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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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Secure and Privacy-preserving Data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Comparative analysis of the M-out-of-N structure in EN50129:2018 and IEC61508:2010

Comparative analysis of the M-out-of-N structure in EN50129:...

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2020 International Conference on Mechatronics technology and Intelligent Manufacturing, ICMTIM 2020

作者： Wang, Yiwei Wang, Yixuan Ma, Lianchuan Wen, Jiakun Zhang, Fan School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Mathematics and Physics University of Science&Technology Beijing Beijing100083 China National Engineering Research Center for Rail Transit Operation Control System Beijing Jiaotong University Beijing100044 China

By comparing and analysing EN50129:2018 and IEC 61508:2010, it is found that there are differences in the meaning of the M-out-of-N structure between these two standards. The reason for the differences lays in that the M-out-of-N structure definition of EN50129:2018 is based on the required functions, while the M-out-of-N structure definition of IEC 61508:2010 is based on the dangerous failures. The 2 out of 2 structure defined by EN50129:2018 is equivalent to the 1 out of 2 structure defined by IEC 61508:2010. The 2 out of 2 structure defined by IEC 61508:2010 is equivalent to the parallel redundancy or dual hot standby structure. Further, it is found that the formula given by EN50129:2018 is not as conservative as that given by IEC61508:2010. © 2020 Institute of Physics Publishing. All rights reserved.

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Typhoon-Induced Stormwater Drives Nutrient Dynamics and Triggers Phytoplankton Blooms in Laizhou Bay, China

SSRN

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SSRN 2023年

作者： Li, Hongguan Li, Shanshan Zhang, Mingzheng Li, Xiuren Xu, Zehao Ma, Haoyang Liang, Shengkang Song, Dehai Li, Junlong Ma, Yuanqing Qin, Huawei Ji, Diansheng Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education Ocean University of China Qingdao266100 China College of Chemistry and Chemical Engineering Ocean University of China Qingdao266100 China College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao266100 China Laboratory for Marine Ecology and Environmental Science Qingdao National Laboratory for Marine Science and Technology Qingdao266237 China Key Laboratory of Physical Oceanography Ocean University of China Ministry of Education Qingdao National Laboratory for Marine Science and Technology Qingdao China State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring China National Environmental Monitoring Center Beijing100012 China Shandong Marine Resources and Environment Research Institute Yantai264000 China National Marine Environmental Monitoring Center China

The heavy rainfall brought by typhoons result in pulsed inputs of freshwater and nutrients, causing drastic changes in the offshore hydrographic and biogeochemical conditions in a short time. In this study, we evaluated the heavy rainfall impact of Typhoons Rumbia and Lekima on Laizhou Bay (LZB). We found that torrential rainfall significantly modified riverine nutrient concentrations, component ratios, and fluxes. Our results demonstrated that 9% and 16% of the annual riverine total nitrogen (TN) and total phosphorus (TP) fluxes were transported to the LZB within two weeks in Rumbia, and the proportions were 17% and 35% in Lekima, respectively. Influenced by terrestrial inputs, the sea surface temperature (SST) and sea surface salinity (SSS) decreased postRumbia, while the dissolved inorganic nitrogen (DIN) increased from 0.13 ± 0.13 mg/L to 0.47 ± 0.32 mg/L. Furthermore, the SST and SSS decreased by approximately 2.5 ℃ and 2.3 psu postLekima, and the concentration of DIN increased from 0.13 ± 0.08 mg/L to 0.58 ± 0.48 mg/L, which induced subsequent diatom blooms. However, rapid luxury uptake of dissolved inorganic phosphorus (DIP) by phytoplankton resulted in the LZB remaining under P-limited conditions. Freshwater and nutrient pulse input accelerated the growth of highly adaptable and small diatoms in coastal waters, and the dominant species transitioned to Skeletonema costatum after Lekima. We suggest that excess freshwater inputs alter the hydrodynamic structure and residence time, combined with other suitable factors, resulting in a dramatic increase in Noctiluca scintillans and the formation of harmful algal blooms (HABs) after a decline in diatom blooms. © 2023, The Authors. All rights reserved.

关键词： Nutrients

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Evaluation and Uncertainty Analysis of Himawari-8 Hourly Aerosol Product Version 3.1 and its Influence on Surface Solar Radiation Before and During the COVID-19 Outbreak

SSRN

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SSRN 2023年

作者： Tang, Chenqian Shia, Chong Husi, Letu Ma, Run Yoshida, Mayumi Kikuchi, Maki Xu, Jian Li, Nan Zhao, Mengjie Chen, Liangfu Shi, Guangyu State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China Nanjing University of Information Science & Technology Nanjing210044 China Remote Sensing Technology Center of Japan Tsukuba305-8505 Japan Japan Aerospace Exploration Agency Tsukuba305-8505 Japan National Space Science Center Chinese Academy of Sciences Beijing100190 China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing210044 China Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100049 China

