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Research on Integrated Deployment Method of Digital Earth Thematic application Model

IOP Conference Series: Earth and Environmental Science 2020年第1期502卷

作者： Zhao Danpu Tai Xianqing Ma Zhijie Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Elec-tronics Chinese Academy of Sciences Beijing 100190 China Research Center of Intelligent City and Intelligent Transportation CAS R&D Center for Internet of Things Wuxi 214135 China

Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth.

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and Control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Contrasting Trends and Drivers of Global Surface and Canopy Urban Heat Islands

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Geophysical Research Letters 2023年第15期50卷 e2023GL104661-e2023GL104661页

作者： Du, Huilin Zhan, Wenfeng Voogt, James Bechtel, Benjamin Chakraborty, T.C. Liu, Zihan Hu, Leiqiu Wang, Zhihua Li, Jiufeng Fu, Peng Liao, Weilin Luo, Ming Li, Long Wang, Shasha Huang, Fan Miao, Shiqi Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Nanjing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China Department of Geography Western University LondonON Canada Department of Geography Ruhr-University Bochum Bochum Germany Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory RichlandWA United States Department of Atmospheric Science University of Alabama in Huntsville HuntsvilleAL United States School of Sustainable Engineering and the Built Environment Arizona State University TempeAZ United States Center for Environment Energy and Economy HarrisburgPA United States Guangdong Key Laboratory for Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six-times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. © 2023. The Authors.

关键词： Surface properties

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Large-Scale Single Tree information Extraction of Oil Palm in Malaysia Based on Sub-Meter Visible Light Satellite Images

SSRN

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SSRN 2024年

作者： Zhang, Sen Li, Nan Cui, Yaoping Dong, Jinwei Yu, Le Ran, Lishan Chen, Ziyue Niu, Jiqiang Yan, Wei Kanniah, Kasturi Devi Xiao, Xiangming Zhang, Yongguang Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application International Institute for Earth System Sciences Nanjing University Nanjing210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Department of Geography The University of Hong Kong Pokfulam999077 Hong Kong State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Beijing100875 China College of Geographic Sciences Xinyang Normal University Xinyang464000 China Department of Geoinformation Faculty of Built Environment & Surveying Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Department of Microbiology and Plant Biology Center for Spatial Analysis University of Oklahoma NormanOK73019 United States College of Environment and Planning Faculty of Geoinformation and Real Estate

The distribution of specific tree species and single tree information can shift down the research scale of geography and ecology, bridging research from regional to local scales. However, efficiently and finely extracting the spatial distribution and single tree information for a tree species regionally has always been a challenge due to the constraints of data and algorithms. With sub-meter visible light satellite images (RGB bands) in 2021 to 2022, we constructed a novel method for extracting the spatial distribution and single tree information of oil palm across Malaysia, namely the Single Tree Extraction Method - Oil Palm (STEM-OP). We developed a graphic morphological algorithm based on gradient difference window detection to extract the spatial distribution of oil palms. Then, we proposed an algorithm of double ring sliding detection algorithm to identify the canopy texture features of single oil palm. Finally, an adaptive iterative erosion algorithm was employed to separate overlapping and adjacent canopies into single trees. The overall accuracy of both the spatial distribution and number of single oil palm extracted by STEM-OP exceeded 90%. Our study revealed that the total area of oil palm in Malaysia was 5.25 million hectares, with a total of 572.6 million trees and an average planting density of 109 palms per hectare. The STEM-OP addresses the computational inefficiencies of methods with training samples and limitations of satellite image bands. For the first time, our study extracts the refined spatial distribution of oil palm and obtains the planting density and single tree position on a national scale. All of these are of great value to the formulation of oil palm development policies and ecological protection measures. © 2024, The Authors. All rights reserved.

关键词： Morphology

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Seamless maps of major elements of the Moon: Results from high-resolution geostationary satellite

arXiv

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arXiv 2020年

作者： Lu, Yu Wu, Yun-Zhao Li, Cui Ma, Jin-Song Qi, Wen-Wen Tan, Wei Li, Xiao-Man Shi, Zhi-Cheng He, Hong-Yan Dai, Shu-Wu Li, Guo Liu, Feng-Jing Wang, Jing-Qiao Wang, Xiao-Yan Wang, Qi Meng, Ling-Jie Key Laboratory of Planetary Sciences Purple Mountain Observatory Chinese Academy of Sciences Nanjing210034 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Comparative Planetology Hefei230026 China Space Science Institute Macau University of Science and Technology China School of Geography and Ocean Science Nanjing University Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Institute of Spacecraft System Engineering Beijing China China Centre for Resources Satellite Data and Application Beijing China Earth Observation System and Data Center China National Space Administration Beijing China

