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Artificial intelligence for geoscience:Progress,challenges,and perspectives

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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A distributed event-triggered generalized Nash equilibrium seeking algorithm

A distributed event-triggered generalized Nash equilibrium s...

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第40届中国控制会议

作者： Xin Cai Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University School of Electrical Engineering Xinjiang University

This paper addresses the problem of seeking generalized Nash equilibrium（GNE） for aggregative games with doubleintegrator agents by using a small-gain approach. In the existing continuous-time seeking algorithms, a default requirement for algorithms execution is that the interactions among agents be implemented in a continuous-time manner. This paper aims to overcome this weakness and presents an event-triggered communication scheme towards designing a distributed GNE seeking algorithm for agents to arrive at GNE of aggregative games. It is shown that the proposed scheme is free of Zeno *** results are presented to validate the proposed methods.

关键词： aggregative games coupled constraints double integrators event-triggered communication generalized Nash equilibrium

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Multi-type energy storage expansion planning: A review for high-penetration renewable energy integration

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Renewable and Sustainable Energy Reviews 2025年 219卷

作者： Zhang, Zhi Chen, Yanbo Ma, Tianyang Tian, Haoxin Liu, Jingyu Zhou, Ming Wang, Wei State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Energy storage has emerged as a pivotal enabling technology for the integration of renewable energy and the decarbonization of energy systems. The growing penetration of renewable energy sources (RES) has brought about a significant transformation in the operational characteristics of power systems, posing substantial challenges in maintaining power balance and ensuring stable operation across multiple time scales. Multi-type energy storage, with their distinct regulation characteristics, can meet the multi-time scale regulation requirements of power systems. As a result, scientific and efficient storage expansion planning (SEP) has become a critical task in promoting the energy transition. Although numerous studies have thoroughly explored the advancements of energy storage technologies, a comprehensive and systematic review of SEP is still remains underexplored. To fill this research gap, this study first delves into the operational challenges faced by high-penetration RES power systems and synthesizes current research on multifaceted energy storage applications. Subsequently, it offers a systematic review of planning methodologies for multi-type energy storage, covering traditional application scenarios such as source-side, grid-side, and load-side. This research further extends to SEP methods tailored to address the challenges of high-RES integration, including medium- and long-term energy balance, power balance considering uncertainties, real-time security support, and a detailed analysis of the corresponding models, algorithms, and storage technologies is also provided. Finally, the study explores emerging challenges related to power system decarbonization, the impacts of climate change, and the evolving energy storage market landscape. Furthermore, it proposes future research directions and development trends in SEP. © 2025 Elsevier Ltd

关键词： Energy transition

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Jseun: Joint Semantic-Instance Segmentation of Point Cloud Via Neighbor Features for Indoor Scenes

SSRN

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SSRN 2023年

作者： Chen, Hui Jiang, Jinlong Chen, Wanlou Liu, Wanquan Liu, Peter Xiaoping College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Nanovision Medical Technology Co. Ltd Shanghai200000 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Efficient and accurate segmentation of the point cloud for indoor scenes is an important task for 3D scene understanding. Due to the complexity of indoor environments. It is difficult to fully extract the details of point cloud, resulting in poor recognizability in existing models. In this paper, a novel framework called joint semantic-instance segmentation via enhancedutilization of neighbor features is proposed to achieve semantic and instance segmentation of indoor scenes. This framework mines point cloud information from a local scale, but it is different from other models in that it focuses on the full mining of point cloud neighbor information. In this regard, a new neighbor feature extraction module is developed to enrich the expression of neighborhood information and enlarge the construction of the neighborhood neighbor scope. Besides, semantic and instance segmentations take advantages of each other by constructing a mutual promotion model of semantic and instance, which can obtain more accurate semantic and instance predictions for indoor scenes. The proposed scheme is evaluated on the S3DIS dataset and compared with existing methods. Experimental results demonstrate that this method is more effective and efficient than previous approaches in joint 3D semantic and instance segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

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IEEE transactions on neural networks and learning systems 2021年第10期32卷 4278-4290页

作者： Ke Gu Hongyan Liu Zhifang Xia Junfei Qiao Weisi Lin Daniel Thalmann Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore EPFL CH Lausanne Switzerland

This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

关键词： Feature extraction Monitoring Atmospheric modeling COVID-19 Atmospheric measurements Transforms Temperature measurement

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Energy Management for Zones-Based Isolated DC Multi-microgrids 3rd

Energy Management for Zones-Based Isolated DC Multi-microgri...

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3rd EAI International Conference on Smart Grid and Internet of Things, SGIoT 2019

作者： Nawaz, Arshad Wu, Jing Long, Chengnian Lin, Yi Bing Department of Automation School of Electronic Information and Electrical Engineering Key Laboratory of System Control and Information Processing Ministry of China Shanghai Jiao Tong University Shanghai China Department of Computer Science and Information Engineering National Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9783030496098

In this paper, zones based distributed energy management for isolated multi-microgrids is proposed. Loads are categorized into different zones to form zonal multi-microgrids. Each microgrid has own energy management system which can locally manages the supply and demand of its zonal load and minimize the operational cost. Distributed Network Operator (DNO) act as central controller to facilitate the energy exchange between zonal microgrids and balancing the overall system-wide supply and demand in economic way. Demand Response Program (DRP) is also utilized for peak load shifting within the scheduling horizon. In addition to minimization of operation cost, the utilization of DR will also assure the reliability of supply. In the proposed distributed energy management, each microgrid balances its supply and demand locally and exchange surplus and deficit power with other microgrids through DNO. The performance of proposed scheme is demonstrated through case study simulation of radial multi-microgrid structure. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy management

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Data-Driven Modeling and Analysis of Dynamic Wake for Wind Farm control: A Comparison Study

Data-Driven Modeling and Analysis of Dynamic Wake for Wind F...

