检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Effect of celestial body gravity on Taiji mission range and range acceleration noise

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Physical Review D 2022年第10期106卷 102005-102005页

作者： Xiaoqing Han Xiaodong Peng Wenlin Tang Zhen Yang Xiaoshan Ma Chen Gao Li-e Qiang Yuzhu Zhang Mengyuan Zhao Jiafeng Zhang Binbin Liu Key Laboratory of Electronics and Information Technology for Space System National Space Science Center Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Taiji Laboratory for Gravitational Wave Universe Hangzhou 310024 China Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province Hangzhou 310024 China

The Taiji project is a space gravitational-wave detection mission consisting of three satellites that form a giant equilateral triangle with a side length of approximately 3×106 km in the heliocentric orbit. The target of the mission is to detect gravitational-wave signals in the 0.1 mHz to 1 Hz band. However, its intersatellite distances are affected by celestial bodies in the solar system, introducing changes that may reach levels of 30,000 km in magnitude. Thus, it is important to investigate whether the gravitational fields of celestial bodies in the detection frequency band will have an impact on the realization of ultrahigh-sensitivity intersatellite ranging measurements. In this study, a high-precision orbit propagator with an accuracy of 0.03 pm on the 1 AU scale is developed. Based on this orbit propagator, the impact of the major large celestial bodies in the solar system on the detection sensitivity in the intersatellite measurements are analyzed. Detailed numerical studies show that the linear spectral density of the range and range acceleration are approximately 1×10−13 m/Hz and 5×10−23 m/s2/Hz, respectively, at 0.1 mHz, indicating that the gravitational disturbance generated by the major celestial bodies in the solar system will not affect the aim of the Taiji mission to detect gravitational waves in the frequency band 0.1 mHz to 1 Hz.

关键词： Gravitational wave detection Gravitational wave detectors

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Aerosol characteristics and impacts on weather and climate over the Tibetan Plateau

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National science Review 2020年第3期7卷 492-495页

作者： Chuanfeng Zhao Yikun Yang Hao Fan Jianping Huang Yunfei Fu Xiaoye Zhang Shichang Kang Zhiyuan Cong Husi Letu Massimo Menenti State Key Laboratory of Earth Surface Processes and Resource Ecology and College of Global Change and Earth System Science Beijing Normal University Collaborative Innovation Center for Western Ecological Safety College of Atmospheric Sciences Lanzhou University School of Earth and Space Sciences University of Science and Technology of China Institute of Atmospheric Composition Chinese Academy of Meteorological Sciences State Key Laboratory of Cryospheric Sciences Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

With the most massive snow and ice globally between 60°S and 60°N, the Tibetan Plateau (TP), also called the third pole of the world, plays significant roles in the weather, climate and lives in South Asia and East Asia and even the world [1–3].Investigating the variation of weather and the climate system over the TP has long been carried out, which is signif-

关键词： Aerosol characteristics and impacts on weather and climate over the Tibetan Plateau

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Modified ddpg algorithm applied to simulation supervised learning 48

Modified ddpg algorithm applied to simulation supervised lea...

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48th International Conference on Computers and Industrial Engineering, CIE 2018

作者： Jia, Zhengxuan Xiao, Yingying Zhao, Zhilong Lin, Tingyu State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

With rapid development of machine learning, supervised learning and reinforcement learning have witnessed a considerable success. However, some problems like the absence of supervising signal in supervised learning are not yet solved. In this paper, a novel approach has been proposed based on the deep deterministic policy gradient algorithm to address such kind of problems, forming the training of an agent supervised by the simulation environment. As verification, our proposed approach is applied to a modified tower defense game, and the experimental results confirmed the effectiveness of our proposed approach. © 2018, Curran Associates Inc. All rights reserved.

关键词： Supervised learning

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A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on space Payload

A Comparative Evaluation of AutoEncoder-Based Unsupervised A...

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International Conference on Dependable systems and Their Applications (DSA)

作者： Junrong Du Lei Song Taisheng Zheng Lili Guo Chao Ma Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Science Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Beijing key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

The anomaly detection technology is the basis for ensuring the safe and stable operation of the on-rail payload. The traditional threshold-based anomaly detection method has low accuracy and poor flexibility, and cannot detect abnormalities in real time. In addition, due to the lack of abnormal samples, the distribution of positive and negative samples is extremely imbalanced, which increases the difficulty of abnormal detection. Therefore, this paper proposes an unsupervised learning method based on AutoEncoder and its variants, the Basic AutoEncoder, Deep AutoEncoder and Sparse AutoEncoder are used to verify the algorithm on three public datasets. And using the above three algorithms to carry out the case application on the real load dataset. The experiments show whether in the public dataset or the real data of the payload, the three methods of AutoEncoder have achieved good results, proving the AutoEncoder and its variants have a good application in anomaly detection. At the same time, it is verified that the three algorithms have different effects on different datasets, which proves that the AutoEncoder with different characteristics need to be selected in different scenarios.

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Traffic Control Task Scheduling Optimization Approach of Urban Road Intersection Based on Edge Computing

Traffic Control Task Scheduling Optimization Approach of Urb...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Xiang Fu Fei Wang Nan Ji Yilong Ren School of Transportation Science and Engineering Beihang University BUAA Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Intelligent Transport System (ITS) R & D Center Shanghai Urban Construction Design and Research Institute (Group) Co. Ltd. Shanghai China

ISBN: (纸本)9781665412971

With the development of Connected Automated Vehicles (CAV), there will be a long period of mixed driving, which causes the traffic control task at intersections becomes much more complicated. The control tasks would include not only the traffic signal timing optimization but also the computing tasks for different levels of vehicles. Current control task management methods are proved to be inferior to solve this problem. In this paper, we propose an intersection control task scheduling optimization approach based on edge computing. Firstly, the traffic control edge computing system for intersections is constructed. Then, different types of control tasks are listed. Criticality and timeliness are introduced to calculate the control task's dynamic priority. The control task scheduling optimization model, which considering these two parameters is proposed. At last, the improved Hungarian algorithm is applied to solve the proposed task scheduling optimization model. simulation results show that our proposed methods can effectively reduce task processing costs and ensure the critical tasks execute firstly.