The hourly Himawari-8 version 3.1 (V31) aerosol product has been released and incorporates an updated Level 2 algorithm that uses forecast data as an a priori estimate. However, there has not been a thorough evaluation of V31 data across a full-disk scan, and V31 has yet to be applied in the analysis of its influence on surface solar radiation (SSR). This study firstly investigates the accuracy of V31 aerosol products, which includes three categories of aerosol optical depth (AOD) (AODMean, AODPure, and AODMerged) as well as the corresponding Ångström exponent (AE), using ground-based measurements from the AERONET and SKYNET. Results indicate that V31 AOD products are more consistent with ground-based measurements compared to previous products (V30). The highest correlation and lowest error were seen in the AODMerged, with a correlation coefficient of 0.8335 and minimal root mean square error of 0.1919. In contrast, the AEMerged shows a larger discrepancy with measurements unlike the AEMean and AEPure. Error analysis reveals that V31 AODMerged has generally stable accuracy across various ground types and geometrical observation angles, however, there are higher uncertainties in areas with high aerosol loading, particularly for fine aerosols. The temporal analysis shows that V31 AODMerged performs better compared to V30, particularly in the afternoon. Finally, the impacts of aerosols on SSR based on the V31 AODMerged are investigated through the development of a sophisticated SSR estimation algorithm in the clear sky. Results demonstrate that the estimated SSR is significant consistency with those of well-known CERES products, with preservation of 20 times higher spatial resolution. The spatial analysis reveals a significant reduction of AOD in the North China Plain before and during the COVID-19 outbreak, resulting in an average 24.57 W m–2 variation of the surface shortwave radiative forcing in clear sky daytime. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Spatial non-locality induced non-markovian EIT in a single giant atom

arXiv

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arXiv 2021年

作者： Zhu, Y.T. Wu, R.B. Xue, S. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Mercury transformation processes in nature: Critical knowledge gaps and perspectives for moving forward

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Journal of Environmental Sciences 2022年第9期34卷 152-165页

作者： Zhiyuan Gao Wang Zheng Yanbin Li Yurong Liu Mengjie Wu Shouying Li Ping Li Guangliang Liu Xuewu Fu Shuxiao Wang Feiyue Wang Yong Cai Xinbin Feng Baohua Gu Huan Zhong Yongguang Yin Centre for Earth Observation Science and Department of Environment and GeographyUniversity of ManitobaWinnipegMB R3T 2N2Canada Institute of Surface-Earth System Science Tianjin UniversityTianjin 300192China Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education and College of Chemistry and Chemical EngineeringOcean University of ChinaQingdao 266100China State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan 430070China School of the Environment Nanjing UniversityState Key Laboratory of Pollution Control and Resource ReuseNanjing 210023China State Key Laboratory of Environmental Geochemistry Institute of GeochemistryChinese Academy of SciencesGuiyang 550081China Department of Chemistry and Biochemistry Florida International UniversityMiamiFL 33199USA School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua UniversityBeijing 100084China Environmental Sciences Division Oak Ridge National LaboratoryOak RidgeTennessee 37831USA Environmental and Life Sciences Program(EnLS) Trent UniversityPeterboroughOntario K9L 0G2Canada Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China

The transformation of mercury(Hg) in the environment plays a vital role in the cycling of Hg and its risk to the ecosystem and human health. Of particular importance are Hg oxidation/reduction and methylation/demethylation processes driven or mediated by the dynamics of light, microorganisms, and organic carbon, among others. Advances in understanding those Hg transformation processes determine our capacity of projecting and mitigating Hg risk. Here, we provide a critical analysis of major knowledge gaps in our understanding of Hg transformation in nature, with perspectives on approaches moving forward. Our analysis focuses on Hg transformation processes in the environment, as well as emerging methodology in exploring these processes. Future avenues for improving the understanding of Hg transformation processes to protect ecosystem and human health are also explored.

关键词： Mercury oxidation reduction methylation demethylation isotope fractionation

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Model-Free Sliding Mode Iterative Learning control for Cz Silicon Single Crystal Diameter

Model-Free Sliding Mode Iterative Learning Control for Cz Si...

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第三十九届中国控制会议

作者： Yin Wan Ding Liu Junchao Ren Congcong Liu Jie Sun National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Aiming at the complex nonlinear dynamic time-varying characteristics for Czochralski（Cz） silicon single crystal growth process and the difficulty in modeling and controlling the crystal diameter by conventional mechanisms, based on the idea of data-driven modeling and control, this paper proposes an improved model-free sliding mode iterative learning control（MFA-SMILC） method. First, a data-driven model of crystal diameter is established using an extreme learning machine（ELM）with actual process data;Then, based on the compact-format dynamic linear（CFDL） data model, a discrete sliding mode control algorithm is used to design a data-driven controller structure for crystal diameters, and the stability of the iterative tracking error is verified by the stability analysis;Finally, the proposed MFA-SMILC controller is applied to silicon single crystal diameter control, and compared with the conventional model-free adaptive iterative learning control（MFA-ILC）, it is found that MFASMILC has faster response speed and convergence speed, which verifies the effectiveness of the proposed control method.

关键词： Cz silicon single crystal growth Crystal diameter Model free iterative learning control Sliding mode control

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