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2020 Chinese Automation Congress, CAC 2020

作者： Chen, Zhenyu Doekemeijer, Bart M Lin, Zhongwei Xie, Zhen Si, Zongming Liu, Jizhen Wingerden, Jan-Willem Van State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing China Delft University of Technology Netherlands New Energy Co. Ltd Jinan250099 China

ISBN: (纸本)9781728176871

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective flows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics' consistency is maintained. © 2020 IEEE.

关键词： Dynamic mode decomposition

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An edge-based model for non-Markovian sexually transmitted infections in coupled network

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International Journal of Biomathematics 2020年第2期13卷 119-153页

作者： Xiaofeng Luo Junyuan Yang Zhen Jin School of Computer and Information Technology Shanxi University Taiyuan 030006P.R.China Complex System Research Center Shanxi University Taiyuan 030006ShanxiP.R.China Shanxi Key Laboratory of Mathematical Techniques and Big DataAnalysis on Disease Control and Prevention Shanxi University Taiyuan 030006ShanxiP.R.China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan 030006 P.R.China

In this paper,allowing for general transmission and recovery times distributions,we proposed an edge-based age-structured-like compartmental model for STIs(EBACMS)in a coupled *** considered sexual transmissions between men with also heterosexual ***,we gave the general approach of proving the nonnegativity of solutions for the system coupling ordinary and partial differen-tial equations,which can be applied to all edge-based compartment *** then analyzed the epidemic threshold Ro with different distributions which couples the thresholds of the single-layer and bipartite networks in the percolation *** also studied the global stability of disease-free equilibrium with R0<1 and the final epidemic size F(the proportion of the population experiencing infection during the epidemic)with R0>*** addition,numerical simulations indicated that given a fixed exponential transmission distribution,a higher variance(with same mean)in general recovery distribution gives smaller R0 and *** analysis on Ro and F in terms of the parameters illustrated that male-to-male transmission routes have a greater impact on Rg and F than the heterosexual transmission routes for the Markovian transmission process and arbitrary recovery *** results provide a good theoretical guideline to consider the distributions of real-world STIs.

关键词： Coupled network EBCM non-Markovian process sexually transmitted disease epidemic age-structured

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An Optimization for Traffic Signal control with a Stochastic Link Flow Model

An Optimization for Traffic Signal Control with a Stochastic...

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第32届中国控制与决策会议

作者： Siyuan Liu Shu Lin School of Computer Science and Technology University of Chinese Academy of Sciences Key Laboratory of System Control and Information Processing Ministry of Education

Traffic signal control is an important part of the urban traffic control system, and it is a challenging problem to provide effective traffic signal control for a large complex traffic network. In order to improve the traffic efficiency of road network intersections and reduce the congestion of road networks, we optimize the traffic signals at intersections based on the Stochastic Link Flow Model（SLFM）. In this paper, the Stochastic Link Flow Model is briefly introduced and the green splits of traffic signal are used as the optimization variables to optimize the traffic flow density, which is described as a stochastic switching of four different link state modes within the traffic network. Lastly, we verified the results with Matlab simulations and confirmed the efficiency of our optimization results.

关键词： Optimization Traffic signals Stochastic traffic model

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QoS-Aware Mapping and Scheduling for Virtual Network Functions in Industrial 5G-TSN Network

QoS-Aware Mapping and Scheduling for Virtual Network Functio...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Yajing Zhang Qimin Xu Mingyan Li Cailian Chen Xinping Guan Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China College of Computer Science Chongqing University Chongqing P. R. China

ISBN: (纸本)9781728181059

Driven by the advantages of the ubiquitous connection of 5G and the determinacy of Time-Sensitive Networking (TSN), the integration of 5G and TSN is expected to provide flexible and deterministic communications for the industry. However, due to the diverse quality of service (QoS) demands of industrial applications, it is challenging to provide suitable QoS mapping across industrial 5G-TSN networks and offer dynamic services via heterogeneous infrastructures. To address this issue, we design a QoS-aware dynamic data injection scheme to realize the interconnection between 5G and TSN under edge-assisted 5G-TSN architecture. To break the tight coupling between applications and infrastructures, we focus on the virtual network function (VNF) mapping problems to facilitate the QoS provisioning for different applications leveraging the network function virtualization (NFV) technique. We first formulate it as a mixed integer linear programming (MILP) with time-sensitive constraints. Then we develop PVMS, a preemption-based two-stage heuristic algorithm for VNF mapping and scheduling in the 5G-TSN network. In particular, we dynamically map the VNFs of arrived applications by greedily searching the earliest available 5G-TSN resources. To further meet the low-latency requirements, we employ a preemption mechanism to provide no-wait transmission for higher priority applications at the cost of the preempted applications being postponed. Simulation results demonstrate that the proposed PVMS has better performance in terms of the acceptance ratio, average delay, and QoS guarantee.

关键词： Job shop scheduling 5G mobile communication Simulation Heuristic algorithms Quality of service Dynamic scheduling Network function virtualization

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