关键词： Processor scheduling Computational modeling Heuristic algorithms Traffic control Scheduling Timing Task analysis

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

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arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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Giant electro-optic effect in chiral topological semimetal RhSi

arXiv

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arXiv 2021年

作者： Li, Zhi Gao, Yuewen Gu, Yu Zhang, Shengli Iitaka, Toshiaki Liu, W.M. MIIT key Laboratory of Advanced Display Materials and Devices Ministry of Industry and Information Technology Institute of optoelectronics & Nanomaterials Nanjing University of Science and Technology Nanjing210094 China Discrete Event Simulation Research Team RIKEN Center for Computational Science 2-1 Hirosawa Saitama Wako351-0198 Japan Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

We studied the linear electro-optic effect of chiral topological semimetal RhSi which is characterized by high-fold chiral fermions separated in energy space. We identify that the general second order conductivity (formula presented) includes a real symmetric component and an imaginary antisymmetric component, which are from the inter-band shift and intra-band injection current with frequency ω, respectively. The (formula presented) is significantly enhanced by the high electron velocity and nontrivial band topology of chiral fermion, and modifies the phase velocity of light wave. We also predict that the electro-optic coefficient (formula presented) of chiral crystal RhSi is about 7000 pm/V at photon energy 0.01 eV and 1.1 eV, which is about 200 times that of widely used LiNbO3 crystal. The giant electro-optic coefficient renders a relatively low half-wave voltage in order of hundreds volt, and demonstrates potential application as electro-optic crystals for the wavelength in the second telecom window of optical fiber communications. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical fibers

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Prediction of Direct Normal Irradiance Based on Ensemble Deep Learning Models

Prediction of Direct Normal Irradiance Based on Ensemble Dee...

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International Conference on Electronics technology (ICET)

作者： Chengying Zhang Zhen Yan Chao Ma Xiaoli Xu Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781728162836

ISBN: (纸本)9781728162843

Due to the intermittency and instability of solar power plant output, the key components are often in danger of being scrapped. Therefore, it is necessary to accurately predict the output of solar power plants. At present, the solar radiation intensity is considered to be one of the most important factors affecting the output of solar power plants. In essentially, the solar radiation intensity is a general concept, which is composed of several parameters. Among these parameters, direct normal Irradiance (DNI) prediction has become a hot and difficult research topic. In recent years, with the continuous improvement of hardware performance, a deep learning model has become one of the best methods to solve most time series prediction problems. In this paper, an ensemble deep learning model is proposed. This model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The former is responsible for extracting spatial features from data, while the latter is responsible for extracting temporal features. As a case study, the proposed method will predict the DNI of four typical days (spring equinox, summer solstice, autumn equinox and winter solstice) and its seven days before and after which is based on the meteorological data of Zhangbei. The results show that the deep learning model can be effectively applied to DNI prediction. However, the method proposed in this paper has a higher prediction accuracy compared with other models.

关键词： direct normal irradiance prediction deep learning convolutional neural network long-short term memory ensemble learning framework Solar power plants DNI Machine Learning winter solstice Power Plants Equinox irradiance Continuous improvement Radiation flux density intermittency meteorological data Output

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The physics-based deterministic scenarios for earthquake hazards and losses of the Zhujiangkou fault in southern China

npj Natural Hazards

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npj Natural Hazards 2025年第1期2卷 1-13页

作者： Yilong Li Ming Wang Kai Liu Houyun Yu Zhenguo Zhang Wei Zhang Xiaofei Chen Xiuwei Ye Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance Beijing Normal University Zhuhai China School of National Safety and Emergency Management Beijing Normal University Zhuhai China Department of Earth and Space Sciences Southern University of Science and Technology Shenzhen China School of National Safety and Emergency Management Beijing Normal University Beijing China Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology Southern University of Science and Technology Shenzhen China China Earthquake Administration Key Laboratory of Earthquake Monitoring and Disaster Mitigation Technology Guangdong Earthquake Agency Guangzhou China Guangdong Science and Technology Collaborative Innovation Center for Earthquake Prevention and Disaster Mitigation Guangzhou China Key Laboratory of Disaster Forecast and Control in Engineering of the Ministry of Education Urban Earthquake Safety Laboratory Guangzhou China

Dynamic rupture directivity significantly impacts earthquake hazard and loss assessments; yet, it is often overlooked, leading to uncertainties. We developed high-resolution, broadband, deterministic scenarios for earthquake hazards and losses of the Zhujiangkou Fault (ZF) in the Guangdong-Hong Kong-Macao Greater Bay Area of southern China. The calculations cover the entire physical process from fault dynamic rupture through strong ground motion propagation to quantitative loss estimations. We fully incorporate the real 3D physical fields, including fault structure, regional stress, friction conditions, inhomogeneous velocity, and topography. Results revealed higher earthquake hazards and losses triggered by the northwestern ZF segment, affecting Hong Kong, Shenzhen, Dongguan, Macao, and Guangzhou. The rupture directivity effect, driven by seismic wave interferences in forward propagation, leads to stark differences in hazards and losses across urban agglomerations, even under similar fault conditions. These findings help enhance the precision of earthquake risk assessments and provide practical approaches for disaster prevention.

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