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4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Li, Tan Song, Wei-Ning Zhou, Jun-Hua East China University of Technology School of Software Nanchang China Beijing Institute of Electronic System Engineering State Key Laboratory of Intelligent Manufacturing System Technology Beijing China

ISBN: (纸本)9781728176734

With the development of industry, enterprises have put forward higher requirements for real-time data collection and analysis. According to this, a distributed industrial big data platform for Industrial Internet applications is designed, which can effectively support the industrial field big data collection and storage. The system contains a distributed database, real-time database and offline big data platform to implement the collection and storage of all data in the system. Thus, the industrial big data platform architecture and related data hierarchical processing flow are proposed, which can effectively meet the requirements of the multi-source data and real-time processing of smart factory. The proposed data platform technical architecture will have important reference value for the realization of smart manufacturing and smart factory. © 2021 IEEE.

关键词： Big data

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Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications

arXiv

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arXiv 2024年

作者： Liu, Jiayue Mao, Tianqi He, Dongxuan Yang, Yang Gao, Zhen Zheng, Dezhi Zhang, Jun State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

The escalating interests on underwater exploration/ reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. simulation results validate the superiority of the proposed DRL-based beam alignment scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Ray tracing

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The influence of urban three-dimensional structure and building greenhouse effect on local radiation flux

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Science China Earth Sciences 2021年第11期64卷 1934-1948页

作者： Renhua ZHANG Wenhui KUANG Shiqi YANG Zhaoliang LI Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081China

Accurate measurements of the three-dimensional structure characteristics of urban buildings and their greenhouse effect are important for evaluating the impact of urbanization on the radiation energy budget and research on the urban heat island(UHI)*** decrease in evapotranspiration or the increase in sensible heat caused by urbanization is considered to be the main cause of the UHI effect,but little is known about the influence of the main factor“net radiant flux”of the urban surface heat *** this study,experimental observation and quantitative model simulation were used to find that with the increase of building surface area after urbanization,the direct solar radiation flux and net radiation flux on building surface areas changed *** order to accurately quantify the relationship between the positive and negative effects,this study puts forward the equivalent calculation principle of“aggregation element”,which is composed of a building’s sunny face and its shadow face,and the algorithm of the contribution of the area to thermal *** research clarifies the greenhouse effect of a building with walls of glass *** shows that when the difference between absorption rates of a concrete wall and grass is−0.21,the cooling effect is *** the case of concrete walls with glass windows,the difference between absorption rates of a building wall and grass is−0.11,which is also a cooling *** greenhouse effect value of a building with glass windows reduces the cooling effect value to 56%of the effect of a building with concrete *** simulation of changes in net radiant flux and flux density shows that the greenhouse effect of a 5-story building with windows yields 15.5%less cooling effect than one with concrete walls,and a 30-story building with windows reduces the cooling effect by 23.0%.The simulation results confirmed that the difference in the equivalent absorption rate of the aggregation element is the“director”of

关键词： Aggregation element algorithm Greenhouse effect of buildings with windows Net radiation flux Direct solar radiation flux Architectural shadow

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Insights from the Second Tibetan Plateau Scientific Expedition: Unveiling the westerly–monsoon synergy and hydroclimate changes

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Atmospheric and Oceanic Science Letters 2024年第5期17卷 1-2页

作者： Chaofan Li Yaoming Ma Tianjun Zhou Riyu Lu Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Land-Atmosphere Interaction and Its Climatic Effects Group State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Hydraulic&Environmental Engineering China Three Gorges UniversityYichangChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina College of Atmospheric Sciences Lanzhou UniversityLanzhouChina Qomolangma Atmospheric and Environmental Observation and Research Station TingriChina Kathmandu Center for Research and Education Chinese Academy of Sciences-Tribhuvan UniversityBeijingChina China-Pakistan Joint Research Center on Earth Sciences IslamabadPakistan

The Tibetan Plateau(TP),often referred to as the“Asian Water Tower”,holds vast reserves of glaciers,snow,and permafrost,serving as the crucial source for major rivers that support billions of people across *** TP’s unique geographical positioning fosters significant interplay between the westerly and monsoon systems,the hydroclimate changes on the TP and its interactions with these two major atmospheric circulation systems through both the thermodynamic and dynamic processes,as well as the atmospheric water cycle of the *** interactions have far-reaching impacts on the weather and climate of China,Asia,and even the global atmospheric circulation.

