检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Jun Hui, Dafeng Liu, Zhanfeng Yang, Long Wang, Faming Lu, Hongfang Ren, Hai CAS Engineering Laboratory for Vegetation Ecosystem Restoration on Islands and Coastal Zones Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical Garden Chinese Academy of Sciences Guangzhou510650 China Department of Biological Sciences Tennessee State University NashvilleTN37209 United States Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong Provincial Key laboratory of Applied Botany China

Understanding the effects of nutrient availability on decomposition of both aboveground and belowground plant materials is needed to predict carbon and nutrient cycling in terrestrial ecosystems. In this study, we evaluated the influence of nitrogen (N) and phosphorus (P) additions on the decomposition of leaf and fine root (including higher-order and lower-order roots) litter of two dominant species ( Schima superba and Michelia macclurei ) in a subtropical evergreen broadleaved forest over two years. We found that neither N nor P addition affected the leaf litter decomposition rate of either species. However, N addition inhibited decomposition of S. superba lower-order roots, and P addition accelerated decomposition of M. macclurei higher-order roots. Lower-order roots decomposed more slowly than leaves or higher-order roots, perhaps because concentrations of acid-insoluble compounds are higher and concentrations of Ca and Mn are lower in the lower-order roots. As a consequence, lower-order roots tended to contribute more than higher-order roots or leaves to soil organic matter accumulation. P addition greatly increased P contents in leaf litter at the beginning of the experiment, and leaf litters in P addition plots immobilized P throughout the experiment. The results indicate that the effects of N and P addition on litter decomposition differ between leaves and fine roots. Analysis of only leaf litter decomposition may limit our understanding of how nutrient availability affects ecosystem biogeochemical processes. In simulating carbon and nutrient cycling in evergreen broadleaved forest, researches should ideally assess the decomposition of both aboveground and belowground plant materials. © 2022, The Authors. All rights reserved.

关键词： Tropics

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Estimation of Travel Climate Comfort Degree in the Cross-border Region between China and Russia based on GIS

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Journal of Resources and Ecology 2019年第6期10卷 657-666页

作者： ZHOU Yezhi WANG Juanle WANG Yi Elena AGrigorieva China University of Mining and Technology(Beijing) Beijing 100083China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China National Science and Technology Infrastructure Center Beijing 100862China Institute for Complex Analysis of Regional Problems Far-Eastern BranchRussian Academy of SciencesBirobidzhan 679016Russia Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China

The duration of travel climate comfort degree is an important factor that influences the length of the tourism season and the development of a tourism *** this study,we used the monthly average meteorological data for the last 10 years from 46 weather stations in Heilongjiang Province(China)and Primorsky Krai(Russia)to calculate the temperature-humidity index(THI)and wind chill index(WCI)based on ArcGIS software interpolation *** obtained the climate comfort charts of the study area with a grid size a 1 km2 grid size,and analyzed the spatial distribution of comfort for each *** results show the following:1)The THI and WCI of the cross-border region gradually decrease from south to north and from low altitude to high *** annual comfortable climate period is longer when analyzed in terms of the WCI rather\than the THI.2)The travel climate comfortable period of the study area shows significant regional difference and the length of the comfortable period in Heilongjiang Province is 4 to 5 ***,the period in Primorsky Krai decreases from south to north and the length of the comfortable period length in its southern region can reach 7 months.3)The predominant length of the climate comfortable period in the cross-border area is 5 months per year,and it covers 46.6%of the total area,while areas that have a climate comfortable period of 2 months are the most limited,covering less than 0.3%of the *** results provide a scientific basis for the utilization and development of a meteorological tourism resources and touring arrangements for tourists in the cross-border region between China and Russia.