Major elements such as Fe, Ti, Mg, Al, Ca, and Si play very important roles in understanding the origin and evolution of the Moon. Previous maps of these major elements derived from orbital data are based on mosaic images or low-resolution Gamma ray data. The hue variations and gaps among orbital boundaries in the mosaic images are not conducive to geological studies. This paper aims to produce seamless and homogenous distribution maps of major elements using the single-exposure image of the whole lunar disk obtained by China's high-resolution geostationary satellite, Gaofen-4, with a spatial resolution of ~500 m. The elemental contents of soil samples returned by Apollo and Luna missions were used as ground truth, and were correlated with the reflectance of the sampling sites extracted from Gaofen-4 data. The final distribution maps of these major oxides are generated with the statistical regression model. With these products the average contents and proportions of the major elements for maria and highlands were estimated and compared. The results showed that SiO2 and TiO2 have the highest and lowest fractions in mare and highland areas, respectively. Besides, the relative concentrations of these elements could serve as indicators of geologic processes, e.g., the obviously asymmetric distributions of Al2O3, CaO, and SiO2 around Tycho crater may suggest that Tycho crater was formed by an oblique impact from the southwest direction. Copyright © 2020, The Authors. All rights reserved.

关键词： Silica

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Flood Monitoring of Heilongjiang River Basin in China and Russia Transboundary Region based on SAR Backscattering Characteristics

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Journal of Geo-information Science 2022年第4期24卷 802-813页

作者： Zhangwenxuan Wang, Juanle College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing100083 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China

In recent years, Heilongjiang (Amur) River, a border river between China and Russia, is frequently flooded, which brings huge population casualties and economic losses to China and Russia in the basin. Strengthening flood monitoring in the basin is an urgent need for both countries. Influenced by terrible cloudy and rainy weather during the flooding season, the function of traditional optical remote sensing images are restricted. Thus, it is difficult to obtain cloud-free images during the flood. In this paper, through making full use of the advantages of all-weather radar data, a method of monitoring flood in large area based on Sentinel-1 synthetic aperture radar data is proposed. The probability density distribution of backscattering coefficient of SAR image is fitted by Gamma distribution and Gaussian distribution. The global threshold is obtained automatically to segment the initial water by iterating a posteriori probability difference. The misclassification type that looks like water in the initial water classification is refined and removed based on auxiliary data. Moreover, the uniformity of the extracted flood is improved by the post-processing of morphological operation. It can be concluded from the results that, firstly, compared with the traditional segmentation algorithm, the method proposed in this paper carries out the piecewise fitting of the probability density function based on the distribution law of the backscattering coefficient of the SAR image. It also divides the global statistics into local relations, which significantly improves the poor performance, caused by the large difference between water and non-water pixels, of the conventional segmentation algorithm. The flood distribution year by year from 2017 to 2020 is obtained. The overall accuracy of the results is between 87.78% and 94.89% while the Kappa coefficient is between 0.76 and 0.89. Secondly, especially for large semi-arid areas, this paper uses the relationships among backscatter

关键词： Synthetic aperture radar

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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The influence on characteristics parameters for white-LED with current

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Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 2013年第SUPPL.2期28卷 234-238页

作者： Fang, Jinglu Niu, Pingjuan Tian, Huijuan School of Electronics and Information Engineering Tianjin Polytechnic University Tianjin 300387 China Key Laboratory of Advanced Electrical Engineering and Energy Technology Tianjin Polytechnic University Tianjin 300387 China Engineering Research Center of High Power Solid State Lighting Application System Ministry Education Tianjin 300387 China

Light-emitting diodes (LEDs) are a kind of Electroluminescent Devices, characteristics parameters have a close relationship with the current. This paper mainly talks about in the two kinds of current power supply, continuous current supply and PWM pulse current supply, the effects of current on characteristics parameters of white LEDs, such as luminous flux, spectra, domain wavelength, peak wavelength, correlation color temperature and color coordinate. Through experimental test and data analysis, some conclusions was obtained that for continuous current supply, the injection current has the greatest impact on LED the luminous flux and correlation color temperature, almost linear relationship, and has a little effects on spectra, domain wavelength, color coordinate and peak wavelength. For PWM pulsed current supply, the change trend of all characteristic parameters are similar to continuous current supply, but better and more stable than continuous current supply. The results can guide the choice of current power supply mode to use and help towards the application of LED in domestic light.

