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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 Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang li school of Electrical and Information engineering Tianjin UniversityTianjin 300072China school of computer science(National Pilot software engineering school) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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面向融合运行的飞行员在环建模技术综述

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交通运输工程学报 2024年第4期24卷 208-227页

作者：魏麟杨济睿李秀易肖越郑远李诚龙中国民用航空飞行学院飞行技术学院四川广汉618307 中国民用航空飞行学院航空电子电气学院四川广汉618307 中国民用航空飞行学院民航监察员培训学院四川广汉618307 北京航空航天大学电子信息工程学院北京100191 中国民用航空飞行学院计算机学院四川广汉618307 中国民用航空飞行学院民航飞行技术与飞行安全重点实验室四川广汉618307

研究了面向有人机与无人机融合运行环境的飞行员模型,基于有人机舱内驾驶和无人机远程遥控驾驶2种操纵类型,介绍了飞行员建模技术的发展历程和各模型特点,通过仿真软件研究了有人机与无人机飞行员在环的响应特性,讨论了通信延迟环节对... 详细信息

研究了面向有人机与无人机融合运行环境的飞行员模型,基于有人机舱内驾驶和无人机远程遥控驾驶2种操纵类型,介绍了飞行员建模技术的发展历程和各模型特点,通过仿真软件研究了有人机与无人机飞行员在环的响应特性,讨论了通信延迟环节对于无人机驾驶回路的影响,总结了各类飞行员模型在不同任务场景下的适用性和不足之处。研究结果表明:面向融合运行环境的飞行员建模瓶颈主要体现在有人机与无人机飞行员所处的操纵回路异构、无人机飞行员缺乏临场情景意识和无人机系统指挥与控制链路(C2链路)存在不确定性延迟等方面;有人机飞行员建模总体上是根据控制理论方法描述人体结构和飞行员的操纵特性,且人工智能方法的发展使得基于智能控制理论的飞行员模型设计方法能够更好地描述飞行员的操纵特性;无人机飞行员由于处于不同的操纵回路位置,远程遥控驾驶通过C2链路实现无人机状态感知和操纵指令上行,因此,无人机飞行员模型建模更趋向于人在环路的表达,需根据应用场景设计相匹配的飞行员模型,才可更真实地反映飞行员在特定场景下的控制与决策行为特性;面向未来的融合运行环境,构建人在环路模型应重点考虑如何描述无人机远程飞行员因临场情景意识缺乏导致的决策差异,如何更好地拟合人机系统操纵特性,以及如何构建具有高效、可靠、低延迟C2链路的飞行员模型等问题。

关键词：航空控制融合运行有人机无人机飞行员模型飞行员在环指挥与控制链路

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多策略的主题集中式万维网爬虫设计

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计算机科学 2004年第7期31卷 84-86,208页

作者：王超朱炜李俊潘金贵南京大学计算机软件新技术国家重点实验室

万物网搜索引擎的建立、操作和维护需要许多的资源,而且在信息时效性和对特定用户的针对性方面还存在着不稳定性。在"主题集中式万维网爬虫"方面的研究希望通过利用主题减少对信息的爬行范围,同时提高信息的利用率。相关的一些研究者已... 详细信息

万物网搜索引擎的建立、操作和维护需要许多的资源,而且在信息时效性和对特定用户的针对性方面还存在着不稳定性。在"主题集中式万维网爬虫"方面的研究希望通过利用主题减少对信息的爬行范围,同时提高信息的利用率。相关的一些研究者已采用不同的方法进行了主题集中式爬虫的设计。本文讨论了多策略的主题集中式爬虫系统的设计,它具有低网宽消耗和容易执行的特点。实验表明:本系统可综合网页的相关性和重要性两方面的需要,并表现出良好的稳定性。同时,本系统在选择优先战略方面是可调和,有很强的灵活性。

关键词：多策略主题集中式爬虫设计相关性重要性万维网

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安全管理研究综述

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计算机科学 2004年第8期31卷 1-4页

作者：姚键叶保留蔡圣闻袁卫忠茅兵黄皓谢立软件新技术国家重点实验室

本文提出了安全管理的三大目标:集成管理、综合管理和智能管理,综述了安全管理的模型、体系结构、产品和关键技术,强调了安全管理在安全领域的重要地位,指出了需要进一步研究的问题。

关键词：安全管理信息模型安全策略智能管理集成管理综合管理网络管理

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A quantum federated learning framework for classical clients