关键词： travel climate comfortable degree temperature-humidity index wind chill index the cross-border region between China and Russia climate adaption

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Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach

arXiv

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arXiv 2017年

作者： Li, Tongwen Shen, Huanfeng Yuan, Qiangqiang Zhang, Xuechen Zhang, Liangpei School of Resource and Environmental Sciences Wuhan University Wuhan Hubei430079 China Collaborative Innovation Center for Geospatial Technology WuhanHubei430079 China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan Hubei430079 China School of Geodesy and Geomatics Wuhan University Wuhan Hubei430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei430079 China

Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi-DBN performs significantly better than the traditional neural network. The cross-validation R increases from 0.63 to 0.94, and RMSE decreases from 29.56 to 13.68 μ g / m3 . On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μ g / m3 . This study provides a new perspective for air pollution monitoring in large geographic regions. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI

arXiv

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arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng 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 Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Composition-Dependent Optical, Dielectric and D-Orbital Electron Characteristic of High-Throughput Horizontal Composition Gradient Li1-Xmgxti2o4 Combinatorial Film

SSRN

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SSRN 2024年

作者： Wang, Yueming Li, Yongfu Wei, Mingyang Jin, Kui Li, Qian Zhang, Ding Zhou, Xiang'an Han, Yating Lian, Jie School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao266237 China Center for Optics Research and Engineering Key Laboratory of Laser & Infrared System Ministry of Education Shandong University Qingdao266237 China National Lab for Superconductivity Institute of Physics Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China

The unique physical properties of LiTi2O4 and MgTi2O4 have attracted extensive research interest in various fields, including new type batteries and high-temperature superconductors. However, the intricate mechanism governing the d-orbital electron within this system still awaits further excavation. Herein, we employed the combinatorial laser molecular beam epitaxy technique to fabricate a high-quality composition-spread Li1-xMgxTi2O4 film and evaluated its structure, composition, surface morphology and phonon modes by X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, and Raman scattering spectroscopy. The optical constants and dielectric properties of diverse microregions with varying Li/Mg content over a wide spectral range (189-1658 nm) were measured nondestructively using the ellipsometer, revealing the composition-dependent optical properties of Li1-xMgxTi2O4. By fitting the second derivative spectra (d2Ε/dE2) using the standard critical point model, five transitions within the detected spectral range were observed. Combining previous analyses and first-principles calculation, we attribute the critical points to crystal field (CF) transition 4A2 →4T1 originating from the spin state of tetrahedral Li/Mg ions, the binding of Ti-related pairs and electron transitions between O-2p to Ti-3d orbits. This exploration of the precise structure of the d-metal band provides a positive reference for the regulation and application of relevant devices. © 2024, The Authors. All rights reserved.

关键词： Lithium compounds

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Title: Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

arXiv

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arXiv 2018年

作者： Wang, Yuan Yuan, Qiangqiang Li, Tongwen Shen, Huanfeng Zheng, Li Zhang, Liangpei School of Geodesy and Geomatics Wuhan University Wuhan Hubei430079 China School of Resource and Environmental Sciences Wuhan University Wuhan Hubei430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei430079 China The Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan Hubei430079 China Key Laboratory of Geospace Environment and Geodesy Ministry of Education Wuhan University Wuhan Hubei430079 China Collaborative Innovation Center for Geospatial Technology WuhanHubei430079 China

In this study, we evaluated the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products and compared them with the Collection 6 (C6) products over regions in China with multifarious underlying surfaces during 2001–2016. The AOD retrievals were validated against 20 AERONET (V3) sites, and the results show that the correlation coefficient (R) for dark target (DT) retrievals in C6.1 is 0.946, while the fraction within the expected error (EE) can be considered relatively low at only 54.03%. Deep blue (DB) retrievals in C6.1 have a slightly lower R value (0.931), but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the low elevation vegetation (LEV) areas. For the high elevation vegetation (HEV) areas, DB performs synthetically better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the different land cover types. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DB tends to be high in areas where the main land cover type is bare soil and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals i

关键词： Supercomputers

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Multispectral Pansharpening Based on High-Pass Modulation Injection Model with Difference Factor