关键词： Pulse width modulation

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Spatial distribution of rare earth elements and their impact factors in an area with a high abundance of regolith-hosted deposits

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Chemosphere 2024年 352卷 141374页

作者： Lin, Zhuoling Zhang, Yaduo Liang, Xiaoliang Huang, Guangqing Fan, Fenglei Yin, Xiaoling Chen, Zhihao Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou 510640 China Guangdong Public Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 China University of Chinese Academy of Sciences Beijing 100049 China School of Geography South China Normal University Guangzhou 510631 China Guangdong Provincial Key Laboratory of Mineral Physics and Material Research & Development Guangzhou 510640 China College of Natural Resources and Environment South China Agricultural University Guangzhou 510642 China

Despite the widespread occurrence of regolith-hosted rare earth elements (REEs) across South China, their spatial distribution characteristics in soils and their impact factors remain largely uncertain. This knowledge gap impedes the exploration of regolith-hosted REE deposits and the assessment of the environmental risks associated with REEs. To address this issue, 180 soil samples were collected from Meizhou City, Guangdong Province, a region known for its high abundance of regolith-hosted REEs. Subsequently, the correlations between REE enrichment/fractionation and various factors, i.e., topography, climate conditions, land use, and landform were analysed using the geo-detector method. The results revealed a highly uneven spatial distribution of REEs and their fractionation features with some regions displaying distinct spatial patterns. Elevation was the dominant factor influencing this distribution, and showed strong correlations with the concentrations of REEs, light REEs (LREEs) and heavy REEs (HREEs);the LREE/HREE ratio;and the positive Ce anomaly (δCe). The negative Eu anomaly (δEu) showed a good correlation with rock type. The enrichment and fractionation of REEs indicated a coupling among the abovementioned factors. For REE enrichment, areas with elevations of 138–148 m, precipitation levels of 1553–1574 mm, annual average land surface temperatures of 30.4–30.5 °C, leaf area index values of 22–29 and surface cutting degree of 21.5–29.9 m showed the highest average abundance within each type (scope) of the predominant factors. These findings highlight the key factors affecting REE distribution, thereby aiding the efficient utilization of regolith-hosted REE resources and the evaluation of their environmental risks. © 2024 Elsevier Ltd

关键词： Geo-detector REE enrichment REE fractionation Soil Spatial heterogeneity Topography

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Experimental investigation of abnormal transverse flow enhancement of α particles in heavy-ion collisions

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Physical Review C 2021年第4期104卷 044611-044611页

作者： Y. Huang W. Lin H. Zheng R. Wada A. Bonasera Z. Chen J. Han R. Han M. Huang K. Hagel T. Keutgen X. Liu Y. G. Ma C. W. Ma Z. Majka G. Qu L. Qin P. Ren G. Tian J. Wang Z. Yang J. B. Natowitz Key Laboratory of Radiation Physics and Technology of the Ministry of Education Sichuan University Chengdu 610064 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA School of Physics Henan Normal University Xinxiang 453007 China Laboratori Nazionali del Sud INFNvia Santa Sofia 62 95123 Catania Italy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics Information Inner Mongolia University for Nationalities Tongliao 028000 China FNRS and IPN Université Catholique de Louvain B-1348 Louvain-Neuve Belgium Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China Jagiellonian University Krakow Poland School of Science Huzhou University Huzhou 313000 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China

The mass dependence of the transverse flow for Z=1–5 fragments from the collisions of Ar40+Al27, Ar40+Ti48, and Ar40+Ni58 at 47 MeV/nucleon is investigated experimentally in this article. The transverse flow values are determined using the in-plane components of the fragment transverse momenta, where three conventional methods, i.e., the kinetic flow tensor method, the transverse momentum analysis method, and the azimuthal correlation method, are applied to reconstruct the reaction plane in an event-by-event basis. It is demonstrated from the comparison of the present experimental mass dependent flow measurements and the model simulations using an improved antisymmetrized molecular dynamics model that the experimentally observed abnormal α transverse flow enhancement is closely related to the reaction plane reconstruction procedure in the flow extraction. We further investigate the physical existence of the abnormal α flow behavior using a two-particle azimuthal correlation method, which allows us to provide the relative flow magnitude information with an identification of fragment charge number without the knowledge of the reaction plane differing from the three conventional methods. It is found that the relative flow magnitudes deduced from the two-particle azimuthal correlation functions with an identification of Z, with the correction for the recoil effect imposed by the momentum conservation, show a monotonically increasing trend as a function of fragment charge number, with no exception of the α flow enhancement. These results, in addition to those from the improved antisymmetrized molecular dynamics model simulations, definitely provide experimental evidences for the inexistence of the abnormal α flow behavior in the heavy-ion collisions at the present incident energy region in nature.

关键词： Collective flow Low & intermediate energy heavy-ion reactions Nuclear reaction analysis

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