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science china(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan li Qiaoyan Wen Sujuan Qin Fei Gao state key laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia school of computer science(National Pilot software engineering school) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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基于JavaCard的移动代理安全模型研究

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通信学报 2003年第11期24卷 27-33页

作者：王汝传孙开翠杨立扬郑晓燕南京邮电学院计算机科学与技术系中国科学院研究生院信息安全国家重点实验室北京100039

欲阻止恶意主机攻击移动代理,代理中的代码信息和数据信息必须被隐藏起来。同时,还要使它们对于执行环境来说是可访问和可执行的。现有措施大都采用软件方法(如密码技术),但并不足以解决恶意主机问题。一个完全不同的方法就是使用可信... 详细信息

欲阻止恶意主机攻击移动代理,代理中的代码信息和数据信息必须被隐藏起来。同时,还要使它们对于执行环境来说是可访问和可执行的。现有措施大都采用软件方法(如密码技术),但并不足以解决恶意主机问题。一个完全不同的方法就是使用可信赖的且可抵御攻击的JavaCard来保护移动代理。本文讨论用JavaCard保护移动代理的机理,同时给出我们所设计的一个JavaCard的安全模型。

关键词：移动代理 JavaCard 安全模型

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan li Xia Xie lizhen Cui Fei-Yue Wang the school of computer software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the school of software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information school Xi’an University of Financial and EconomicsXi’an 710100China the state key laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan university Haikou 570228China the state key laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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基于数据挖掘的异常入侵检测系统研究

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计算机科学 2004年第10期31卷 61-65页

作者：吕志军袁卫忠仲海骏黄皓曾庆凯谢立南京大学计算机软件新技术国家重点实验室

网络上不断出现新的攻击方法,要求入侵检测系统具有能检测新的未知攻击的异常检测能力。本文提出了一个基于数据挖掘的异常入侵检测系统ADESDM。ADESDM系统提出了同时从网络数据的协议特征,端口号和应用层数据中挖掘可疑行为的方法。在... 详细信息

网络上不断出现新的攻击方法,要求入侵检测系统具有能检测新的未知攻击的异常检测能力。本文提出了一个基于数据挖掘的异常入侵检测系统ADESDM。ADESDM系统提出了同时从网络数据的协议特征,端口号和应用层数据中挖掘可疑行为的方法。在挖掘过程中,不但采用了基于强规则的关联规则挖掘方法,还针对强规则挖掘方法的缺点,提出了基于弱规则的关联规则挖掘方法,来检测那些异常操作少,分布时间长等不易检测的的网络攻击。同时利用网络通信的时间、方向、端口号、主机地址等属性之间的影响,建立以各属性为节点的贝叶斯网络作为异常判别器,进一步判别关联规则挖掘中发现的可疑行为,提高了系统检测的准确率。

关键词：入侵检测系统关联规则挖掘端口号数据挖掘异常检测网络攻击攻击方法行为影响准确率

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10-Million Atoms Simulation of First-Principle Package LS3DF

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Journal of computer science & Technology 2024年第1期39卷 45-62页

作者：严昱瑾李海波赵曈汪林望石林刘涛谭光明贾伟乐孙凝晖 state key laboratory of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190 China university of Chinese Academy of sciences Beijing 101408China Computing System Optimization laboratory Huawei TechnologiesBeijing 100094China Institute of Semiconductors Chinese Academy of SciencesBeijing 100083China school of Materials science and engineering Yancheng Institute of TechnologyYancheng 224051China

The growing demand for semiconductor devices simulation poses a big challenge for large-scale electronic structure *** various methods,the linearly scaling three-dimensional fragment(LS3DF)method exhibits excellent scalability in large-scale *** on algorithmic and system-level optimizations,we propose a highly scalable and highly efficient implementation of LS3DF on a domestic heterogeneous supercomputer equipped with *** terms of algorithmic optimizations,the original all-band conjugate gradient algorithm is refined to achieve faster convergence,and mixed precision computing is adopted to increase overall *** terms of system-level optimiza-tions,the original two-layer parallel structure is replaced by a coarse-grained parallel *** strategies such as multi-stream,kernel fusion,and redundant computation removal are proposed to increase further utilization of the com-putational power provided by the heterogeneous *** a result,our optimized LS3DF can scale to a 10-million sili-con atoms system,attaining a peak performance of 34.8 PFLOPS(21.2% of the peak).All the improvements can be adapt-ed to the next-generation supercomputers for larger simulations.

关键词： single instruction multiple thread accelerator electronic structure high-performance computing linearly scaling three-dimensional fragment(LS3DF)

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