science and Technology of China Hefei230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China

This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Besides, considering the limited computation, communication and energy resources in the wireless sensor networks, we propose an optimization problem which minimizes the average expected state estimation error based on the proposed algorithm. To solve the proposed problem efficiently, we transform it into a convex optimization problem. And a sub-optimal solution is attained. Examples and simulations are provided to illustrate the theoretical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Kalman filters

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Graph Structure Prefix Injection Transformer for Multi-Modal Entity Alignment

SSRN

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SSRN 2024年

作者： Li, Zhifei Luo, Xiangyu Zhang, Miao Zhang, Yan Xiao, Kui School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Cyber Science and Technology Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan430062 China

Multi-modal entity alignment aims to integrate equivalent entities in diverse multi-modal knowledge graphs. However, previous studies have primarily focused on multi-modal fusion methods without considering the impact of graph structure heterogeneity on entity alignment tasks due to the distinct structural features of different knowledge graphs. Moreover, the multi-modal information that enriches entity representations, including image, textual content, and numerical attributes, frequently incorporates noise due to modal discrepancies. To tackle these challenges, we introduce the Graph Structural Prefix Injection transformer (GSIEA) for multi-modal entity alignment. This novel approach minimizes structural heterogeneity’s impact and optimizes structural data utilization by integrating a structural prefix inject interaction module within the multi-head attention mechanism. Then, we adopt a convolutional enhancement module to extract local features of multi-modal information and complete feature fusion by cross-modal weights. Extensive experiments on two datasets demonstrate that GSIEA outperforms previous baseline models, showcasing an average performance improvement of 3.26% on MRR, with a maximum enhancement of 12.5%. Additionally, the average performance improvement on Hits@1 is 4.96%, with a maximum enhancement of 16.92%. Our code is available at https://***/HubuKG/GSIEA. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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