Multispectral Pansharpening Based on High-Pass Modulation In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yiyang Su Hongyu Yao Peng Wang Xiwang Zhang Jue Wang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application Ministry of Natural Resources Zhangzhou Institute of Surveying and Mapping Zhangzhou China Fujian Provincial Key Lab of Coastal Basin Environment Fujian Polytechnic Normal University Fuqing China Donghai Laboratory Zhoushan China Anhui Province Key Laboratory of Physical Geographic Environment Chuzhou University Chuzhou China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions Ministry of Education Henan University Kaifeng China Rady School of Management University of California San Diego California USA

This paper proposes a multispectral pansharpening based on high-pass modulation injection model with difference factor (HPM-DF), which is dedicated to solving the problem of difference between multispectral image and panchromatic image acquired at different moments. In the high-pass modulation injection model, we introduce a difference factor and use the alternating direction method of multipliers (ADMM) to fully analyze the difference variability to derive the final fusion product. Experiments assessed at both reduced and full resolution show that the proposed method can acquire better performance than the traditional pansharpening methods.

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Poissonian Hyperspectral Image Denoising without Using Anscombe Transform

Poissonian Hyperspectral Image Denoising without Using Ansco...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yulan Wang Peng Wang Xiwang Zhang Jue Wang Matthieu Muller College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing Institute of Surveying and Mapping Beijing China Key Laboratory of Land Satellite Remote Sensing Application Ministry of Natural Resources Jiangsu Province Surveying and Mapping Engineering Institute Nanjing China Xi’an Key Laboratory of Territorial Spatial Information Chang'an University Xi’an China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions Ministry of Education Henan University Kaifeng China Rady School of Management University of California San Diego California USA CNRS Grenoble INP GIPSA-lab University Grenoble Alpes Grenoble France

In the most existing Hyperspectral Image (HSI) denoising methods, Poisson noise is first transformed into Gaussian noise through Anscombe transform and then remove it. However, transform errors may occur that affect the final denoising results when using Anscombe transform. In this paper, we propose a Poissonian hyperspectral image denoising method without using Anscombe transform (WUAT) to directly remove the noise of Poissonian HSI under the maximum a posteriori (MAP) model by finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). The experimental results show that the proposed method can acquire better performance than most state-of-the-art denoising methods.

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Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

arXiv

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arXiv 2018年

作者： Li, Zhiwei Shen, Huanfeng Cheng, Qing Liu, Yuhao You, Shucheng He, Zongyi School of Resource and Environmental Sciences Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan China School of Urban Design Wuhan University Wuhan China Department of Remote Sensing China Land Surveying and Planning Institute Beijing China

Cloud detection is an important preprocessing step for the precise application of optical satellite imagery. In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors. In the network architecture of MSCFF, the symmetric encoder-decoder module, which provides both local and global context by densifying feature maps with trainable convolutional filter banks, is utilized to extract multi-scale and high-level spatial features. The feature maps of multiple scales are then up-sampled and concatenated, and a novel multi-scale feature fusion module is designed to fuse the features of different scales for the output. The two output feature maps of the network are cloud and cloud shadow maps, which are in turn fed to binary classifiers outside the model to obtain the final cloud and cloud shadow mask. The MSCFF method was validated on hundreds of globally distributed optical satellite images, with spatial resolutions ranging from 0.5 to 50 m, including Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and collected high-resolution images exported from Google Earth. The experimental results show that MSCFF achieves a higher accuracy than the traditional rule-based cloud detection methods and the state-of-the-art deep learning models, especially in bright surface covered areas. The effectiveness of MSCFF means that it has great promise for the practical application of cloud detection for multiple types of medium and high-resolution remote sensing images. Our established global high-resolution cloud detection validation dataset has been made available online (http://***/en/mscff/). Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Design of a Capsule Robot system for Gastric Hemorrhage Detection using Luminol

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yuchen Mao Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application Complicated Systems and Intelligent Robot Laboratory School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system.

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