关键词： Tibetan Plateau holds

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Graph-to-Text Generation Combining Directed and Undirected Structural Information in Knowledge Graphs*

Graph-to-Text Generation Combining Directed and Undirected S...

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Natural Language Processing (ICNLP), International Conference on

作者： Hongda Gong Shimin Shan Hongkui Wei Software School Dalian University of Technology Dalian University of Technology DaLian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

Graph-to-text generation task is transforms knowledge graphs into natural language. In current research, pretrained language models(PLMs) have shown better performance than structured graph encoders in the generation task. Currently, PLMs serialise knowledge graphs mostly by transforming them into undirected graph structures. The advantage of an undirected graph structure is that it provides a more comprehensive representation of the information in knowledge graph, but it is difficult to capture the dependencies between entities, so the information represented may not be accurate. Therefore, We use four types of positional embedding to obtain both the directed and undirected structure of the knowledge graph, so that we can more fully represent the information in knowledge graph, and the dependencies between entities. We then add a semantic aggregation module to the Transformer layer of PLMs, which is used to obtain a more comprehensive representation of the information in knowledge graph, as well as to capture the dependencies between entities. Thus, our approach combines the advantages of both directed and undirected structural information. In addition, our new approach is more capable of capturing generic knowledge and can show better results with small samples of data.

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Modelling analysis embodies drastic transition among global potential natural vegetations in face of changing climate

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Forest Ecosystems 2024年第2期11卷 184-192页

作者： Zhengchao Ren Lei Liu Fang Yin Xiaoni Liu School of Earth Science and Resources Chang’an UniversityXi’an710054China Research Center of Ecological Construction and Environmental Conservation in Gansu Province Gansu Agricultural UniversityLanzhou730070China State Key Laboratory of Tibetan Plateau Earth System Environment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China School of Land Engineering Chang’an UniversityXi’an710054China College of Pratacultural Science Gansu Agricultural UniversityLanzhou730070China

Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention ***,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate *** this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation *** achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV *** find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and ***,we observe an unexpected transition between tundra and savanna despite their geographical *** shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each ***,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing *** spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.

关键词： Potential natural vegetation Global warming Vegetation classification Predicted model CSCS

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Obtaining Soil Moisture Data Using an L-Band Passive Microwave Radiometer Based on Unmanned Aerial Vehicles

Obtaining Soil Moisture Data Using an L-Band Passive Microwa...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yawei Xu Jinyang Du Hui Lu Jiaxin Tian Kaixun He Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Numerical Terradynamic Simulation Group W.A. Franke College of Forestry & Conservation The University of Montana Missoula United States State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing China Department of Earth System Science Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Tsinghua University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Unmanned aerial vehicles (UAVs) can offer higher spatial resolution images compared to satellites. In this experiment, an L-band microwave radiometer named PoLRa was equipped on an UAV to detect surface soil moisture and a new soil moisture retrieval algorithm was developed for it. Compared with the algorithm provided by suppliers of PoLRa, the new algorithm can significantly improve the accuracy of soil moisture and enhance spatial details. A comparison with ground-based soil moisture measurements showed that the UAV's spatial resolution reached 10 meters with an accuracy of 0.08 m 3 /m 3 . This demonstrates significant advantages for monitoring soil moisture at the farmland scale, making it applicable to precision agriculture, flash flood warnings, and drought monitoring in the future.

关键词： Accuracy Soil measurements Soil moisture Surface soil Autonomous aerial vehicles Microwave radiometry Spatial resolution

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Autonomous Navigation and Obstacle Avoidance for Mobile Robots in a Tax Environment

Autonomous Navigation and Obstacle Avoidance for Mobile Robo...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Lianyi Yu Zhengxing Wu Yang Jiang Min Tan Junzhi Yu Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Northeastern University Shenyang China Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

This paper focuses on the navigation and obstacle avoidance problems of intelligent mobile robots in a tax environment. Through combining the improved jump point search (JPS) and dynamic window approach (DWA) algorithms, a fusion path planning method is proposed for the mobile robots in tax services. Specially, a bi-directional JPS algorithm with an adaptive heuristic function is provided to decrease the search complexity and improve search flexibility in the global path planning. Meanwhile, considering the features of the tax environment, several evaluation subfunctions are particularly designed to leverage the global information and optimize the local path planning. Finally, various simulations and experiments in a Turtlebot3 robot validate the feasibility and effectiveness of the proposed method. The obtained results provide valuable guidance for the autonomous navigation research of the tax service robots.

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Methodology, progress and challenges of geoscience knowledge graph in International Big Science Program of Deep-Time Digital Earth

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Journal of Geographical Sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCAS Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application University of Chinese Academy of Sciences School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Department of Automation Tsinghua University State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Command Center of Natural Resources Comprehensive Survey School of Computer Science China University of Geosciences

Deep-time Earth research plays a pivotal role in deciphering the rates, patterns, and mechanisms of Earth's evolutionary processes throughout geological history, providing essential scientific foundations for climate prediction, natural resource exploration, and sustainable planetary stewardship. To advance Deep-time Earth research in the era of big data and artificial intelligence, the International Union of Geological Sciences initiated the “Deeptime Digital Earth International Big Science Program”(DDE) in 2019. At the core of this ambitious program lies the development of geoscience knowledge graphs, serving as a transformative knowledge infrastructure that enables the integration, sharing, mining, and analysis of heterogeneous geoscience big data. The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1) establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2) developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3) implementing a comprehensive three-tiered architecture encompassing basic, discipline-specific, and application-oriented knowledge graphs, spanning approximately 20 geoscience disciplines. Through its open knowledge framework and international collaborative network, this initiative has fostered multinational research collaborations, establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable, Accessible, Interoperable, Reusable) data practices in deep-time Earth systems research.

关键词： Deep-time Digital Earth International Big Science Program deep-time Earth geoscience knowledge graph

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Synergistic PM_(2.5)and O_(3)control to address the emerging global PM_(2.5)-O_(3)compound pollution challenges

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Eco-Environment & Health 2024年第3期3卷 325-337页

作者： Chao He Jianhua Liu Yiqi Zhou Jingwei Zhou Lu Zhang Yifei Wang Lu Liu Sha Peng College of Resources and Environment Yangtze UniversityWuhan 430100China Hubei Key Laboratory of Petroleum Geochemistry and Environment Yangtze UniversityWuhan 430100China School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Hydrology and Environmental Hydraulics Group Wageningen University and ResearchWageningen 6700 HBthe Netherlands State Key Laboratory of Freshwater Ecology and Biotechnology Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China State Key Joint Laboratory for Environmental Simulation and Pollution Control School of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Pollution Control and Resource Reuse School of EnvironmentNanjing UniversityNanjing 210023China Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry Hubei University of EconomicsWuhan 430205China

In recent years,the issue of PM_(2.5)-O_(3)compound pollution has become a significant global environmental *** study examines the spatial and temporal patterns of global PM_(2.5)-O_(3)compound pollution and exposure risks,firstly at the global and urban scale,using spatial statistical regression,exposure risk assessment,and trend analyses based on the datasets of daily PM_(2.5)and surface O_(3)concentrations monitored in 120 cities around the world from 2019 to ***,on the basis of the common emission sources,spatial heterogeneity,interacting chemical mechanisms,and synergistic exposure risk levels between PM_(2.5)and O_(3)pollution,we proposed a synergistic PM_(2.5)-O_(3)control framework for the joint control of PM_(2.5)and *** results indicated that:(1)Nearly 50%of cities worldwide were affected by PM_(2.5)-O_(3)compound pollution,with China,South Korea,Japan,and India being the global hotspots for PM2.5-O3 compound pollution;(2)Cities with PM_(2.5)-O_(3)compound pollution have exposure risk levels dominated by ST t ST(Stabilization)and ST t HR(High Risk).Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries,with unequal exposure characteristics;(3)The selected cities showed significant positive spatial correlations between PM_(2.5)and O_(3)concentrations,which were consistent with the spatial distribution of the precursors NOx and VOCs;(4)During the study period,52.5%of cities worldwide achieved synergistic reductions in annual average PM_(2.5)and O_(3)*** average PM_(2.5)concentration in these cities decreased by 13.97%,while the average O_(3)concentration decreased by 19.18%.This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low–carbon transition.

关键词： PM_(2.5)-O_(3)compound pollution Population exposure risk Spatial correlation Synergistic treatment potential

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